Affiliations
Nursing Administration, University of California, San Diego, San Diego, California
Given name(s)
Joshua
Family name
Lee
Degrees
MD

Prevention of Hospital‐Acquired VTE

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Optimizing prevention of hospital‐acquired venous thromboembolism (VTE): Prospective validation of a VTE risk assessment model

Pulmonary embolism (PE) and deep vein thrombosis (DVT), collectively referred to as venous thromboembolism (VTE), represent a major public health problem, affecting hundreds of thousands of Americans each year.1 The best estimates are that at least 100,000 deaths are attributable to VTE each year in the United States alone.1 VTE is primarily a problem of hospitalized and recently‐hospitalized patients.2 Although a recent meta‐analysis did not prove mortality benefit of prophylaxis in the medical population,3 PE is frequently estimated to be the most common preventable cause of hospital death.46

Pharmacologic methods to prevent VTE are safe, effective, cost‐effective, and advocated by authoritative guidelines.7 Even though the majority of medical and surgical inpatients have multiple risk factors for VTE, large prospective studies continue to demonstrate that these preventive methods are significantly underutilized, often with only 30% to 50% eligible patients receiving prophylaxis.812

The reasons for this underutilization include lack of physician familiarity or agreement with guidelines, underestimation of VTE risk, concern over risk of bleeding, and the perception that the guidelines are resource‐intensive or difficult to implement in a practical fashion.13 While many VTE risk‐assessment models are available in the literature,1418 a lack of prospectively validated models and issues regarding ease of use have further hampered widespread integration of VTE risk assessments into order sets and inpatient practice.

We sought to optimize prevention of hospital‐acquired (HA) VTE in our 350‐bed tertiary‐care academic center using a VTE prevention protocol and a multifaceted approach that could be replicated across a wide variety of medical centers.

Patients and Methods

Study Design

We developed, implemented, and refined a VTE prevention protocol and examined the impact of our efforts. We observed adult inpatients on a longitudinal basis for the prevalence of adequate VTE prophylaxis and for the incidence of HA VTE throughout a 36‐month period from calendar year 2005 through 2007, and performed a retrospective analysis for any potential adverse effects of increased VTE prophylaxis. The project adhered to the HIPAA requirements for privacy involving health‐related data from human research participants. The study was approved by the Institutional Review Board of the University of California, San Diego, which waived the requirement for individual patient informed consent.

We included all hospitalized adult patients (medical and surgical services) at our medical center in our observations and interventions, including patients of all ethnic groups, geriatric patients, prisoners, and the socially and economically disadvantaged in our population. Exclusion criteria were age under 14 years, and hospitalization on Psychiatry or Obstetrics/Gynecology services.

Development of a VTE Risk‐assessment Model and VTE Prevention Protocol

A core multidisciplinary team with hospitalists, pulmonary critical care VTE experts, pharmacists, nurses, and information specialists was formed. After gaining administrative support for standardization, we worked with medical staff leaders to gain consensus on a VTE prevention protocol for all medical and surgical areas from mid‐2005 through mid‐2006. The VTE prevention protocol included the elements of VTE risk stratification, definitions of adequate VTE prevention measures linked to the level of VTE risk, and definitions for contraindications to pharmacologic prophylactic measures. We piloted risk‐assessment model (RAM) drafts for ease of use and clarity, using rapid cycle feedback from pharmacy residents, house staff, and medical staff attending physicians. Models often cited in the literature15, 18 that include point‐based scoring of VTE risk factors (with prophylaxis choices hinging on the additive sum of scoring) were rejected based on the pilot experience.

We adopted a simple model with 3 levels of VTE risk that could be completed by the physician in seconds, and then proceeded to integrate this RAM into standardized data collection instruments and eventually (April 2006) into a computerized provider order entry (CPOE) order set (Siemmens Invision v26). Each level of VTE risk was firmly linked to a menu of acceptable prophylaxis options (Table 1). Simple text cues were used to define risk assessment, with more exhaustive listings of risk factors being relegated to accessible reference tables.

Three‐tier VTE Risk Assessment with Prevention Measures for Each Level of Risk
LowModerateHigh
  • NOTE: IPC indicated for contraindications to pharmacologic prophylaxis.

  • Abbreviations: ESRD, end‐stage renal disease; INR, international normalized ratio; IPC, intermittent pneumatic compression devices; LMWH, low‐molecular‐weight heparin; LOS, length of stay; q, dose every; SC, subcutaneously; SCI, spinal cord injury; UFH, unfractionated heparin; VTE, venous thromboembolism.

Ambulatory patient without VTE risk factors; observation patient with expected LOS 2 days; same day surgery or minor surgeryAll other patients (not in low‐risk or high‐risk category); most medical/surgical patients; respiratory insufficiency, heart failure, acute infectious, or inflammatory diseaseLower extremity arthroplasty; hip, pelvic, or severe lower extremity fractures; acute SCI with paresis; multiple major trauma; abdominal or pelvic surgery for cancer
Early ambulationUFH 5000 units SC q 8 hours; OR LMWH q day; OR UFH 5000 units SC q 12 hours (if weight < 50 kg or age > 75 years); AND suggest adding IPCLMWH (UFH if ESRD); OR fondaparinux 2.5 mg SC daily; OR warfarin, INR 2‐3; AND IPC (unless not feasible)

Intermittent pneumatic compression devices were endorsed as an adjunct in all patients in the highest risk level, and as the primary method in patients with contraindications to pharmacologic prophylaxis. Aspirin was deemed an inappropriate choice for VTE prophylaxis. Subcutaneous unfractionated or low‐molecular‐weight heparin were endorsed as the primary method of prophylaxis for the majority of patients without contraindications.

Integration of the VTE Protocol into Order Sets

An essential strategy for the success of the VTE protocol included integrating guidance for the physician into the flow of patient care, via standardized order sets. The CPOE VTE prevention order set was modular by design, as opposed to a stand alone design. After conferring with appropriate stakeholders, preexisting and nonstandardized prompts for VTE prophylaxis were removed from commonly used order sets, and the standardized module was inserted in its place. This allowed for integration of the standardized VTE prevention module into all admission and transfer order sets, essentially insuring that all patients admitted or transferred within the medical center would be exposed to the protocol. Physicians using a variety of admission and transfer order sets were prompted to select each patient's risk for VTE, and declare the presence or absence of contraindications to pharmacologic prophylaxis. Only the VTE prevention options most appropriate for the patient's VTE and anticoagulation risk profile were presented as the default choice for VTE prophylaxis. Explicit designation of VTE risk level and a prophylaxis choice were presented in a hard stop mechanism, and utilization of these orders was therefore mandatory, not optional. Proper use (such as the proper classification of VTE risk by the ordering physician) was actively monitored on an auditing basis, and order sets were modified occasionally on the basis of subjective and objective feedback.

Assessment of VTE Risk Assessment Interobserver Agreement

Data from 150 randomly selected patients from the audit pool (from late 2005 through mid‐2006) were abstracted by the nurse practitioner in a detailed manner. Five independent reviewers assessed each patient for VTE risk level, and for a determination of whether or not they were on adequate VTE prophylaxis on the day of the audit per protocol. Interobserver agreement was calculated for these parameters using kappa scores.

Prospective Monitoring of Adequate VTE Prophylaxis

A daily medical center inpatient census report of eligible patients in the medical center for >48 hours was downloaded into an Microsoft Excel spreadsheet, with each patient assigned a consecutive number. The Excel random number generator plug‐in function was used to generate a randomly sequenced list of the patients. The research nurse practitioner targeted serial patients on the list for further study, until she accomplished the requisite number of audits each day. The mean number of audits per month declined over the study years as the trends stabilized and as grant funding expired, but remained robust throughout (2005: 107 audits per month; 2006: 80 audits per month; and 2007: 57 audits per month).

The data collected on each patient randomly selected for audit included age, gender, location, service, date and time of review, and date of admission. The audit VTE RAM (identical to the VTE RAM incorporated into the order set), was used to classify each patient's VTE risk as low, moderate, or high. For each audit, we determined if the patient was on an adequate VTE prevention regimen consistent with our protocol, given their VTE risk level, demographics, and absence or presence of contraindications to pharmacologic prophylaxis. All questionable cases were reviewed by at least 2 physicians at weekly meetings with a final consensus determination. Adequacy of the VTE regimen was judged by orders entered on the day of the audit, but we also noted whether or not ordered intermittent compression devices were in place and functioning at the time of the audit.

Prospective (Concurrent) Discovery and Analysis of VTE Cases

The team nurse practitioner used the PACS radiology reporting and archival system (IMPAX version 4.5; AGFA Healthcare Informatics, Greenville, SC) to identify all new diagnoses of VTE, in the process described below.

Procedure codes for following studies were entered into the IMPAX search engine to locate all such exams performed in the previous 1 to 3 days:

  • Ultrasound exams of the neck, upper extremities, and lower extremities;

  • Computed tomography (CT) angiograms of the chest;

  • Ventilation/perfusion nuclear medicine scans; and

  • Pulmonary angiograms.

 

Negative studies and studies that revealed unchanged chronic thromboses were excluded, while clots with a chronic appearance but no evidence of prior diagnosis were included. Iliofemoral, popliteal, calf vein, subclavian, internal and external jugular vein, and axillary vein thromboses were therefore included, as were all PEs. Less common locations, such as renal vein and cavernous sinus thromboses, were excluded. The improvement/research team exerted no influence over decisions about whether or not testing was done.

Each new case of VTE was then classified as HA VTE or community‐acquired VTE. A new VTE was classified as HA if the diagnosis was first suspected and made in the hospital. A newly diagnosed VTE was also classified as HA if the VTE was suspected in the ambulatory setting, but the patient had been hospitalized within the arbitrary window of the preceding 30 days.

Each new diagnosis of HA VTE was reviewed by core members of the multidisciplinary support team. This investigation included a determination of whether the patient was on an adequate VTE prophylaxis regimen at the time of the HA VTE, using the RAM and linked prophylaxis menu described above. The VTE prevention regimen ordered at the time the inpatient developed the HA VTE was classified as adherent or nonadherent to the University of California, San Diego (UCSD) protocol: patients who developed VTE when on suboptimal prophylaxis per protocol were classified as having a potentially preventable case. Potentially iatrogenic precipitants of VTE (such as the presence of a central venous catheter or restraints) were also noted. All data were entered into a Microsoft Access database for ease of retrieval and reporting.

All tests for VTE were performed based on clinical signs and symptoms, rather than routine screening, except for the Trauma and Burn services, which also screen for VTE in high‐risk patients per their established screening protocols.

Statistical Analysis of VTE Prophylaxis and HA VTE Cases

Gender differences between cases of VTE and randomly sampled and audited inpatients were examined by chi‐square analysis, and analysis of variance (ANOVA) was used to examine any age or body mass index (BMI) differences between audits and cases.

The unadjusted risk ratio (RR) for adequate prophylaxis was compared by year, with year 2005 being the baseline (comparison) year, by chi‐square analysis.

The unadjusted RR of HA VTE was calculated by dividing the number of cases found in the calendar year by the hospital census of adult inpatients at risk. For each case, a classification for the type of VTE (PE vs. DVT vs. combinations) was recorded. Cases not receiving adequate prophylaxis were categorized as preventable DVT. Unadjusted RRs were calculated for each year by chi‐square analysis, compared to the baseline (2005) year.

All data were analyzed using Stata (version 10; Stata Corp., College Station, TX). Results for the different analysis were considered significant at P < 0.05.

Retrospective Study of Unintentional Adverse Effects

The increase in anticoagulant use accompanying the introduction of the VTE prophylaxis order set warranted an evaluation of any subsequent rise in related adverse events. A study was done to determine the rates of bleeding and heparin‐induced thrombocytopenia (HIT) before and after the implementation of the VTE prophylaxis order set.

A retrospective analysis was conducted to evaluate outcomes in our inpatients from December 2004 through November 2006, with April to November, 2006 representing the post‐order set implementation time period. Any patient with a discharge diagnosis code of e934.2 (anticoagulant‐related adverse event) was selected for study to identify possible bleeding attributable to pharmacologic VTE prophylaxis. Major or minor bleeding attributable to pharmacologic VTE prophylaxis was defined as a bleed occurring 72 hours after receiving pharmacologic VTE prophylaxis. Major bleeding was defined as cerebrovascular, gastrointestinal, retroperitoneal, or overt bleeding with a decrease in hemoglobin 2 mg/dL with clinical symptoms such as hypotension or hypoxia (not associated with hemodialysis) or transfusion of 2 units of packed red blood cells. Minor bleeding was defined as ecchymosis, epistaxis, hematoma, hematuria, hemoptysis, petechiae, or bleeding without a decrease in hemoglobin 2 g/dL.

Possible cases of HIT were identified by screening for a concomitant secondary thrombocytopenia code (287.4). Chart review was then conducted to determine a causal relationship between the use of pharmacologic VTE prophylaxis and adverse events during the hospital stay. HIT attributable to pharmacologic VTE prophylaxis was determined by assessing if patients developed any of the following clinical criteria after receiving pharmacologic VTE prophylaxis: platelet count <150 109/L or 50% decrease from baseline, with or without an associated venous or arterial thrombosis or other sequelae (skin lesions at injection site, acute systemic reaction) and/or a positive heparin‐induced platelet activation (HIPA) test. In order to receive a diagnosis of HIT, thrombocytopenia must have occurred between days 5 to 15 of heparin therapy, unless existing evidence suggested that the patient developed rapid‐onset HIT or delayed‐onset HIT. Rapid‐onset HIT was defined as an abrupt drop in platelet count upon receiving a heparin product, due to heparin exposure within the previous 100 days. Delayed‐onset HIT was defined as HIT that developed several days after discontinuation of heparin. Other evident causes of thrombocytopenia were ruled out.

Statistical Analysis of Retrospective Study of Unintentional Adverse Effects

Regression analysis with chi‐square and ANOVA were used in the analysis of the demographic data. RRs were calculated for the number of cases coded with an anticoagulant‐related adverse event secondary thrombocytopenia before and after the order set implementation.

Educational Efforts and Feedback

Members of the multidisciplinary team presented information on HA VTE and the VTE prevention protocol at Medical and Surgical grand rounds, teaching rounds, and noon conference, averaging 1 educational session per quarter. Feedback and education was provided to physicians and nursing staff when audits revealed that a patient had inadequate prophylaxis with reference to the protocol standard. In addition, these conversations provided on opportunity to explore reasons for nonadherence with the protocol, confusion regarding the VTE RAM, and other barriers to effective prophylaxis, thereby providing guidance for further protocol revision and educational efforts. We adjusted the order set based on active monitoring of order set use and the audit process.

Results

There were 30,850 adult medical/surgical inpatients admitted to the medical center with a length of stay of 48 hours or more in 2005 to 2007, representing 186,397 patient‐days of observation. A total of 2,924 of these patients were randomly sampled during the VTE prophylaxis audit process (mean 81 audits per month). Table 2 shows the characteristics of randomly sampled audit patients and of the patients diagnosed with HA VTE. The demographics of the 30,850‐inpatient population (mean age = 50 years; 60.7% male; 52% Surgical Services) mirrored the demographics of the randomly sampled inpatients that underwent audits, validating the random sampling methods.

Description of Population Audits and Hospital‐acquired Venous Thromboembolism
 Number (n = 3285)% of Study Population*Cases (n = 361) [n (%)]Audits (n = 2924) [n (%)]OR (95% CI)
  • Abbreviations: CI, confidence interval; OR, odds ratio; SD, standard deviation.

  • Cases and audits.

Age (years) mean SD51 16 (range 15‐100) 53 1750 171.01 (1.003‐1.016)
Gender, males199361213 (59)1782 (61)0.93 (0.744‐1.16)
Major service:     
Surgery171452200 (55)1516 (52) 
Medicine156648161 (45)1408 (48) 
Service, detail     
Hospitalist10413283 (23)958 (33) 
General surgery8312575 (21)756 (26) 
Trauma4191377 (22)342 (12) 
Cardiology3131045 (13)268 (9) 
Orthopedics244715 (4)229 (8) 
Burn unit205629 (8)176 (6) 
Other222730 (8)192 (7) 

The majority of inpatients sampled in the audits were in the moderate VTE risk category (84%), 12% were in the high‐risk category, and 4% were in the low‐risk category. The distribution of VTE risk did not change significantly over this time period.

Interobserver Agreement

The VTE RAM interobserver agreement was assessed on 150 patients with 5 observers as described above. The kappa score for the VTE risk level was 0.81. The kappa score for the judgment of whether the patient was on adequate prophylaxis or not was 0.90.

Impact on Percent of Patients with Adequate Prophylaxis (Longitudinal Audits)

Audits of randomly sampled inpatients occurred longitudinally throughout the study period as described above. Based on the intervention, the percent of patients on adequate prophylaxis improved significantly (P < 0.001) by each calendar year (see Table 3), from a baseline of 58% in 2005 to 78% in 2006 (unadjusted relative benefit = 1.35; 95% confidence interval [CI] = 1.28‐1.43), and 93% in 2007 (unadjusted relative benefit = 1.61; 95% CI = 1.52, 1.69). The improvement seen was more marked in the moderate VTE risk patients when compared to the high VTE risk patients. The percent of audited VTE prophylaxis improved from 53% in calendar year (CY) 2005 to 93% in 2007 (unadjusted relative benefit = 1.75; 95% CI = 1.70‐1.81) in the moderate VTE risk group, while the high VTE risk group improved from 83% to 92% in the same time period (unadjusted relative benefit = 1.11; 95% CI = 0.95‐1.25).

Unadjusted Risk Ratio (Relative Benefit) of Receiving Adequate Venous Thromboembolism Prophylaxis by Year, in Randomly Selected Inpatients
 200520062007
  • Abbreviation: CI, confidence interval.

  • P < 0.001.

All audits1279960679
Prophylaxis adequate, n (%)740 (58)751 (78)631 (93)
Relative benefit (95% CI)11.35* (1.28‐1.43)1.61* (1.52‐1.69)

Overall, adequate VTE prophylaxis was present in over 98% of audited patients in the last 6 months of 2007, and this high rate has been sustained throughout 2008. Age, ethnicity, and gender were not associated with differential rates of adequate VTE prophylaxis.

Figure 1 is a timeline of interventions and the impact on the prevalence of adequate VTE prophylaxis. The first 7 to 8 months represent the baseline rate 50% to 55% of VTE prophylaxis. In this baseline period, the improvement team was meeting, but had not yet begun meeting with the large variety of medical and surgical service leaders. Consensus‐building sessions with these leaders in the latter part of 2005 through mid‐2006 correlated with improvement in adequate VTE prophylaxis rates to near 70%. The consensus‐building sessions also prepared these varied services for a go live date of the modular order set that was incorporated into all admit and transfer order sets, often replacing preexisting orders referring to VTE prevention measures. The order set resulted in an improvement to 80% adequate prophylaxis, with the incremental improvement occurring virtually overnight with the go live date at the onset of quarter 2 (Q2) of 2006. Monitoring of the order set use confirmed that it was easy and efficient to use, but also revealed that physicians were at times classifying patients as low VTE risk inaccurately, when they possessed qualities that actually qualified them for moderate risk status by our protocol. We therefore inserted a secondary CPOE screen when patients were categorized as low VTE risk, asking the physician to deny or confirm that the patient had no risk factors that qualified them for moderate risk status. This confirmation screen essentially acted as a reminder to the physician to ask Are you sure this patient does not need VTE prophylaxis? This minor modification of the CPOE order set improved adequate VTE prophylaxis rates to 90%. Finally, we asked nurses to evaluate patients who were not on therapeutic or prophylactic doses of anticoagulants. Patients with VTE risk factors but no obvious contraindications generated a note from the nurse to the doctor, prompting the doctor to reassess VTE risk and potential contraindications. This simple intervention raised the percent of audited patients on adequate VTE prophylaxis to 98% in the last 6 months of 2007.

Figure 1
Percent of randomly sampled inpatients with adequate VTE prophylaxis; 2,924 randomly sampled adult inpatients (mean 81 patients per month) audited for adequacy of VTE prophylaxis regimen on the day of audit. Improvement is correlated with incremental interventions on the statistical process control chart. Control limits determined using a p‐chart macro in Microsoft Excel with a P value of 0.01. VTE = venous thromboembolism; Q = quarter; ID = identification.

Description of Prospectively Identified VTE

We identified 748 cases of VTE among patients admitted to the medical center over the 36‐month study period; 387 (52%) were community‐acquired VTE. There were 361 HA cases (48% of total cases) over the same time period. There was no difference in age, gender, or BMI between the community‐acquired and hospital‐related VTE.

Of the 361 HA cases, 199 (55%) occurred on Surgical Services and 162 (45%) occurred on Medical Services; 58 (16%) unique patients had pulmonary emboli, while 303 (84%) patients experienced only DVT. Remarkably, almost one‐third of the DVT occurred in the upper extremities (108 upper extremities, 240 lower extremities), and most (80%) of the upper‐extremity DVT were associated with central venous catheters.

Of 361 HA VTE cases, 292 (81%) occurred in those in the moderate VTE risk category, 69 HA VTE cases occurred in high‐risk category patients (19%), and no VTE occurred in patients in the low‐risk category.

Improvement in HA VTE

HA VTE were identified and each case analyzed on an ongoing basis over the entire 3 year study period, as described above. Table 4 depicts a comparison of HA VTE on a year‐to‐year basis and the impact of the VTE prevention protocol on the incidence of HA VTE. In 2007 (the first full CY after the implementation of the order set) there was a 39% relative risk reduction (RRR) in the risk of experiencing an HA VTE. The reduction in the risk of preventable HA VTE was even more marked (RRR = 86%; 7 preventable VTE in 2007, compared to 44 in baseline year of 2005; RR = 0.14; 95% CI = 0.06‐0.31).

HA VTE Characteristics and Positive Impact of VTE Prevention Protocol, Demonstrating Significant Risk Reduction for Cases of HA VTE, HA DVT, and Preventable VTE from 2005 to 2007
 HA VTE by Year
 200520062007
  • Abbreviations: CI, confidence interval; DVT, deep vein thrombosis; HA, hospital‐acquired; PE, pulmonary embolus; VTE, venous thromboembolism.

  • P < 0.001.

  • P < 0.01.

Patients at Risk9720992311,207
Cases with any HA VTE13113892
Risk for HA VTE1 in 761 in 731 in 122
Unadjusted relative risk (95% CI)1.01.03 (0.81‐1.31)0.61* (0.47‐0.79)
Cases with PE212215
Risk for PE1 in 4631 in 4511 in 747
Unadjusted relative risk (95% CI)1.01.03 (0.56‐1.86)0.62 (0.32‐1.20)
Cases with DVT (and no PE)11011677
Risk for DVT1 in 881 in 851 in 146
Unadjusted relative risk (95% CI)1.01.03 (0.80‐1.33)0.61* (0.45‐0.81)
Cases with preventable VTE44217
Risk for preventable VTE1 in 2211 in 4731 in 1601
Unadjusted relative risk (95% CI)1.00.47 (0.28‐0.79)0.14* (0.06‐0.31)

Retrospective Analysis of Impact on HIT and Bleeding

There were no statistically significant differences in the number of cases coded for an anticoagulant‐related bleed or secondary thrombocytopenia (Table 5). Chart review revealed there were 2 cases of minor bleeding attributable to pharmacologic VTE prophylaxis before the order set implementation. There were no cases after implementation. No cases of HIT attributable to pharmacologic VTE prophylaxis were identified in either study period, with all cases being attributed to therapeutic anticoagulation.

Pre/Post‐orderset Anticoagulation Related Adverse Events
 Pre‐order SetPost‐order SetPost‐order Set RR (CI)
  • Abbreviations: RR, relative risk; CI, 95% confidence interval; HIT, Heparin induced Thrombocytopenia

Bleeding events74280.70 (0.46‐1.08)
Due to prophylaxis2 (minor)0 
HIT events971.44 (0.54‐3.85)
Due to prophylaxis00 
Patient admissions3211717294 

Discussion

We demonstrated that implementation of a standardized VTE prevention protocol and order set could result in a dramatic and sustained increase in adequate VTE prophylaxis across an entire adult inpatient population. This achievement is more remarkable given the rigorous criteria defining adequate prophylaxis. Mechanical compression devices were not accepted as primary prophylaxis in moderate‐risk or high‐risk patients unless there was a documented contraindication to pharmacologic prophylaxis, and high VTE risk patients required both mechanical and pharmacologic prophylaxis to be considered adequately protected, for example. The relegation of mechanical prophylaxis to an ancillary role was endorsed by our direct observations, in that we were only able to verify that ordered mechanical prophylaxis was in place 60% of the time.

The passive dissemination of guidelines is ineffective in securing VTE prophylaxis.19 Improvement in VTE prophylaxis has been suboptimal when options for VTE prophylaxis are offered without providing guidance for VTE risk stratification and all options (pharmacologic, mechanical, or no prophylaxis) are presented as equally acceptable choices.20, 21 Our multifaceted strategy using multiple interventions is an approach endorsed by a recent systematic review19 and others in the literature.22, 23 The interventions we enacted included a method to prompt clinicians to assess patients for VTE risk, and then to assist in the selection of appropriate prophylaxis from standardized options. Decision support and clinical reminders have been shown to be more effective when integrated into the workflow19, 24; therefore, a key strategy of our study involved embedding the VTE risk assessment tool and guidance toward appropriate prophylactic regimens into commonly used admission/transfer order sets. We addressed the barriers of physician unfamiliarity or disagreement with guidelines10 with education and consensus‐building sessions with clinical leadership. Clinical feedback from audits, peer review, and nursing‐led interventions rounded out the layered multifaceted interventional approach.

We designed and prospectively validated a VTE RAM during the course of our improvement efforts, and to our knowledge our simple 3‐category (or 3‐level) VTE risk assessment model is the only validated model. The VTE risk assessment/prevention protocol was validated by several important parameters. First, it proved to be practical and easy to use, taking only seconds to complete, and it was readily adopted by all adult medical and surgical services. Second, the VTE RAM demonstrated excellent interobserver agreement for VTE risk level and decisions about adequacy of VTE prophylaxis with 5 physician reviewers. Third, the VTE RAM predicted risk for VTE. All patients suffering from HA VTE were in the moderate‐risk to high‐risk categories, and HA VTE occurred disproportionately in those meeting criteria for high risk. Fourth, implementation of the VTE RAM/protocol resulted in very high, sustained levels of VTE prophylaxis without any detectable safety concerns. Finally and perhaps most importantly, high rates of adherence to the VTE protocol resulted in a 40% decline in the incidence of HA VTE in our institution.

The improved prevalence of adequate VTE prophylaxis reduced, but did not eliminate, HA VTE. The reduction observed is consistent with the 40% to 50% efficacy of prophylaxis reported in the literature.7 Our experience highlights the recent controversy over proposals by the Centers for Medicare & Medicaid Services (CMS) to add HA VTE to the list of do not pay conditions later this year,25 as it is clear from our data that even near‐perfect adherence with accepted VTE prevention measures will not eliminate HA VTE. After vigorous pushback about the fairness of this measure, the HA VTE do not pay scope was narrowed to include only certain major orthopedic procedure patients.

Services with a preponderance of moderate‐risk patients had the largest reduction in HA VTE. Efforts that are focused only on high‐risk orthopedic, trauma, and critical care patients will miss the larger opportunities for maximal reduction in HA VTE for multiple reasons. First, moderate VTE risk patients are far more prevalent than high VTE risk patients (84% vs. 12% of inpatients at our institution). Second, high‐risk patients are already at a baseline relatively high rate of VTE prophylaxis compared to their moderate VTE risk counterparts (83% vs. 53% at our institution). Third, a large portion of patients at high risk for VTE (such as trauma patients) also have the largest prevalence of absolute or relative contraindications to pharmacologic prophylaxis, limiting the effect size of prevention efforts.

Major strengths of this study included ongoing rigorous concurrent measurement of both processes (percent of patients on adequate prophylaxis) and outcomes (HA VTE diagnosed via imaging studies) over a prolonged time period. The robust random sampling of inpatients insured that changes in VTE prophylaxis rates were not due to changes in the distribution of VTE risk or bias potentially introduced from convenience samples. The longitudinal monitoring of imaging study results for VTE cases is vastly superior to using administrative data that is reliant on coding. The recent University Healthsystem Consortium (UHC) benchmarking data on venous thromboembolism were sobering but instructive.26 UHC used administrative discharge codes for VTE in a secondary position to identify patients with HA VTE, which is a common strategy to follow the incidence of HA VTE. The accuracy of identifying surgical patients with an HA VTE was only 60%. Proper use of the present on admission (POA) designation would have improved this to 83%, but 17% of cases either did not occur or had history only with a labor‐intensive manual chart review. Performance was even worse in medical patients, with only a 30% accuracy rate, potentially improved to 79% if accurate POA designation had been used, and 21% of cases identified by administrative methods either did not occur or had history only. In essence, unless an improvement team uses chart review of each case potentially identified as a HA VTE case, the administrative data are not reliable. Concurrent discovery of VTE cases allows for a more accurate and timely chart review, and allows for near real‐time feedback to the responsible treatment team.

The major limitation of this study is inherent in the observational design and the lack of a control population. Other factors besides our VTE‐specific improvement efforts could affect process and outcomes, and reductions in HA VTE could conceivably occur because of changes in the make‐up of the admitted inpatient population. These limitations are mitigated to some degree by several observations. The VTE risk distribution in the randomly sampled inpatient population did not vary significantly from year to year. The number of HA VTE was reduced in 2007 even though the number of patients and patient days at risk for developing VTE went up. The incidence of community‐acquired VTE remained constant over the same time period, highlighting the consistency of our measurement techniques and the VTE risk in the community we serve. Last, the improvements in VTE prophylaxis rates increased at times that correlated well with the introduction of layered interventions, as depicted in Figure 1.

There were several limitations to the internal study on adverse effects of VTE protocol implementation. First, this was a retrospective study, so much of the data collection was dependent upon physician progress notes and discharge summaries. Lack of documentation could have precluded the appropriate diagnosis codes from being assigned. Next, the study population was generated from coding data, so subjectivity could have been introduced during the coding process. Also, a majority of the patients did not fit the study criteria due to discharge with the e934.2 code, because they were found to have an elevated international normalized ratio (INR) after being admitted on warfarin. Finally, chart‐reviewer bias could have affected the results, as the chart reviewer became more proficient at reviewing charts over time. Despite these limitations, the study methodology allowed for screening of a large population for rare events. Bleeding may be a frequent concern with primary thromboprophylaxis, but data from clinical trials and this study help to demonstrate that rates of adverse events from pharmacologic VTE prophylaxis are very rare.

Another potential limitation is raised by the question of whether our methods can be generalized to other sites. Our site is an academic medical center and we have CPOE, which is present in only a small minority of centers. Furthermore, one could question how feasible it is to get institution‐wide consensus for a VTE prevention protocol in settings with heterogenous medical staffs. To address these issues, we used a proven performance improvement framework calling for administrative support, a multidisciplinary improvement team, reliable measures, and a multifaceted approach to interventions. This framework and our experiences have been incorporated into improvement guides27, 28 that have been the centerpiece of the Society of Hospital Medicine VTE Prevention Collaborative improvement efforts in a wide variety of medical environments. The collaborative leadership has observed that success is the rule when this model is followed, in institutions large and small, academic or community, and in both paper and CPOE environments. Not all of these sites use a VTE RAM identical to ours, and there are local nuances to preferred choices of prophylaxis. However, they all incorporated simple VTE risk stratification with only a few levels of risk. Reinforcing the expectation that pharmacologic prophylaxis is indicated for the majority of inpatients is likely more important than the nuances of choices for each risk level.

We demonstrated that dramatic improvement in VTE prophylaxis is achievable, safe, and effective in reducing the incidence of HA VTE. We used scalable, portable methods to make a large and convincing impact on the incidence of HA VTE, while also developing and prospectively validating a VTE RAM. A wide variety of institutions are achieving significant improvement using similar strategies. Future research and improvement efforts should focus on how to accelerate integration of this model across networks of hospitals, leveraging networks with common order sets or information systems. Widespread success in improving VTE prophylaxis would likely have a far‐reaching benefit on morbidity and PE‐related mortality.

References
  1. U.S. Department of Health and Human Services. Surgeon General's Call to Action to Prevent Deep Vein Thrombosis and Pulmonary Embolism.2008 Clean-up Rule No. CU01 invoked here. . Available at: http://www.surgeongeneral.gov/topics/deepvein. Accessed June 2009.
  2. Heit JA,Melton LJ,Lohse CM, et al.Incidence of venous thromboembolism in hospitalized patients vs. community residents.Mayo Clin Proc.2001;76:11021110.
  3. Dentali F,Douketis JD,Gianni M,Lim W,Crowther MA.Meta‐analysis: anticoagulant prophylaxis to prevent symptomatic venous thromboembolism in hospitalized medical patients.Ann Intern Med.2007;146(4):278288.
  4. Heit JA,O'Fallon WM,Petterson TM, et al.Relative impact of risk factors for deep vein thrombosis and pulmonary embolism.Arch Intern Med.2002;162:12451248.
  5. Tapson VF,Hyers TM,Waldo AL, et al.Antithrombotic therapy practices in US hospitals in an era of practice guidelines.Arch Intern Med.2005;165:14581464.
  6. Clagett GP,Anderson FA,Heit JA, et al.Prevention of venous thromboembolism.Chest.1995;108:312334.
  7. Geerts WH,Bergqvist D,Pineo GF, et al.Prevention of venous thromboembolism: ACCP Evidence‐Based Clinical Practice Guidelines (8th Edition).Chest.2008;133(6 Suppl):381S453S.
  8. Goldhaber SZ,Tapson VF.A prospective registry of 5,451 patients with ultrasound‐confirmed deep vein thrombosis.Am J Cardiol.2004;93:259262.
  9. Monreal M,Kakkar A,Caprini J, et al.The outcome after treatment of venous thromboembolism is different in surgical and acutely ill medical patients. Findings from the RIETE registry.J Thromb Haemost.2004;2:18921898.
  10. Tapson V,Decousus H,Pini M, et al.Venous thromboembolism prophylaxis in acutely ill hospitalized medical patients: findings from the international medical prevention registry on venous thromboembolism.Chest.2007;132(3):936945.
  11. Kahn SR,Panju A,Geerts W, et al.Multicenter evaluation of the use of venous thromboembolism prophylaxis in acutely ill medical patients in Canada.Thromb Res.2007;119(2):145155.
  12. Cohen AT,Tapson VF,Bergmann JF, et al.Venous thromboembolism risk and prophylaxis in the acute hospital care setting (ENDORSE study): a multinational cross‐sectional study.Lancet.2008;371(9610):387394.
  13. Kakkar AK,Davidson BL,Haas SK.Compliance with recommended prophylaxis for venous thromboembolism: improving the use and rate of uptake of clinical practice guidelines.J Thromb Haemost.2004;2:221227.
  14. Anderson F,Spencer F.Risk factors for venous thromboembolism.Circulation.2003;107:I‐9I‐16.
  15. Caprini J,Arcelus J,Reyna J.Effective risk stratification of surgical and nonsurgical patients for venous thromboembolic disease.Semin Hematol.2001;38(2 suppl 5):1219.
  16. Gensini GF,Prisco D,Falciani M,Comeglio M,Colella A.Identification of candidates for prevention of venous thromboembolism.Semin Thromb Hemost.1997;23(1):5567.
  17. Haas S.Venous thromboembolic risk and its prevention in hospitalized medical patients.Semin Thromb Hemost.2002;28(6);577583.
  18. Motykie G,Zebala L,Caprini J, et al.A guide to venous thromboembolism risk factor assessment.J Thromb Thrombolysis.2000;9:253262.
  19. Tooher R,Middleton P,Pham C, et al.A systematic review of strategies to improve prophylaxis for venous thromboembolism in hospitals.Ann Surg.2005;241:397415.
  20. O'Connor C,Adhikari N,DeCaire K,Friedrich J.Medical admission order sets to improve deep vein thrombosis prophylaxis rates and other outcomes.J Hosp Med.2009;4(2):8189.
  21. Maynard G.Medical admission order sets to improve deep vein thrombosis prevention: a model for others or a prescription for mediocrity? [Editorial].J Hosp Med.2009;4(2):7780.
  22. Oxman AD,Thomson MA,Davis DA,Haynes RB.No magic bullets: a systematic review of 102 trials of interventions to improve professional practice.CMAJ.1995;153:14231431.
  23. Bullock‐Palmer RP,Weiss S,Hyman C.Innovative approaches to increase deep vein thrombosis prophylaxis rate resulting in a decrease in hospital‐acquired deep vein thrombosis at a tertiary‐care teaching hospital.J Hosp Med.2008;3(2):148155.
  24. Shojania KG,McDonald KM,Wachter RM,Owens DK.Closing the Quality Gap: A Critical Analysis of Quality Improvement Strategies.Rockville, MD:Agency for Healthcare Research and Quality;2004.
  25. CMS Office of Public Affairs. Fact Sheet: CMS Proposes Additions to List of Hospital‐Acquired Conditions for Fiscal Year 2009. Available at: http://www.cms.hhs.gov/apps/media/press/factsheet.asp?Counter=3042. Accessed June2009.
  26. The DVT/PE 2007 Knowledge Transfer Meeting. Proceedings of November 30, 2007 meeting. Available at: http://www.uhc.edu/21801.htm. Accessed June2009.
  27. Maynard G,Stein J. Preventing Hospital‐Acquired Venous Thromboembolism. A Guide for Effective Quality Improvement. Society of Hospital Medicine, VTE Quality Improvement Resource Room. Available at: http://www.hospitalmedicine.org/ResourceRoomRedesign/RR_VTE/VTE_Home.cfm. Accessed June 2009.
  28. Maynard G,Stein J.Preventing Hospital‐Acquired Venous Thromboembolism: A Guide for Effective Quality Improvement. Prepared by the Society of Hospital Medicine. AHRQ Publication No. 08–0075.Rockville, MD:Agency for Healthcare Research and Quality. September2008. Available at: http://www.ahrq.gov/qual/vtguide. Accessed June 2009.
Article PDF
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Journal of Hospital Medicine - 5(1)
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Page Number
10-18
Legacy Keywords
adhesence, care standerdization, computerized physician orders entry, deep vein thrombosis prophylaxis, preventive services, quality, improvement, venous, thromboembolism
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Pulmonary embolism (PE) and deep vein thrombosis (DVT), collectively referred to as venous thromboembolism (VTE), represent a major public health problem, affecting hundreds of thousands of Americans each year.1 The best estimates are that at least 100,000 deaths are attributable to VTE each year in the United States alone.1 VTE is primarily a problem of hospitalized and recently‐hospitalized patients.2 Although a recent meta‐analysis did not prove mortality benefit of prophylaxis in the medical population,3 PE is frequently estimated to be the most common preventable cause of hospital death.46

Pharmacologic methods to prevent VTE are safe, effective, cost‐effective, and advocated by authoritative guidelines.7 Even though the majority of medical and surgical inpatients have multiple risk factors for VTE, large prospective studies continue to demonstrate that these preventive methods are significantly underutilized, often with only 30% to 50% eligible patients receiving prophylaxis.812

The reasons for this underutilization include lack of physician familiarity or agreement with guidelines, underestimation of VTE risk, concern over risk of bleeding, and the perception that the guidelines are resource‐intensive or difficult to implement in a practical fashion.13 While many VTE risk‐assessment models are available in the literature,1418 a lack of prospectively validated models and issues regarding ease of use have further hampered widespread integration of VTE risk assessments into order sets and inpatient practice.

We sought to optimize prevention of hospital‐acquired (HA) VTE in our 350‐bed tertiary‐care academic center using a VTE prevention protocol and a multifaceted approach that could be replicated across a wide variety of medical centers.

Patients and Methods

Study Design

We developed, implemented, and refined a VTE prevention protocol and examined the impact of our efforts. We observed adult inpatients on a longitudinal basis for the prevalence of adequate VTE prophylaxis and for the incidence of HA VTE throughout a 36‐month period from calendar year 2005 through 2007, and performed a retrospective analysis for any potential adverse effects of increased VTE prophylaxis. The project adhered to the HIPAA requirements for privacy involving health‐related data from human research participants. The study was approved by the Institutional Review Board of the University of California, San Diego, which waived the requirement for individual patient informed consent.

We included all hospitalized adult patients (medical and surgical services) at our medical center in our observations and interventions, including patients of all ethnic groups, geriatric patients, prisoners, and the socially and economically disadvantaged in our population. Exclusion criteria were age under 14 years, and hospitalization on Psychiatry or Obstetrics/Gynecology services.

Development of a VTE Risk‐assessment Model and VTE Prevention Protocol

A core multidisciplinary team with hospitalists, pulmonary critical care VTE experts, pharmacists, nurses, and information specialists was formed. After gaining administrative support for standardization, we worked with medical staff leaders to gain consensus on a VTE prevention protocol for all medical and surgical areas from mid‐2005 through mid‐2006. The VTE prevention protocol included the elements of VTE risk stratification, definitions of adequate VTE prevention measures linked to the level of VTE risk, and definitions for contraindications to pharmacologic prophylactic measures. We piloted risk‐assessment model (RAM) drafts for ease of use and clarity, using rapid cycle feedback from pharmacy residents, house staff, and medical staff attending physicians. Models often cited in the literature15, 18 that include point‐based scoring of VTE risk factors (with prophylaxis choices hinging on the additive sum of scoring) were rejected based on the pilot experience.

We adopted a simple model with 3 levels of VTE risk that could be completed by the physician in seconds, and then proceeded to integrate this RAM into standardized data collection instruments and eventually (April 2006) into a computerized provider order entry (CPOE) order set (Siemmens Invision v26). Each level of VTE risk was firmly linked to a menu of acceptable prophylaxis options (Table 1). Simple text cues were used to define risk assessment, with more exhaustive listings of risk factors being relegated to accessible reference tables.

Three‐tier VTE Risk Assessment with Prevention Measures for Each Level of Risk
LowModerateHigh
  • NOTE: IPC indicated for contraindications to pharmacologic prophylaxis.

  • Abbreviations: ESRD, end‐stage renal disease; INR, international normalized ratio; IPC, intermittent pneumatic compression devices; LMWH, low‐molecular‐weight heparin; LOS, length of stay; q, dose every; SC, subcutaneously; SCI, spinal cord injury; UFH, unfractionated heparin; VTE, venous thromboembolism.

Ambulatory patient without VTE risk factors; observation patient with expected LOS 2 days; same day surgery or minor surgeryAll other patients (not in low‐risk or high‐risk category); most medical/surgical patients; respiratory insufficiency, heart failure, acute infectious, or inflammatory diseaseLower extremity arthroplasty; hip, pelvic, or severe lower extremity fractures; acute SCI with paresis; multiple major trauma; abdominal or pelvic surgery for cancer
Early ambulationUFH 5000 units SC q 8 hours; OR LMWH q day; OR UFH 5000 units SC q 12 hours (if weight < 50 kg or age > 75 years); AND suggest adding IPCLMWH (UFH if ESRD); OR fondaparinux 2.5 mg SC daily; OR warfarin, INR 2‐3; AND IPC (unless not feasible)

Intermittent pneumatic compression devices were endorsed as an adjunct in all patients in the highest risk level, and as the primary method in patients with contraindications to pharmacologic prophylaxis. Aspirin was deemed an inappropriate choice for VTE prophylaxis. Subcutaneous unfractionated or low‐molecular‐weight heparin were endorsed as the primary method of prophylaxis for the majority of patients without contraindications.

Integration of the VTE Protocol into Order Sets

An essential strategy for the success of the VTE protocol included integrating guidance for the physician into the flow of patient care, via standardized order sets. The CPOE VTE prevention order set was modular by design, as opposed to a stand alone design. After conferring with appropriate stakeholders, preexisting and nonstandardized prompts for VTE prophylaxis were removed from commonly used order sets, and the standardized module was inserted in its place. This allowed for integration of the standardized VTE prevention module into all admission and transfer order sets, essentially insuring that all patients admitted or transferred within the medical center would be exposed to the protocol. Physicians using a variety of admission and transfer order sets were prompted to select each patient's risk for VTE, and declare the presence or absence of contraindications to pharmacologic prophylaxis. Only the VTE prevention options most appropriate for the patient's VTE and anticoagulation risk profile were presented as the default choice for VTE prophylaxis. Explicit designation of VTE risk level and a prophylaxis choice were presented in a hard stop mechanism, and utilization of these orders was therefore mandatory, not optional. Proper use (such as the proper classification of VTE risk by the ordering physician) was actively monitored on an auditing basis, and order sets were modified occasionally on the basis of subjective and objective feedback.

Assessment of VTE Risk Assessment Interobserver Agreement

Data from 150 randomly selected patients from the audit pool (from late 2005 through mid‐2006) were abstracted by the nurse practitioner in a detailed manner. Five independent reviewers assessed each patient for VTE risk level, and for a determination of whether or not they were on adequate VTE prophylaxis on the day of the audit per protocol. Interobserver agreement was calculated for these parameters using kappa scores.

Prospective Monitoring of Adequate VTE Prophylaxis

A daily medical center inpatient census report of eligible patients in the medical center for >48 hours was downloaded into an Microsoft Excel spreadsheet, with each patient assigned a consecutive number. The Excel random number generator plug‐in function was used to generate a randomly sequenced list of the patients. The research nurse practitioner targeted serial patients on the list for further study, until she accomplished the requisite number of audits each day. The mean number of audits per month declined over the study years as the trends stabilized and as grant funding expired, but remained robust throughout (2005: 107 audits per month; 2006: 80 audits per month; and 2007: 57 audits per month).

The data collected on each patient randomly selected for audit included age, gender, location, service, date and time of review, and date of admission. The audit VTE RAM (identical to the VTE RAM incorporated into the order set), was used to classify each patient's VTE risk as low, moderate, or high. For each audit, we determined if the patient was on an adequate VTE prevention regimen consistent with our protocol, given their VTE risk level, demographics, and absence or presence of contraindications to pharmacologic prophylaxis. All questionable cases were reviewed by at least 2 physicians at weekly meetings with a final consensus determination. Adequacy of the VTE regimen was judged by orders entered on the day of the audit, but we also noted whether or not ordered intermittent compression devices were in place and functioning at the time of the audit.

Prospective (Concurrent) Discovery and Analysis of VTE Cases

The team nurse practitioner used the PACS radiology reporting and archival system (IMPAX version 4.5; AGFA Healthcare Informatics, Greenville, SC) to identify all new diagnoses of VTE, in the process described below.

Procedure codes for following studies were entered into the IMPAX search engine to locate all such exams performed in the previous 1 to 3 days:

  • Ultrasound exams of the neck, upper extremities, and lower extremities;

  • Computed tomography (CT) angiograms of the chest;

  • Ventilation/perfusion nuclear medicine scans; and

  • Pulmonary angiograms.

 

Negative studies and studies that revealed unchanged chronic thromboses were excluded, while clots with a chronic appearance but no evidence of prior diagnosis were included. Iliofemoral, popliteal, calf vein, subclavian, internal and external jugular vein, and axillary vein thromboses were therefore included, as were all PEs. Less common locations, such as renal vein and cavernous sinus thromboses, were excluded. The improvement/research team exerted no influence over decisions about whether or not testing was done.

Each new case of VTE was then classified as HA VTE or community‐acquired VTE. A new VTE was classified as HA if the diagnosis was first suspected and made in the hospital. A newly diagnosed VTE was also classified as HA if the VTE was suspected in the ambulatory setting, but the patient had been hospitalized within the arbitrary window of the preceding 30 days.

Each new diagnosis of HA VTE was reviewed by core members of the multidisciplinary support team. This investigation included a determination of whether the patient was on an adequate VTE prophylaxis regimen at the time of the HA VTE, using the RAM and linked prophylaxis menu described above. The VTE prevention regimen ordered at the time the inpatient developed the HA VTE was classified as adherent or nonadherent to the University of California, San Diego (UCSD) protocol: patients who developed VTE when on suboptimal prophylaxis per protocol were classified as having a potentially preventable case. Potentially iatrogenic precipitants of VTE (such as the presence of a central venous catheter or restraints) were also noted. All data were entered into a Microsoft Access database for ease of retrieval and reporting.

All tests for VTE were performed based on clinical signs and symptoms, rather than routine screening, except for the Trauma and Burn services, which also screen for VTE in high‐risk patients per their established screening protocols.

Statistical Analysis of VTE Prophylaxis and HA VTE Cases

Gender differences between cases of VTE and randomly sampled and audited inpatients were examined by chi‐square analysis, and analysis of variance (ANOVA) was used to examine any age or body mass index (BMI) differences between audits and cases.

The unadjusted risk ratio (RR) for adequate prophylaxis was compared by year, with year 2005 being the baseline (comparison) year, by chi‐square analysis.

The unadjusted RR of HA VTE was calculated by dividing the number of cases found in the calendar year by the hospital census of adult inpatients at risk. For each case, a classification for the type of VTE (PE vs. DVT vs. combinations) was recorded. Cases not receiving adequate prophylaxis were categorized as preventable DVT. Unadjusted RRs were calculated for each year by chi‐square analysis, compared to the baseline (2005) year.

All data were analyzed using Stata (version 10; Stata Corp., College Station, TX). Results for the different analysis were considered significant at P < 0.05.

Retrospective Study of Unintentional Adverse Effects

The increase in anticoagulant use accompanying the introduction of the VTE prophylaxis order set warranted an evaluation of any subsequent rise in related adverse events. A study was done to determine the rates of bleeding and heparin‐induced thrombocytopenia (HIT) before and after the implementation of the VTE prophylaxis order set.

A retrospective analysis was conducted to evaluate outcomes in our inpatients from December 2004 through November 2006, with April to November, 2006 representing the post‐order set implementation time period. Any patient with a discharge diagnosis code of e934.2 (anticoagulant‐related adverse event) was selected for study to identify possible bleeding attributable to pharmacologic VTE prophylaxis. Major or minor bleeding attributable to pharmacologic VTE prophylaxis was defined as a bleed occurring 72 hours after receiving pharmacologic VTE prophylaxis. Major bleeding was defined as cerebrovascular, gastrointestinal, retroperitoneal, or overt bleeding with a decrease in hemoglobin 2 mg/dL with clinical symptoms such as hypotension or hypoxia (not associated with hemodialysis) or transfusion of 2 units of packed red blood cells. Minor bleeding was defined as ecchymosis, epistaxis, hematoma, hematuria, hemoptysis, petechiae, or bleeding without a decrease in hemoglobin 2 g/dL.

Possible cases of HIT were identified by screening for a concomitant secondary thrombocytopenia code (287.4). Chart review was then conducted to determine a causal relationship between the use of pharmacologic VTE prophylaxis and adverse events during the hospital stay. HIT attributable to pharmacologic VTE prophylaxis was determined by assessing if patients developed any of the following clinical criteria after receiving pharmacologic VTE prophylaxis: platelet count <150 109/L or 50% decrease from baseline, with or without an associated venous or arterial thrombosis or other sequelae (skin lesions at injection site, acute systemic reaction) and/or a positive heparin‐induced platelet activation (HIPA) test. In order to receive a diagnosis of HIT, thrombocytopenia must have occurred between days 5 to 15 of heparin therapy, unless existing evidence suggested that the patient developed rapid‐onset HIT or delayed‐onset HIT. Rapid‐onset HIT was defined as an abrupt drop in platelet count upon receiving a heparin product, due to heparin exposure within the previous 100 days. Delayed‐onset HIT was defined as HIT that developed several days after discontinuation of heparin. Other evident causes of thrombocytopenia were ruled out.

Statistical Analysis of Retrospective Study of Unintentional Adverse Effects

Regression analysis with chi‐square and ANOVA were used in the analysis of the demographic data. RRs were calculated for the number of cases coded with an anticoagulant‐related adverse event secondary thrombocytopenia before and after the order set implementation.

Educational Efforts and Feedback

Members of the multidisciplinary team presented information on HA VTE and the VTE prevention protocol at Medical and Surgical grand rounds, teaching rounds, and noon conference, averaging 1 educational session per quarter. Feedback and education was provided to physicians and nursing staff when audits revealed that a patient had inadequate prophylaxis with reference to the protocol standard. In addition, these conversations provided on opportunity to explore reasons for nonadherence with the protocol, confusion regarding the VTE RAM, and other barriers to effective prophylaxis, thereby providing guidance for further protocol revision and educational efforts. We adjusted the order set based on active monitoring of order set use and the audit process.

Results

There were 30,850 adult medical/surgical inpatients admitted to the medical center with a length of stay of 48 hours or more in 2005 to 2007, representing 186,397 patient‐days of observation. A total of 2,924 of these patients were randomly sampled during the VTE prophylaxis audit process (mean 81 audits per month). Table 2 shows the characteristics of randomly sampled audit patients and of the patients diagnosed with HA VTE. The demographics of the 30,850‐inpatient population (mean age = 50 years; 60.7% male; 52% Surgical Services) mirrored the demographics of the randomly sampled inpatients that underwent audits, validating the random sampling methods.

Description of Population Audits and Hospital‐acquired Venous Thromboembolism
 Number (n = 3285)% of Study Population*Cases (n = 361) [n (%)]Audits (n = 2924) [n (%)]OR (95% CI)
  • Abbreviations: CI, confidence interval; OR, odds ratio; SD, standard deviation.

  • Cases and audits.

Age (years) mean SD51 16 (range 15‐100) 53 1750 171.01 (1.003‐1.016)
Gender, males199361213 (59)1782 (61)0.93 (0.744‐1.16)
Major service:     
Surgery171452200 (55)1516 (52) 
Medicine156648161 (45)1408 (48) 
Service, detail     
Hospitalist10413283 (23)958 (33) 
General surgery8312575 (21)756 (26) 
Trauma4191377 (22)342 (12) 
Cardiology3131045 (13)268 (9) 
Orthopedics244715 (4)229 (8) 
Burn unit205629 (8)176 (6) 
Other222730 (8)192 (7) 

The majority of inpatients sampled in the audits were in the moderate VTE risk category (84%), 12% were in the high‐risk category, and 4% were in the low‐risk category. The distribution of VTE risk did not change significantly over this time period.

Interobserver Agreement

The VTE RAM interobserver agreement was assessed on 150 patients with 5 observers as described above. The kappa score for the VTE risk level was 0.81. The kappa score for the judgment of whether the patient was on adequate prophylaxis or not was 0.90.

Impact on Percent of Patients with Adequate Prophylaxis (Longitudinal Audits)

Audits of randomly sampled inpatients occurred longitudinally throughout the study period as described above. Based on the intervention, the percent of patients on adequate prophylaxis improved significantly (P < 0.001) by each calendar year (see Table 3), from a baseline of 58% in 2005 to 78% in 2006 (unadjusted relative benefit = 1.35; 95% confidence interval [CI] = 1.28‐1.43), and 93% in 2007 (unadjusted relative benefit = 1.61; 95% CI = 1.52, 1.69). The improvement seen was more marked in the moderate VTE risk patients when compared to the high VTE risk patients. The percent of audited VTE prophylaxis improved from 53% in calendar year (CY) 2005 to 93% in 2007 (unadjusted relative benefit = 1.75; 95% CI = 1.70‐1.81) in the moderate VTE risk group, while the high VTE risk group improved from 83% to 92% in the same time period (unadjusted relative benefit = 1.11; 95% CI = 0.95‐1.25).

Unadjusted Risk Ratio (Relative Benefit) of Receiving Adequate Venous Thromboembolism Prophylaxis by Year, in Randomly Selected Inpatients
 200520062007
  • Abbreviation: CI, confidence interval.

  • P < 0.001.

All audits1279960679
Prophylaxis adequate, n (%)740 (58)751 (78)631 (93)
Relative benefit (95% CI)11.35* (1.28‐1.43)1.61* (1.52‐1.69)

Overall, adequate VTE prophylaxis was present in over 98% of audited patients in the last 6 months of 2007, and this high rate has been sustained throughout 2008. Age, ethnicity, and gender were not associated with differential rates of adequate VTE prophylaxis.

Figure 1 is a timeline of interventions and the impact on the prevalence of adequate VTE prophylaxis. The first 7 to 8 months represent the baseline rate 50% to 55% of VTE prophylaxis. In this baseline period, the improvement team was meeting, but had not yet begun meeting with the large variety of medical and surgical service leaders. Consensus‐building sessions with these leaders in the latter part of 2005 through mid‐2006 correlated with improvement in adequate VTE prophylaxis rates to near 70%. The consensus‐building sessions also prepared these varied services for a go live date of the modular order set that was incorporated into all admit and transfer order sets, often replacing preexisting orders referring to VTE prevention measures. The order set resulted in an improvement to 80% adequate prophylaxis, with the incremental improvement occurring virtually overnight with the go live date at the onset of quarter 2 (Q2) of 2006. Monitoring of the order set use confirmed that it was easy and efficient to use, but also revealed that physicians were at times classifying patients as low VTE risk inaccurately, when they possessed qualities that actually qualified them for moderate risk status by our protocol. We therefore inserted a secondary CPOE screen when patients were categorized as low VTE risk, asking the physician to deny or confirm that the patient had no risk factors that qualified them for moderate risk status. This confirmation screen essentially acted as a reminder to the physician to ask Are you sure this patient does not need VTE prophylaxis? This minor modification of the CPOE order set improved adequate VTE prophylaxis rates to 90%. Finally, we asked nurses to evaluate patients who were not on therapeutic or prophylactic doses of anticoagulants. Patients with VTE risk factors but no obvious contraindications generated a note from the nurse to the doctor, prompting the doctor to reassess VTE risk and potential contraindications. This simple intervention raised the percent of audited patients on adequate VTE prophylaxis to 98% in the last 6 months of 2007.

Figure 1
Percent of randomly sampled inpatients with adequate VTE prophylaxis; 2,924 randomly sampled adult inpatients (mean 81 patients per month) audited for adequacy of VTE prophylaxis regimen on the day of audit. Improvement is correlated with incremental interventions on the statistical process control chart. Control limits determined using a p‐chart macro in Microsoft Excel with a P value of 0.01. VTE = venous thromboembolism; Q = quarter; ID = identification.

Description of Prospectively Identified VTE

We identified 748 cases of VTE among patients admitted to the medical center over the 36‐month study period; 387 (52%) were community‐acquired VTE. There were 361 HA cases (48% of total cases) over the same time period. There was no difference in age, gender, or BMI between the community‐acquired and hospital‐related VTE.

Of the 361 HA cases, 199 (55%) occurred on Surgical Services and 162 (45%) occurred on Medical Services; 58 (16%) unique patients had pulmonary emboli, while 303 (84%) patients experienced only DVT. Remarkably, almost one‐third of the DVT occurred in the upper extremities (108 upper extremities, 240 lower extremities), and most (80%) of the upper‐extremity DVT were associated with central venous catheters.

Of 361 HA VTE cases, 292 (81%) occurred in those in the moderate VTE risk category, 69 HA VTE cases occurred in high‐risk category patients (19%), and no VTE occurred in patients in the low‐risk category.

Improvement in HA VTE

HA VTE were identified and each case analyzed on an ongoing basis over the entire 3 year study period, as described above. Table 4 depicts a comparison of HA VTE on a year‐to‐year basis and the impact of the VTE prevention protocol on the incidence of HA VTE. In 2007 (the first full CY after the implementation of the order set) there was a 39% relative risk reduction (RRR) in the risk of experiencing an HA VTE. The reduction in the risk of preventable HA VTE was even more marked (RRR = 86%; 7 preventable VTE in 2007, compared to 44 in baseline year of 2005; RR = 0.14; 95% CI = 0.06‐0.31).

HA VTE Characteristics and Positive Impact of VTE Prevention Protocol, Demonstrating Significant Risk Reduction for Cases of HA VTE, HA DVT, and Preventable VTE from 2005 to 2007
 HA VTE by Year
 200520062007
  • Abbreviations: CI, confidence interval; DVT, deep vein thrombosis; HA, hospital‐acquired; PE, pulmonary embolus; VTE, venous thromboembolism.

  • P < 0.001.

  • P < 0.01.

Patients at Risk9720992311,207
Cases with any HA VTE13113892
Risk for HA VTE1 in 761 in 731 in 122
Unadjusted relative risk (95% CI)1.01.03 (0.81‐1.31)0.61* (0.47‐0.79)
Cases with PE212215
Risk for PE1 in 4631 in 4511 in 747
Unadjusted relative risk (95% CI)1.01.03 (0.56‐1.86)0.62 (0.32‐1.20)
Cases with DVT (and no PE)11011677
Risk for DVT1 in 881 in 851 in 146
Unadjusted relative risk (95% CI)1.01.03 (0.80‐1.33)0.61* (0.45‐0.81)
Cases with preventable VTE44217
Risk for preventable VTE1 in 2211 in 4731 in 1601
Unadjusted relative risk (95% CI)1.00.47 (0.28‐0.79)0.14* (0.06‐0.31)

Retrospective Analysis of Impact on HIT and Bleeding

There were no statistically significant differences in the number of cases coded for an anticoagulant‐related bleed or secondary thrombocytopenia (Table 5). Chart review revealed there were 2 cases of minor bleeding attributable to pharmacologic VTE prophylaxis before the order set implementation. There were no cases after implementation. No cases of HIT attributable to pharmacologic VTE prophylaxis were identified in either study period, with all cases being attributed to therapeutic anticoagulation.

Pre/Post‐orderset Anticoagulation Related Adverse Events
 Pre‐order SetPost‐order SetPost‐order Set RR (CI)
  • Abbreviations: RR, relative risk; CI, 95% confidence interval; HIT, Heparin induced Thrombocytopenia

Bleeding events74280.70 (0.46‐1.08)
Due to prophylaxis2 (minor)0 
HIT events971.44 (0.54‐3.85)
Due to prophylaxis00 
Patient admissions3211717294 

Discussion

We demonstrated that implementation of a standardized VTE prevention protocol and order set could result in a dramatic and sustained increase in adequate VTE prophylaxis across an entire adult inpatient population. This achievement is more remarkable given the rigorous criteria defining adequate prophylaxis. Mechanical compression devices were not accepted as primary prophylaxis in moderate‐risk or high‐risk patients unless there was a documented contraindication to pharmacologic prophylaxis, and high VTE risk patients required both mechanical and pharmacologic prophylaxis to be considered adequately protected, for example. The relegation of mechanical prophylaxis to an ancillary role was endorsed by our direct observations, in that we were only able to verify that ordered mechanical prophylaxis was in place 60% of the time.

The passive dissemination of guidelines is ineffective in securing VTE prophylaxis.19 Improvement in VTE prophylaxis has been suboptimal when options for VTE prophylaxis are offered without providing guidance for VTE risk stratification and all options (pharmacologic, mechanical, or no prophylaxis) are presented as equally acceptable choices.20, 21 Our multifaceted strategy using multiple interventions is an approach endorsed by a recent systematic review19 and others in the literature.22, 23 The interventions we enacted included a method to prompt clinicians to assess patients for VTE risk, and then to assist in the selection of appropriate prophylaxis from standardized options. Decision support and clinical reminders have been shown to be more effective when integrated into the workflow19, 24; therefore, a key strategy of our study involved embedding the VTE risk assessment tool and guidance toward appropriate prophylactic regimens into commonly used admission/transfer order sets. We addressed the barriers of physician unfamiliarity or disagreement with guidelines10 with education and consensus‐building sessions with clinical leadership. Clinical feedback from audits, peer review, and nursing‐led interventions rounded out the layered multifaceted interventional approach.

We designed and prospectively validated a VTE RAM during the course of our improvement efforts, and to our knowledge our simple 3‐category (or 3‐level) VTE risk assessment model is the only validated model. The VTE risk assessment/prevention protocol was validated by several important parameters. First, it proved to be practical and easy to use, taking only seconds to complete, and it was readily adopted by all adult medical and surgical services. Second, the VTE RAM demonstrated excellent interobserver agreement for VTE risk level and decisions about adequacy of VTE prophylaxis with 5 physician reviewers. Third, the VTE RAM predicted risk for VTE. All patients suffering from HA VTE were in the moderate‐risk to high‐risk categories, and HA VTE occurred disproportionately in those meeting criteria for high risk. Fourth, implementation of the VTE RAM/protocol resulted in very high, sustained levels of VTE prophylaxis without any detectable safety concerns. Finally and perhaps most importantly, high rates of adherence to the VTE protocol resulted in a 40% decline in the incidence of HA VTE in our institution.

The improved prevalence of adequate VTE prophylaxis reduced, but did not eliminate, HA VTE. The reduction observed is consistent with the 40% to 50% efficacy of prophylaxis reported in the literature.7 Our experience highlights the recent controversy over proposals by the Centers for Medicare & Medicaid Services (CMS) to add HA VTE to the list of do not pay conditions later this year,25 as it is clear from our data that even near‐perfect adherence with accepted VTE prevention measures will not eliminate HA VTE. After vigorous pushback about the fairness of this measure, the HA VTE do not pay scope was narrowed to include only certain major orthopedic procedure patients.

Services with a preponderance of moderate‐risk patients had the largest reduction in HA VTE. Efforts that are focused only on high‐risk orthopedic, trauma, and critical care patients will miss the larger opportunities for maximal reduction in HA VTE for multiple reasons. First, moderate VTE risk patients are far more prevalent than high VTE risk patients (84% vs. 12% of inpatients at our institution). Second, high‐risk patients are already at a baseline relatively high rate of VTE prophylaxis compared to their moderate VTE risk counterparts (83% vs. 53% at our institution). Third, a large portion of patients at high risk for VTE (such as trauma patients) also have the largest prevalence of absolute or relative contraindications to pharmacologic prophylaxis, limiting the effect size of prevention efforts.

Major strengths of this study included ongoing rigorous concurrent measurement of both processes (percent of patients on adequate prophylaxis) and outcomes (HA VTE diagnosed via imaging studies) over a prolonged time period. The robust random sampling of inpatients insured that changes in VTE prophylaxis rates were not due to changes in the distribution of VTE risk or bias potentially introduced from convenience samples. The longitudinal monitoring of imaging study results for VTE cases is vastly superior to using administrative data that is reliant on coding. The recent University Healthsystem Consortium (UHC) benchmarking data on venous thromboembolism were sobering but instructive.26 UHC used administrative discharge codes for VTE in a secondary position to identify patients with HA VTE, which is a common strategy to follow the incidence of HA VTE. The accuracy of identifying surgical patients with an HA VTE was only 60%. Proper use of the present on admission (POA) designation would have improved this to 83%, but 17% of cases either did not occur or had history only with a labor‐intensive manual chart review. Performance was even worse in medical patients, with only a 30% accuracy rate, potentially improved to 79% if accurate POA designation had been used, and 21% of cases identified by administrative methods either did not occur or had history only. In essence, unless an improvement team uses chart review of each case potentially identified as a HA VTE case, the administrative data are not reliable. Concurrent discovery of VTE cases allows for a more accurate and timely chart review, and allows for near real‐time feedback to the responsible treatment team.

The major limitation of this study is inherent in the observational design and the lack of a control population. Other factors besides our VTE‐specific improvement efforts could affect process and outcomes, and reductions in HA VTE could conceivably occur because of changes in the make‐up of the admitted inpatient population. These limitations are mitigated to some degree by several observations. The VTE risk distribution in the randomly sampled inpatient population did not vary significantly from year to year. The number of HA VTE was reduced in 2007 even though the number of patients and patient days at risk for developing VTE went up. The incidence of community‐acquired VTE remained constant over the same time period, highlighting the consistency of our measurement techniques and the VTE risk in the community we serve. Last, the improvements in VTE prophylaxis rates increased at times that correlated well with the introduction of layered interventions, as depicted in Figure 1.

There were several limitations to the internal study on adverse effects of VTE protocol implementation. First, this was a retrospective study, so much of the data collection was dependent upon physician progress notes and discharge summaries. Lack of documentation could have precluded the appropriate diagnosis codes from being assigned. Next, the study population was generated from coding data, so subjectivity could have been introduced during the coding process. Also, a majority of the patients did not fit the study criteria due to discharge with the e934.2 code, because they were found to have an elevated international normalized ratio (INR) after being admitted on warfarin. Finally, chart‐reviewer bias could have affected the results, as the chart reviewer became more proficient at reviewing charts over time. Despite these limitations, the study methodology allowed for screening of a large population for rare events. Bleeding may be a frequent concern with primary thromboprophylaxis, but data from clinical trials and this study help to demonstrate that rates of adverse events from pharmacologic VTE prophylaxis are very rare.

Another potential limitation is raised by the question of whether our methods can be generalized to other sites. Our site is an academic medical center and we have CPOE, which is present in only a small minority of centers. Furthermore, one could question how feasible it is to get institution‐wide consensus for a VTE prevention protocol in settings with heterogenous medical staffs. To address these issues, we used a proven performance improvement framework calling for administrative support, a multidisciplinary improvement team, reliable measures, and a multifaceted approach to interventions. This framework and our experiences have been incorporated into improvement guides27, 28 that have been the centerpiece of the Society of Hospital Medicine VTE Prevention Collaborative improvement efforts in a wide variety of medical environments. The collaborative leadership has observed that success is the rule when this model is followed, in institutions large and small, academic or community, and in both paper and CPOE environments. Not all of these sites use a VTE RAM identical to ours, and there are local nuances to preferred choices of prophylaxis. However, they all incorporated simple VTE risk stratification with only a few levels of risk. Reinforcing the expectation that pharmacologic prophylaxis is indicated for the majority of inpatients is likely more important than the nuances of choices for each risk level.

We demonstrated that dramatic improvement in VTE prophylaxis is achievable, safe, and effective in reducing the incidence of HA VTE. We used scalable, portable methods to make a large and convincing impact on the incidence of HA VTE, while also developing and prospectively validating a VTE RAM. A wide variety of institutions are achieving significant improvement using similar strategies. Future research and improvement efforts should focus on how to accelerate integration of this model across networks of hospitals, leveraging networks with common order sets or information systems. Widespread success in improving VTE prophylaxis would likely have a far‐reaching benefit on morbidity and PE‐related mortality.

Pulmonary embolism (PE) and deep vein thrombosis (DVT), collectively referred to as venous thromboembolism (VTE), represent a major public health problem, affecting hundreds of thousands of Americans each year.1 The best estimates are that at least 100,000 deaths are attributable to VTE each year in the United States alone.1 VTE is primarily a problem of hospitalized and recently‐hospitalized patients.2 Although a recent meta‐analysis did not prove mortality benefit of prophylaxis in the medical population,3 PE is frequently estimated to be the most common preventable cause of hospital death.46

Pharmacologic methods to prevent VTE are safe, effective, cost‐effective, and advocated by authoritative guidelines.7 Even though the majority of medical and surgical inpatients have multiple risk factors for VTE, large prospective studies continue to demonstrate that these preventive methods are significantly underutilized, often with only 30% to 50% eligible patients receiving prophylaxis.812

The reasons for this underutilization include lack of physician familiarity or agreement with guidelines, underestimation of VTE risk, concern over risk of bleeding, and the perception that the guidelines are resource‐intensive or difficult to implement in a practical fashion.13 While many VTE risk‐assessment models are available in the literature,1418 a lack of prospectively validated models and issues regarding ease of use have further hampered widespread integration of VTE risk assessments into order sets and inpatient practice.

We sought to optimize prevention of hospital‐acquired (HA) VTE in our 350‐bed tertiary‐care academic center using a VTE prevention protocol and a multifaceted approach that could be replicated across a wide variety of medical centers.

Patients and Methods

Study Design

We developed, implemented, and refined a VTE prevention protocol and examined the impact of our efforts. We observed adult inpatients on a longitudinal basis for the prevalence of adequate VTE prophylaxis and for the incidence of HA VTE throughout a 36‐month period from calendar year 2005 through 2007, and performed a retrospective analysis for any potential adverse effects of increased VTE prophylaxis. The project adhered to the HIPAA requirements for privacy involving health‐related data from human research participants. The study was approved by the Institutional Review Board of the University of California, San Diego, which waived the requirement for individual patient informed consent.

We included all hospitalized adult patients (medical and surgical services) at our medical center in our observations and interventions, including patients of all ethnic groups, geriatric patients, prisoners, and the socially and economically disadvantaged in our population. Exclusion criteria were age under 14 years, and hospitalization on Psychiatry or Obstetrics/Gynecology services.

Development of a VTE Risk‐assessment Model and VTE Prevention Protocol

A core multidisciplinary team with hospitalists, pulmonary critical care VTE experts, pharmacists, nurses, and information specialists was formed. After gaining administrative support for standardization, we worked with medical staff leaders to gain consensus on a VTE prevention protocol for all medical and surgical areas from mid‐2005 through mid‐2006. The VTE prevention protocol included the elements of VTE risk stratification, definitions of adequate VTE prevention measures linked to the level of VTE risk, and definitions for contraindications to pharmacologic prophylactic measures. We piloted risk‐assessment model (RAM) drafts for ease of use and clarity, using rapid cycle feedback from pharmacy residents, house staff, and medical staff attending physicians. Models often cited in the literature15, 18 that include point‐based scoring of VTE risk factors (with prophylaxis choices hinging on the additive sum of scoring) were rejected based on the pilot experience.

We adopted a simple model with 3 levels of VTE risk that could be completed by the physician in seconds, and then proceeded to integrate this RAM into standardized data collection instruments and eventually (April 2006) into a computerized provider order entry (CPOE) order set (Siemmens Invision v26). Each level of VTE risk was firmly linked to a menu of acceptable prophylaxis options (Table 1). Simple text cues were used to define risk assessment, with more exhaustive listings of risk factors being relegated to accessible reference tables.

Three‐tier VTE Risk Assessment with Prevention Measures for Each Level of Risk
LowModerateHigh
  • NOTE: IPC indicated for contraindications to pharmacologic prophylaxis.

  • Abbreviations: ESRD, end‐stage renal disease; INR, international normalized ratio; IPC, intermittent pneumatic compression devices; LMWH, low‐molecular‐weight heparin; LOS, length of stay; q, dose every; SC, subcutaneously; SCI, spinal cord injury; UFH, unfractionated heparin; VTE, venous thromboembolism.

Ambulatory patient without VTE risk factors; observation patient with expected LOS 2 days; same day surgery or minor surgeryAll other patients (not in low‐risk or high‐risk category); most medical/surgical patients; respiratory insufficiency, heart failure, acute infectious, or inflammatory diseaseLower extremity arthroplasty; hip, pelvic, or severe lower extremity fractures; acute SCI with paresis; multiple major trauma; abdominal or pelvic surgery for cancer
Early ambulationUFH 5000 units SC q 8 hours; OR LMWH q day; OR UFH 5000 units SC q 12 hours (if weight < 50 kg or age > 75 years); AND suggest adding IPCLMWH (UFH if ESRD); OR fondaparinux 2.5 mg SC daily; OR warfarin, INR 2‐3; AND IPC (unless not feasible)

Intermittent pneumatic compression devices were endorsed as an adjunct in all patients in the highest risk level, and as the primary method in patients with contraindications to pharmacologic prophylaxis. Aspirin was deemed an inappropriate choice for VTE prophylaxis. Subcutaneous unfractionated or low‐molecular‐weight heparin were endorsed as the primary method of prophylaxis for the majority of patients without contraindications.

Integration of the VTE Protocol into Order Sets

An essential strategy for the success of the VTE protocol included integrating guidance for the physician into the flow of patient care, via standardized order sets. The CPOE VTE prevention order set was modular by design, as opposed to a stand alone design. After conferring with appropriate stakeholders, preexisting and nonstandardized prompts for VTE prophylaxis were removed from commonly used order sets, and the standardized module was inserted in its place. This allowed for integration of the standardized VTE prevention module into all admission and transfer order sets, essentially insuring that all patients admitted or transferred within the medical center would be exposed to the protocol. Physicians using a variety of admission and transfer order sets were prompted to select each patient's risk for VTE, and declare the presence or absence of contraindications to pharmacologic prophylaxis. Only the VTE prevention options most appropriate for the patient's VTE and anticoagulation risk profile were presented as the default choice for VTE prophylaxis. Explicit designation of VTE risk level and a prophylaxis choice were presented in a hard stop mechanism, and utilization of these orders was therefore mandatory, not optional. Proper use (such as the proper classification of VTE risk by the ordering physician) was actively monitored on an auditing basis, and order sets were modified occasionally on the basis of subjective and objective feedback.

Assessment of VTE Risk Assessment Interobserver Agreement

Data from 150 randomly selected patients from the audit pool (from late 2005 through mid‐2006) were abstracted by the nurse practitioner in a detailed manner. Five independent reviewers assessed each patient for VTE risk level, and for a determination of whether or not they were on adequate VTE prophylaxis on the day of the audit per protocol. Interobserver agreement was calculated for these parameters using kappa scores.

Prospective Monitoring of Adequate VTE Prophylaxis

A daily medical center inpatient census report of eligible patients in the medical center for >48 hours was downloaded into an Microsoft Excel spreadsheet, with each patient assigned a consecutive number. The Excel random number generator plug‐in function was used to generate a randomly sequenced list of the patients. The research nurse practitioner targeted serial patients on the list for further study, until she accomplished the requisite number of audits each day. The mean number of audits per month declined over the study years as the trends stabilized and as grant funding expired, but remained robust throughout (2005: 107 audits per month; 2006: 80 audits per month; and 2007: 57 audits per month).

The data collected on each patient randomly selected for audit included age, gender, location, service, date and time of review, and date of admission. The audit VTE RAM (identical to the VTE RAM incorporated into the order set), was used to classify each patient's VTE risk as low, moderate, or high. For each audit, we determined if the patient was on an adequate VTE prevention regimen consistent with our protocol, given their VTE risk level, demographics, and absence or presence of contraindications to pharmacologic prophylaxis. All questionable cases were reviewed by at least 2 physicians at weekly meetings with a final consensus determination. Adequacy of the VTE regimen was judged by orders entered on the day of the audit, but we also noted whether or not ordered intermittent compression devices were in place and functioning at the time of the audit.

Prospective (Concurrent) Discovery and Analysis of VTE Cases

The team nurse practitioner used the PACS radiology reporting and archival system (IMPAX version 4.5; AGFA Healthcare Informatics, Greenville, SC) to identify all new diagnoses of VTE, in the process described below.

Procedure codes for following studies were entered into the IMPAX search engine to locate all such exams performed in the previous 1 to 3 days:

  • Ultrasound exams of the neck, upper extremities, and lower extremities;

  • Computed tomography (CT) angiograms of the chest;

  • Ventilation/perfusion nuclear medicine scans; and

  • Pulmonary angiograms.

 

Negative studies and studies that revealed unchanged chronic thromboses were excluded, while clots with a chronic appearance but no evidence of prior diagnosis were included. Iliofemoral, popliteal, calf vein, subclavian, internal and external jugular vein, and axillary vein thromboses were therefore included, as were all PEs. Less common locations, such as renal vein and cavernous sinus thromboses, were excluded. The improvement/research team exerted no influence over decisions about whether or not testing was done.

Each new case of VTE was then classified as HA VTE or community‐acquired VTE. A new VTE was classified as HA if the diagnosis was first suspected and made in the hospital. A newly diagnosed VTE was also classified as HA if the VTE was suspected in the ambulatory setting, but the patient had been hospitalized within the arbitrary window of the preceding 30 days.

Each new diagnosis of HA VTE was reviewed by core members of the multidisciplinary support team. This investigation included a determination of whether the patient was on an adequate VTE prophylaxis regimen at the time of the HA VTE, using the RAM and linked prophylaxis menu described above. The VTE prevention regimen ordered at the time the inpatient developed the HA VTE was classified as adherent or nonadherent to the University of California, San Diego (UCSD) protocol: patients who developed VTE when on suboptimal prophylaxis per protocol were classified as having a potentially preventable case. Potentially iatrogenic precipitants of VTE (such as the presence of a central venous catheter or restraints) were also noted. All data were entered into a Microsoft Access database for ease of retrieval and reporting.

All tests for VTE were performed based on clinical signs and symptoms, rather than routine screening, except for the Trauma and Burn services, which also screen for VTE in high‐risk patients per their established screening protocols.

Statistical Analysis of VTE Prophylaxis and HA VTE Cases

Gender differences between cases of VTE and randomly sampled and audited inpatients were examined by chi‐square analysis, and analysis of variance (ANOVA) was used to examine any age or body mass index (BMI) differences between audits and cases.

The unadjusted risk ratio (RR) for adequate prophylaxis was compared by year, with year 2005 being the baseline (comparison) year, by chi‐square analysis.

The unadjusted RR of HA VTE was calculated by dividing the number of cases found in the calendar year by the hospital census of adult inpatients at risk. For each case, a classification for the type of VTE (PE vs. DVT vs. combinations) was recorded. Cases not receiving adequate prophylaxis were categorized as preventable DVT. Unadjusted RRs were calculated for each year by chi‐square analysis, compared to the baseline (2005) year.

All data were analyzed using Stata (version 10; Stata Corp., College Station, TX). Results for the different analysis were considered significant at P < 0.05.

Retrospective Study of Unintentional Adverse Effects

The increase in anticoagulant use accompanying the introduction of the VTE prophylaxis order set warranted an evaluation of any subsequent rise in related adverse events. A study was done to determine the rates of bleeding and heparin‐induced thrombocytopenia (HIT) before and after the implementation of the VTE prophylaxis order set.

A retrospective analysis was conducted to evaluate outcomes in our inpatients from December 2004 through November 2006, with April to November, 2006 representing the post‐order set implementation time period. Any patient with a discharge diagnosis code of e934.2 (anticoagulant‐related adverse event) was selected for study to identify possible bleeding attributable to pharmacologic VTE prophylaxis. Major or minor bleeding attributable to pharmacologic VTE prophylaxis was defined as a bleed occurring 72 hours after receiving pharmacologic VTE prophylaxis. Major bleeding was defined as cerebrovascular, gastrointestinal, retroperitoneal, or overt bleeding with a decrease in hemoglobin 2 mg/dL with clinical symptoms such as hypotension or hypoxia (not associated with hemodialysis) or transfusion of 2 units of packed red blood cells. Minor bleeding was defined as ecchymosis, epistaxis, hematoma, hematuria, hemoptysis, petechiae, or bleeding without a decrease in hemoglobin 2 g/dL.

Possible cases of HIT were identified by screening for a concomitant secondary thrombocytopenia code (287.4). Chart review was then conducted to determine a causal relationship between the use of pharmacologic VTE prophylaxis and adverse events during the hospital stay. HIT attributable to pharmacologic VTE prophylaxis was determined by assessing if patients developed any of the following clinical criteria after receiving pharmacologic VTE prophylaxis: platelet count <150 109/L or 50% decrease from baseline, with or without an associated venous or arterial thrombosis or other sequelae (skin lesions at injection site, acute systemic reaction) and/or a positive heparin‐induced platelet activation (HIPA) test. In order to receive a diagnosis of HIT, thrombocytopenia must have occurred between days 5 to 15 of heparin therapy, unless existing evidence suggested that the patient developed rapid‐onset HIT or delayed‐onset HIT. Rapid‐onset HIT was defined as an abrupt drop in platelet count upon receiving a heparin product, due to heparin exposure within the previous 100 days. Delayed‐onset HIT was defined as HIT that developed several days after discontinuation of heparin. Other evident causes of thrombocytopenia were ruled out.

Statistical Analysis of Retrospective Study of Unintentional Adverse Effects

Regression analysis with chi‐square and ANOVA were used in the analysis of the demographic data. RRs were calculated for the number of cases coded with an anticoagulant‐related adverse event secondary thrombocytopenia before and after the order set implementation.

Educational Efforts and Feedback

Members of the multidisciplinary team presented information on HA VTE and the VTE prevention protocol at Medical and Surgical grand rounds, teaching rounds, and noon conference, averaging 1 educational session per quarter. Feedback and education was provided to physicians and nursing staff when audits revealed that a patient had inadequate prophylaxis with reference to the protocol standard. In addition, these conversations provided on opportunity to explore reasons for nonadherence with the protocol, confusion regarding the VTE RAM, and other barriers to effective prophylaxis, thereby providing guidance for further protocol revision and educational efforts. We adjusted the order set based on active monitoring of order set use and the audit process.

Results

There were 30,850 adult medical/surgical inpatients admitted to the medical center with a length of stay of 48 hours or more in 2005 to 2007, representing 186,397 patient‐days of observation. A total of 2,924 of these patients were randomly sampled during the VTE prophylaxis audit process (mean 81 audits per month). Table 2 shows the characteristics of randomly sampled audit patients and of the patients diagnosed with HA VTE. The demographics of the 30,850‐inpatient population (mean age = 50 years; 60.7% male; 52% Surgical Services) mirrored the demographics of the randomly sampled inpatients that underwent audits, validating the random sampling methods.

Description of Population Audits and Hospital‐acquired Venous Thromboembolism
 Number (n = 3285)% of Study Population*Cases (n = 361) [n (%)]Audits (n = 2924) [n (%)]OR (95% CI)
  • Abbreviations: CI, confidence interval; OR, odds ratio; SD, standard deviation.

  • Cases and audits.

Age (years) mean SD51 16 (range 15‐100) 53 1750 171.01 (1.003‐1.016)
Gender, males199361213 (59)1782 (61)0.93 (0.744‐1.16)
Major service:     
Surgery171452200 (55)1516 (52) 
Medicine156648161 (45)1408 (48) 
Service, detail     
Hospitalist10413283 (23)958 (33) 
General surgery8312575 (21)756 (26) 
Trauma4191377 (22)342 (12) 
Cardiology3131045 (13)268 (9) 
Orthopedics244715 (4)229 (8) 
Burn unit205629 (8)176 (6) 
Other222730 (8)192 (7) 

The majority of inpatients sampled in the audits were in the moderate VTE risk category (84%), 12% were in the high‐risk category, and 4% were in the low‐risk category. The distribution of VTE risk did not change significantly over this time period.

Interobserver Agreement

The VTE RAM interobserver agreement was assessed on 150 patients with 5 observers as described above. The kappa score for the VTE risk level was 0.81. The kappa score for the judgment of whether the patient was on adequate prophylaxis or not was 0.90.

Impact on Percent of Patients with Adequate Prophylaxis (Longitudinal Audits)

Audits of randomly sampled inpatients occurred longitudinally throughout the study period as described above. Based on the intervention, the percent of patients on adequate prophylaxis improved significantly (P < 0.001) by each calendar year (see Table 3), from a baseline of 58% in 2005 to 78% in 2006 (unadjusted relative benefit = 1.35; 95% confidence interval [CI] = 1.28‐1.43), and 93% in 2007 (unadjusted relative benefit = 1.61; 95% CI = 1.52, 1.69). The improvement seen was more marked in the moderate VTE risk patients when compared to the high VTE risk patients. The percent of audited VTE prophylaxis improved from 53% in calendar year (CY) 2005 to 93% in 2007 (unadjusted relative benefit = 1.75; 95% CI = 1.70‐1.81) in the moderate VTE risk group, while the high VTE risk group improved from 83% to 92% in the same time period (unadjusted relative benefit = 1.11; 95% CI = 0.95‐1.25).

Unadjusted Risk Ratio (Relative Benefit) of Receiving Adequate Venous Thromboembolism Prophylaxis by Year, in Randomly Selected Inpatients
 200520062007
  • Abbreviation: CI, confidence interval.

  • P < 0.001.

All audits1279960679
Prophylaxis adequate, n (%)740 (58)751 (78)631 (93)
Relative benefit (95% CI)11.35* (1.28‐1.43)1.61* (1.52‐1.69)

Overall, adequate VTE prophylaxis was present in over 98% of audited patients in the last 6 months of 2007, and this high rate has been sustained throughout 2008. Age, ethnicity, and gender were not associated with differential rates of adequate VTE prophylaxis.

Figure 1 is a timeline of interventions and the impact on the prevalence of adequate VTE prophylaxis. The first 7 to 8 months represent the baseline rate 50% to 55% of VTE prophylaxis. In this baseline period, the improvement team was meeting, but had not yet begun meeting with the large variety of medical and surgical service leaders. Consensus‐building sessions with these leaders in the latter part of 2005 through mid‐2006 correlated with improvement in adequate VTE prophylaxis rates to near 70%. The consensus‐building sessions also prepared these varied services for a go live date of the modular order set that was incorporated into all admit and transfer order sets, often replacing preexisting orders referring to VTE prevention measures. The order set resulted in an improvement to 80% adequate prophylaxis, with the incremental improvement occurring virtually overnight with the go live date at the onset of quarter 2 (Q2) of 2006. Monitoring of the order set use confirmed that it was easy and efficient to use, but also revealed that physicians were at times classifying patients as low VTE risk inaccurately, when they possessed qualities that actually qualified them for moderate risk status by our protocol. We therefore inserted a secondary CPOE screen when patients were categorized as low VTE risk, asking the physician to deny or confirm that the patient had no risk factors that qualified them for moderate risk status. This confirmation screen essentially acted as a reminder to the physician to ask Are you sure this patient does not need VTE prophylaxis? This minor modification of the CPOE order set improved adequate VTE prophylaxis rates to 90%. Finally, we asked nurses to evaluate patients who were not on therapeutic or prophylactic doses of anticoagulants. Patients with VTE risk factors but no obvious contraindications generated a note from the nurse to the doctor, prompting the doctor to reassess VTE risk and potential contraindications. This simple intervention raised the percent of audited patients on adequate VTE prophylaxis to 98% in the last 6 months of 2007.

Figure 1
Percent of randomly sampled inpatients with adequate VTE prophylaxis; 2,924 randomly sampled adult inpatients (mean 81 patients per month) audited for adequacy of VTE prophylaxis regimen on the day of audit. Improvement is correlated with incremental interventions on the statistical process control chart. Control limits determined using a p‐chart macro in Microsoft Excel with a P value of 0.01. VTE = venous thromboembolism; Q = quarter; ID = identification.

Description of Prospectively Identified VTE

We identified 748 cases of VTE among patients admitted to the medical center over the 36‐month study period; 387 (52%) were community‐acquired VTE. There were 361 HA cases (48% of total cases) over the same time period. There was no difference in age, gender, or BMI between the community‐acquired and hospital‐related VTE.

Of the 361 HA cases, 199 (55%) occurred on Surgical Services and 162 (45%) occurred on Medical Services; 58 (16%) unique patients had pulmonary emboli, while 303 (84%) patients experienced only DVT. Remarkably, almost one‐third of the DVT occurred in the upper extremities (108 upper extremities, 240 lower extremities), and most (80%) of the upper‐extremity DVT were associated with central venous catheters.

Of 361 HA VTE cases, 292 (81%) occurred in those in the moderate VTE risk category, 69 HA VTE cases occurred in high‐risk category patients (19%), and no VTE occurred in patients in the low‐risk category.

Improvement in HA VTE

HA VTE were identified and each case analyzed on an ongoing basis over the entire 3 year study period, as described above. Table 4 depicts a comparison of HA VTE on a year‐to‐year basis and the impact of the VTE prevention protocol on the incidence of HA VTE. In 2007 (the first full CY after the implementation of the order set) there was a 39% relative risk reduction (RRR) in the risk of experiencing an HA VTE. The reduction in the risk of preventable HA VTE was even more marked (RRR = 86%; 7 preventable VTE in 2007, compared to 44 in baseline year of 2005; RR = 0.14; 95% CI = 0.06‐0.31).

HA VTE Characteristics and Positive Impact of VTE Prevention Protocol, Demonstrating Significant Risk Reduction for Cases of HA VTE, HA DVT, and Preventable VTE from 2005 to 2007
 HA VTE by Year
 200520062007
  • Abbreviations: CI, confidence interval; DVT, deep vein thrombosis; HA, hospital‐acquired; PE, pulmonary embolus; VTE, venous thromboembolism.

  • P < 0.001.

  • P < 0.01.

Patients at Risk9720992311,207
Cases with any HA VTE13113892
Risk for HA VTE1 in 761 in 731 in 122
Unadjusted relative risk (95% CI)1.01.03 (0.81‐1.31)0.61* (0.47‐0.79)
Cases with PE212215
Risk for PE1 in 4631 in 4511 in 747
Unadjusted relative risk (95% CI)1.01.03 (0.56‐1.86)0.62 (0.32‐1.20)
Cases with DVT (and no PE)11011677
Risk for DVT1 in 881 in 851 in 146
Unadjusted relative risk (95% CI)1.01.03 (0.80‐1.33)0.61* (0.45‐0.81)
Cases with preventable VTE44217
Risk for preventable VTE1 in 2211 in 4731 in 1601
Unadjusted relative risk (95% CI)1.00.47 (0.28‐0.79)0.14* (0.06‐0.31)

Retrospective Analysis of Impact on HIT and Bleeding

There were no statistically significant differences in the number of cases coded for an anticoagulant‐related bleed or secondary thrombocytopenia (Table 5). Chart review revealed there were 2 cases of minor bleeding attributable to pharmacologic VTE prophylaxis before the order set implementation. There were no cases after implementation. No cases of HIT attributable to pharmacologic VTE prophylaxis were identified in either study period, with all cases being attributed to therapeutic anticoagulation.

Pre/Post‐orderset Anticoagulation Related Adverse Events
 Pre‐order SetPost‐order SetPost‐order Set RR (CI)
  • Abbreviations: RR, relative risk; CI, 95% confidence interval; HIT, Heparin induced Thrombocytopenia

Bleeding events74280.70 (0.46‐1.08)
Due to prophylaxis2 (minor)0 
HIT events971.44 (0.54‐3.85)
Due to prophylaxis00 
Patient admissions3211717294 

Discussion

We demonstrated that implementation of a standardized VTE prevention protocol and order set could result in a dramatic and sustained increase in adequate VTE prophylaxis across an entire adult inpatient population. This achievement is more remarkable given the rigorous criteria defining adequate prophylaxis. Mechanical compression devices were not accepted as primary prophylaxis in moderate‐risk or high‐risk patients unless there was a documented contraindication to pharmacologic prophylaxis, and high VTE risk patients required both mechanical and pharmacologic prophylaxis to be considered adequately protected, for example. The relegation of mechanical prophylaxis to an ancillary role was endorsed by our direct observations, in that we were only able to verify that ordered mechanical prophylaxis was in place 60% of the time.

The passive dissemination of guidelines is ineffective in securing VTE prophylaxis.19 Improvement in VTE prophylaxis has been suboptimal when options for VTE prophylaxis are offered without providing guidance for VTE risk stratification and all options (pharmacologic, mechanical, or no prophylaxis) are presented as equally acceptable choices.20, 21 Our multifaceted strategy using multiple interventions is an approach endorsed by a recent systematic review19 and others in the literature.22, 23 The interventions we enacted included a method to prompt clinicians to assess patients for VTE risk, and then to assist in the selection of appropriate prophylaxis from standardized options. Decision support and clinical reminders have been shown to be more effective when integrated into the workflow19, 24; therefore, a key strategy of our study involved embedding the VTE risk assessment tool and guidance toward appropriate prophylactic regimens into commonly used admission/transfer order sets. We addressed the barriers of physician unfamiliarity or disagreement with guidelines10 with education and consensus‐building sessions with clinical leadership. Clinical feedback from audits, peer review, and nursing‐led interventions rounded out the layered multifaceted interventional approach.

We designed and prospectively validated a VTE RAM during the course of our improvement efforts, and to our knowledge our simple 3‐category (or 3‐level) VTE risk assessment model is the only validated model. The VTE risk assessment/prevention protocol was validated by several important parameters. First, it proved to be practical and easy to use, taking only seconds to complete, and it was readily adopted by all adult medical and surgical services. Second, the VTE RAM demonstrated excellent interobserver agreement for VTE risk level and decisions about adequacy of VTE prophylaxis with 5 physician reviewers. Third, the VTE RAM predicted risk for VTE. All patients suffering from HA VTE were in the moderate‐risk to high‐risk categories, and HA VTE occurred disproportionately in those meeting criteria for high risk. Fourth, implementation of the VTE RAM/protocol resulted in very high, sustained levels of VTE prophylaxis without any detectable safety concerns. Finally and perhaps most importantly, high rates of adherence to the VTE protocol resulted in a 40% decline in the incidence of HA VTE in our institution.

The improved prevalence of adequate VTE prophylaxis reduced, but did not eliminate, HA VTE. The reduction observed is consistent with the 40% to 50% efficacy of prophylaxis reported in the literature.7 Our experience highlights the recent controversy over proposals by the Centers for Medicare & Medicaid Services (CMS) to add HA VTE to the list of do not pay conditions later this year,25 as it is clear from our data that even near‐perfect adherence with accepted VTE prevention measures will not eliminate HA VTE. After vigorous pushback about the fairness of this measure, the HA VTE do not pay scope was narrowed to include only certain major orthopedic procedure patients.

Services with a preponderance of moderate‐risk patients had the largest reduction in HA VTE. Efforts that are focused only on high‐risk orthopedic, trauma, and critical care patients will miss the larger opportunities for maximal reduction in HA VTE for multiple reasons. First, moderate VTE risk patients are far more prevalent than high VTE risk patients (84% vs. 12% of inpatients at our institution). Second, high‐risk patients are already at a baseline relatively high rate of VTE prophylaxis compared to their moderate VTE risk counterparts (83% vs. 53% at our institution). Third, a large portion of patients at high risk for VTE (such as trauma patients) also have the largest prevalence of absolute or relative contraindications to pharmacologic prophylaxis, limiting the effect size of prevention efforts.

Major strengths of this study included ongoing rigorous concurrent measurement of both processes (percent of patients on adequate prophylaxis) and outcomes (HA VTE diagnosed via imaging studies) over a prolonged time period. The robust random sampling of inpatients insured that changes in VTE prophylaxis rates were not due to changes in the distribution of VTE risk or bias potentially introduced from convenience samples. The longitudinal monitoring of imaging study results for VTE cases is vastly superior to using administrative data that is reliant on coding. The recent University Healthsystem Consortium (UHC) benchmarking data on venous thromboembolism were sobering but instructive.26 UHC used administrative discharge codes for VTE in a secondary position to identify patients with HA VTE, which is a common strategy to follow the incidence of HA VTE. The accuracy of identifying surgical patients with an HA VTE was only 60%. Proper use of the present on admission (POA) designation would have improved this to 83%, but 17% of cases either did not occur or had history only with a labor‐intensive manual chart review. Performance was even worse in medical patients, with only a 30% accuracy rate, potentially improved to 79% if accurate POA designation had been used, and 21% of cases identified by administrative methods either did not occur or had history only. In essence, unless an improvement team uses chart review of each case potentially identified as a HA VTE case, the administrative data are not reliable. Concurrent discovery of VTE cases allows for a more accurate and timely chart review, and allows for near real‐time feedback to the responsible treatment team.

The major limitation of this study is inherent in the observational design and the lack of a control population. Other factors besides our VTE‐specific improvement efforts could affect process and outcomes, and reductions in HA VTE could conceivably occur because of changes in the make‐up of the admitted inpatient population. These limitations are mitigated to some degree by several observations. The VTE risk distribution in the randomly sampled inpatient population did not vary significantly from year to year. The number of HA VTE was reduced in 2007 even though the number of patients and patient days at risk for developing VTE went up. The incidence of community‐acquired VTE remained constant over the same time period, highlighting the consistency of our measurement techniques and the VTE risk in the community we serve. Last, the improvements in VTE prophylaxis rates increased at times that correlated well with the introduction of layered interventions, as depicted in Figure 1.

There were several limitations to the internal study on adverse effects of VTE protocol implementation. First, this was a retrospective study, so much of the data collection was dependent upon physician progress notes and discharge summaries. Lack of documentation could have precluded the appropriate diagnosis codes from being assigned. Next, the study population was generated from coding data, so subjectivity could have been introduced during the coding process. Also, a majority of the patients did not fit the study criteria due to discharge with the e934.2 code, because they were found to have an elevated international normalized ratio (INR) after being admitted on warfarin. Finally, chart‐reviewer bias could have affected the results, as the chart reviewer became more proficient at reviewing charts over time. Despite these limitations, the study methodology allowed for screening of a large population for rare events. Bleeding may be a frequent concern with primary thromboprophylaxis, but data from clinical trials and this study help to demonstrate that rates of adverse events from pharmacologic VTE prophylaxis are very rare.

Another potential limitation is raised by the question of whether our methods can be generalized to other sites. Our site is an academic medical center and we have CPOE, which is present in only a small minority of centers. Furthermore, one could question how feasible it is to get institution‐wide consensus for a VTE prevention protocol in settings with heterogenous medical staffs. To address these issues, we used a proven performance improvement framework calling for administrative support, a multidisciplinary improvement team, reliable measures, and a multifaceted approach to interventions. This framework and our experiences have been incorporated into improvement guides27, 28 that have been the centerpiece of the Society of Hospital Medicine VTE Prevention Collaborative improvement efforts in a wide variety of medical environments. The collaborative leadership has observed that success is the rule when this model is followed, in institutions large and small, academic or community, and in both paper and CPOE environments. Not all of these sites use a VTE RAM identical to ours, and there are local nuances to preferred choices of prophylaxis. However, they all incorporated simple VTE risk stratification with only a few levels of risk. Reinforcing the expectation that pharmacologic prophylaxis is indicated for the majority of inpatients is likely more important than the nuances of choices for each risk level.

We demonstrated that dramatic improvement in VTE prophylaxis is achievable, safe, and effective in reducing the incidence of HA VTE. We used scalable, portable methods to make a large and convincing impact on the incidence of HA VTE, while also developing and prospectively validating a VTE RAM. A wide variety of institutions are achieving significant improvement using similar strategies. Future research and improvement efforts should focus on how to accelerate integration of this model across networks of hospitals, leveraging networks with common order sets or information systems. Widespread success in improving VTE prophylaxis would likely have a far‐reaching benefit on morbidity and PE‐related mortality.

References
  1. U.S. Department of Health and Human Services. Surgeon General's Call to Action to Prevent Deep Vein Thrombosis and Pulmonary Embolism.2008 Clean-up Rule No. CU01 invoked here. . Available at: http://www.surgeongeneral.gov/topics/deepvein. Accessed June 2009.
  2. Heit JA,Melton LJ,Lohse CM, et al.Incidence of venous thromboembolism in hospitalized patients vs. community residents.Mayo Clin Proc.2001;76:11021110.
  3. Dentali F,Douketis JD,Gianni M,Lim W,Crowther MA.Meta‐analysis: anticoagulant prophylaxis to prevent symptomatic venous thromboembolism in hospitalized medical patients.Ann Intern Med.2007;146(4):278288.
  4. Heit JA,O'Fallon WM,Petterson TM, et al.Relative impact of risk factors for deep vein thrombosis and pulmonary embolism.Arch Intern Med.2002;162:12451248.
  5. Tapson VF,Hyers TM,Waldo AL, et al.Antithrombotic therapy practices in US hospitals in an era of practice guidelines.Arch Intern Med.2005;165:14581464.
  6. Clagett GP,Anderson FA,Heit JA, et al.Prevention of venous thromboembolism.Chest.1995;108:312334.
  7. Geerts WH,Bergqvist D,Pineo GF, et al.Prevention of venous thromboembolism: ACCP Evidence‐Based Clinical Practice Guidelines (8th Edition).Chest.2008;133(6 Suppl):381S453S.
  8. Goldhaber SZ,Tapson VF.A prospective registry of 5,451 patients with ultrasound‐confirmed deep vein thrombosis.Am J Cardiol.2004;93:259262.
  9. Monreal M,Kakkar A,Caprini J, et al.The outcome after treatment of venous thromboembolism is different in surgical and acutely ill medical patients. Findings from the RIETE registry.J Thromb Haemost.2004;2:18921898.
  10. Tapson V,Decousus H,Pini M, et al.Venous thromboembolism prophylaxis in acutely ill hospitalized medical patients: findings from the international medical prevention registry on venous thromboembolism.Chest.2007;132(3):936945.
  11. Kahn SR,Panju A,Geerts W, et al.Multicenter evaluation of the use of venous thromboembolism prophylaxis in acutely ill medical patients in Canada.Thromb Res.2007;119(2):145155.
  12. Cohen AT,Tapson VF,Bergmann JF, et al.Venous thromboembolism risk and prophylaxis in the acute hospital care setting (ENDORSE study): a multinational cross‐sectional study.Lancet.2008;371(9610):387394.
  13. Kakkar AK,Davidson BL,Haas SK.Compliance with recommended prophylaxis for venous thromboembolism: improving the use and rate of uptake of clinical practice guidelines.J Thromb Haemost.2004;2:221227.
  14. Anderson F,Spencer F.Risk factors for venous thromboembolism.Circulation.2003;107:I‐9I‐16.
  15. Caprini J,Arcelus J,Reyna J.Effective risk stratification of surgical and nonsurgical patients for venous thromboembolic disease.Semin Hematol.2001;38(2 suppl 5):1219.
  16. Gensini GF,Prisco D,Falciani M,Comeglio M,Colella A.Identification of candidates for prevention of venous thromboembolism.Semin Thromb Hemost.1997;23(1):5567.
  17. Haas S.Venous thromboembolic risk and its prevention in hospitalized medical patients.Semin Thromb Hemost.2002;28(6);577583.
  18. Motykie G,Zebala L,Caprini J, et al.A guide to venous thromboembolism risk factor assessment.J Thromb Thrombolysis.2000;9:253262.
  19. Tooher R,Middleton P,Pham C, et al.A systematic review of strategies to improve prophylaxis for venous thromboembolism in hospitals.Ann Surg.2005;241:397415.
  20. O'Connor C,Adhikari N,DeCaire K,Friedrich J.Medical admission order sets to improve deep vein thrombosis prophylaxis rates and other outcomes.J Hosp Med.2009;4(2):8189.
  21. Maynard G.Medical admission order sets to improve deep vein thrombosis prevention: a model for others or a prescription for mediocrity? [Editorial].J Hosp Med.2009;4(2):7780.
  22. Oxman AD,Thomson MA,Davis DA,Haynes RB.No magic bullets: a systematic review of 102 trials of interventions to improve professional practice.CMAJ.1995;153:14231431.
  23. Bullock‐Palmer RP,Weiss S,Hyman C.Innovative approaches to increase deep vein thrombosis prophylaxis rate resulting in a decrease in hospital‐acquired deep vein thrombosis at a tertiary‐care teaching hospital.J Hosp Med.2008;3(2):148155.
  24. Shojania KG,McDonald KM,Wachter RM,Owens DK.Closing the Quality Gap: A Critical Analysis of Quality Improvement Strategies.Rockville, MD:Agency for Healthcare Research and Quality;2004.
  25. CMS Office of Public Affairs. Fact Sheet: CMS Proposes Additions to List of Hospital‐Acquired Conditions for Fiscal Year 2009. Available at: http://www.cms.hhs.gov/apps/media/press/factsheet.asp?Counter=3042. Accessed June2009.
  26. The DVT/PE 2007 Knowledge Transfer Meeting. Proceedings of November 30, 2007 meeting. Available at: http://www.uhc.edu/21801.htm. Accessed June2009.
  27. Maynard G,Stein J. Preventing Hospital‐Acquired Venous Thromboembolism. A Guide for Effective Quality Improvement. Society of Hospital Medicine, VTE Quality Improvement Resource Room. Available at: http://www.hospitalmedicine.org/ResourceRoomRedesign/RR_VTE/VTE_Home.cfm. Accessed June 2009.
  28. Maynard G,Stein J.Preventing Hospital‐Acquired Venous Thromboembolism: A Guide for Effective Quality Improvement. Prepared by the Society of Hospital Medicine. AHRQ Publication No. 08–0075.Rockville, MD:Agency for Healthcare Research and Quality. September2008. Available at: http://www.ahrq.gov/qual/vtguide. Accessed June 2009.
References
  1. U.S. Department of Health and Human Services. Surgeon General's Call to Action to Prevent Deep Vein Thrombosis and Pulmonary Embolism.2008 Clean-up Rule No. CU01 invoked here. . Available at: http://www.surgeongeneral.gov/topics/deepvein. Accessed June 2009.
  2. Heit JA,Melton LJ,Lohse CM, et al.Incidence of venous thromboembolism in hospitalized patients vs. community residents.Mayo Clin Proc.2001;76:11021110.
  3. Dentali F,Douketis JD,Gianni M,Lim W,Crowther MA.Meta‐analysis: anticoagulant prophylaxis to prevent symptomatic venous thromboembolism in hospitalized medical patients.Ann Intern Med.2007;146(4):278288.
  4. Heit JA,O'Fallon WM,Petterson TM, et al.Relative impact of risk factors for deep vein thrombosis and pulmonary embolism.Arch Intern Med.2002;162:12451248.
  5. Tapson VF,Hyers TM,Waldo AL, et al.Antithrombotic therapy practices in US hospitals in an era of practice guidelines.Arch Intern Med.2005;165:14581464.
  6. Clagett GP,Anderson FA,Heit JA, et al.Prevention of venous thromboembolism.Chest.1995;108:312334.
  7. Geerts WH,Bergqvist D,Pineo GF, et al.Prevention of venous thromboembolism: ACCP Evidence‐Based Clinical Practice Guidelines (8th Edition).Chest.2008;133(6 Suppl):381S453S.
  8. Goldhaber SZ,Tapson VF.A prospective registry of 5,451 patients with ultrasound‐confirmed deep vein thrombosis.Am J Cardiol.2004;93:259262.
  9. Monreal M,Kakkar A,Caprini J, et al.The outcome after treatment of venous thromboembolism is different in surgical and acutely ill medical patients. Findings from the RIETE registry.J Thromb Haemost.2004;2:18921898.
  10. Tapson V,Decousus H,Pini M, et al.Venous thromboembolism prophylaxis in acutely ill hospitalized medical patients: findings from the international medical prevention registry on venous thromboembolism.Chest.2007;132(3):936945.
  11. Kahn SR,Panju A,Geerts W, et al.Multicenter evaluation of the use of venous thromboembolism prophylaxis in acutely ill medical patients in Canada.Thromb Res.2007;119(2):145155.
  12. Cohen AT,Tapson VF,Bergmann JF, et al.Venous thromboembolism risk and prophylaxis in the acute hospital care setting (ENDORSE study): a multinational cross‐sectional study.Lancet.2008;371(9610):387394.
  13. Kakkar AK,Davidson BL,Haas SK.Compliance with recommended prophylaxis for venous thromboembolism: improving the use and rate of uptake of clinical practice guidelines.J Thromb Haemost.2004;2:221227.
  14. Anderson F,Spencer F.Risk factors for venous thromboembolism.Circulation.2003;107:I‐9I‐16.
  15. Caprini J,Arcelus J,Reyna J.Effective risk stratification of surgical and nonsurgical patients for venous thromboembolic disease.Semin Hematol.2001;38(2 suppl 5):1219.
  16. Gensini GF,Prisco D,Falciani M,Comeglio M,Colella A.Identification of candidates for prevention of venous thromboembolism.Semin Thromb Hemost.1997;23(1):5567.
  17. Haas S.Venous thromboembolic risk and its prevention in hospitalized medical patients.Semin Thromb Hemost.2002;28(6);577583.
  18. Motykie G,Zebala L,Caprini J, et al.A guide to venous thromboembolism risk factor assessment.J Thromb Thrombolysis.2000;9:253262.
  19. Tooher R,Middleton P,Pham C, et al.A systematic review of strategies to improve prophylaxis for venous thromboembolism in hospitals.Ann Surg.2005;241:397415.
  20. O'Connor C,Adhikari N,DeCaire K,Friedrich J.Medical admission order sets to improve deep vein thrombosis prophylaxis rates and other outcomes.J Hosp Med.2009;4(2):8189.
  21. Maynard G.Medical admission order sets to improve deep vein thrombosis prevention: a model for others or a prescription for mediocrity? [Editorial].J Hosp Med.2009;4(2):7780.
  22. Oxman AD,Thomson MA,Davis DA,Haynes RB.No magic bullets: a systematic review of 102 trials of interventions to improve professional practice.CMAJ.1995;153:14231431.
  23. Bullock‐Palmer RP,Weiss S,Hyman C.Innovative approaches to increase deep vein thrombosis prophylaxis rate resulting in a decrease in hospital‐acquired deep vein thrombosis at a tertiary‐care teaching hospital.J Hosp Med.2008;3(2):148155.
  24. Shojania KG,McDonald KM,Wachter RM,Owens DK.Closing the Quality Gap: A Critical Analysis of Quality Improvement Strategies.Rockville, MD:Agency for Healthcare Research and Quality;2004.
  25. CMS Office of Public Affairs. Fact Sheet: CMS Proposes Additions to List of Hospital‐Acquired Conditions for Fiscal Year 2009. Available at: http://www.cms.hhs.gov/apps/media/press/factsheet.asp?Counter=3042. Accessed June2009.
  26. The DVT/PE 2007 Knowledge Transfer Meeting. Proceedings of November 30, 2007 meeting. Available at: http://www.uhc.edu/21801.htm. Accessed June2009.
  27. Maynard G,Stein J. Preventing Hospital‐Acquired Venous Thromboembolism. A Guide for Effective Quality Improvement. Society of Hospital Medicine, VTE Quality Improvement Resource Room. Available at: http://www.hospitalmedicine.org/ResourceRoomRedesign/RR_VTE/VTE_Home.cfm. Accessed June 2009.
  28. Maynard G,Stein J.Preventing Hospital‐Acquired Venous Thromboembolism: A Guide for Effective Quality Improvement. Prepared by the Society of Hospital Medicine. AHRQ Publication No. 08–0075.Rockville, MD:Agency for Healthcare Research and Quality. September2008. Available at: http://www.ahrq.gov/qual/vtguide. Accessed June 2009.
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Optimizing prevention of hospital‐acquired venous thromboembolism (VTE): Prospective validation of a VTE risk assessment model
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Optimizing prevention of hospital‐acquired venous thromboembolism (VTE): Prospective validation of a VTE risk assessment model
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adhesence, care standerdization, computerized physician orders entry, deep vein thrombosis prophylaxis, preventive services, quality, improvement, venous, thromboembolism
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Clinical Professor of Medicine and Chief, Division of Hospital Medicine, University of California, San Diego Medical Center, 200 West Arbor Drive #8485, San Diego, CA 92103
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Improved Glycemic Control and Hypoglycemia

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Improved inpatient use of basal insulin, reduced hypoglycemia, and improved glycemic control: Effect of structured subcutaneous insulin orders and an insulin management algorithm

Diabetes has reached epidemic proportions in the United States, affecting over 20 million individuals,1 and further rises are expected. A disproportionate increase in diabetes has occurred in the inpatient setting.2 Furthermore, for every 2 patients in the hospital with known diabetes, there may be an additional 1 with newly observed hyperglycemia. Both are common. In 1 report, for example, 24% of inpatients with hyperglycemia had a prior diagnosis of diabetes, whereas another 12% had hyperglycemia without a prior diagnosis of diabetes.3

Although there is a paucity of high quality randomized controlled trials to support tight glycemic control in non‐critical care inpatient settings, poor glycemic control in hospitalized patients is strongly associated with undesirable outcomes for a variety of conditions, including pneumonia,4 cancer chemotherapy,5 renal transplant,6 and postsurgical wound infections.7, 8 Hyperglycemia also induces dehydration, fluid and electrolyte imbalance, gastric motility problems, and venous thromboembolism formation.9

Structured subcutaneous insulin order sets and insulin management protocols have been widely advocated as a method to encourage basal bolus insulin regimens and enhance glycemic control,2, 9, 10 but the effect of these interventions on glycemic control, hypoglycemia, and insulin use patterns in the real world setting has not been well reported. Fear of inducing hypoglycemia is often the main barrier for initiating basal insulin containing regimens and pursuing glycemic targets.2 The evidence would suggest, however, that sliding scale regimens, as opposed to more physiologic basal bolus regimens, may actually increase both hypoglycemic and hyperglycemic excursions.11 A convincing demonstration of the efficacy (improved insulin use patterns and reduced hyperglycemia) and safety (reduced hypoglycemia) of structured insulin order sets and insulin management protocols would foster a more rapid adoption of these strategies.

PATIENTS AND METHODS

In our 400‐bed university hospital, we formed a hospitalist‐led multidisciplinary team in early 2003, with the focus of improving the care delivered to non‐critical care patients with diabetes or hyperglycemia. We used a Plan‐Do‐Study‐Act (PDSA) performance improvement framework, and conducted institutional review board (IRB)‐approved prospective observational research in parallel with the performance improvement efforts, with a waiver for individual informed consent. The study population consisted of all adult inpatients on non‐critical care units with electronically reported point of care (POC) glucose testing from November 2002 through December 2005. We excluded patients who did not have either a discharge diagnosis of Diabetes (ICD 9 codes 250‐251.XX) or demonstrated hyperglycemia (fasting POC glucose >130 mg/dL 2, or a random value of >180 mg/dL) from analysis of glycemic control and hypoglycemia. Women admitted to Obstetrics were excluded. Monthly and quarterly summaries on glycemic control, hypoglycemia, and insulin use patterns (metrics described below) were reported to the improvement team and other groups on a regular basis throughout the intervention period. POC glucose data, demographics, markers of severity of illness, and diagnosis codes were retrieved from the electronic health record.

Interventions

We introduced several interventions and educational efforts throughout the course of our improvement. The 2 key interventions were as follows:

  • Structured subcutaneous insulin order sets (November, 2003).

  • An insulin management algorithm, described below (May 2005).

 

Key Intervention #1: Structured Subcutaneous Insulin Order Set Implementation

In November 2003, we introduced a paper‐based structured subcutaneous insulin order set. This order set encouraged the use of scheduled basal and nutritional insulin, provided guidance for monitoring glucose levels, and for insulin dosing. A hypoglycemia protocol and a standardized correction insulin table were embedded in the order set. This set was similar to examples of structured insulin ordering subsequently presented in the literature.9 In a parallel effort, the University of California, San Diego Medical Center (UCSDMC) was developing a computer physician order entry (CPOE) module for our comprehensive clinical information system, Invision (Siemens Medical Systems, Malvern, PA), that heretofore had primarily focused on result review, patient schedule management, and nursing documentation. In anticipation of CPOE and for the purpose of standardization, we removed outdated sliding scale insulin regimens from a variety preexisting order sets and inserted references to the standardized subcutaneous insulin order set in their stead. The medication administration record (MAR) was changed to reflect the basal/nutritional/correction insulin terminology. It became more difficult to order a stand‐alone insulin sliding scale even before CPOE versions became available. The standardized order set was the only preprinted correction scale insulin order available, and ordering physicians have to specifically opt out of basal and nutritional insulin choices to order sliding scale only regimens. Verbal orders for correction dose scales were deemed unacceptable by medical staff committees. Correctional insulin doses could be ordered as a 1‐time order, but the pharmacy rejected ongoing insulin orders that were not entered on the structured form.

We introduced our first standardized CPOE subcutaneous insulin order set in January 2004 at the smaller of our 2 campuses, and subsequently completed full deployment across both campuses in all adult medical‐surgical care areas by September 2004.

The CPOE version, like the paper version that immediately preceded it, encouraged the use of basal/bolus insulin regimens, promoted the terms basal, nutritional or premeal, and adjustment dose insulin in the order sets and the medication administration record, and was mandatory for providers wishing to order anything but a 1‐time order of insulin. Figure 1 depicts a screen shot of the CPOE version. Similar to the paper version, the ordering physician had to specifically opt out of ordering scheduled premeal and basal insulin to order a sliding scale only regimen. The first screen also ensured that appropriate POC glucose monitoring was ordered and endorsed a standing hypoglycemia protocol order. The CPOE version had only a few additional features not possible on paper. Obvious benefits included elimination of unapproved abbreviations and handwriting errors. Nutritional and correction insulin types were forced to be identical. Fundamentally, however, both the paper and online structured ordering experiences had the same degree of control over provider ordering patterns, and there was no increment in guidance for choosing insulin regimens, hence their combined analysis as structured orders.

Figure 1
Screen shot: Computerized physician order entry version of structured insulin orders.

Key Intervention #2: Insulin Management Algorithm

The structured insulin order set had many advantages, but also had many limitations. Guidance for preferred insulin regimens for patients in different nutritional situations was not inherent in the order set, and all basal and nutritional insulin options were offered as equally acceptable choices. The order set gave very general guidance for insulin dosing, but did not calculate insulin doses or assist in the apportionment of insulin between basal and nutritional components, and guidance for setting a glycemic target or adjusting insulin was lacking.

Recognizing these limitations, we devised an insulin management algorithm to provide guidance incremental to that offered in the order set. In April 2005, 3 hospitalists piloted a paper‐based insulin management algorithm (Figure 2, front; Figure 3, reverse) on their teaching services. This 1‐page algorithm provided guidance on insulin dosing and monitoring, and provided institutionally preferred insulin regimens for patients in different nutritional situations. As an example, of the several acceptable subcutaneous insulin regimens that an eating patient might use in the inpatient setting, we advocated the use of 1 preferred regimen (a relatively peakless, long‐acting basal insulin once a day, along with a rapid acting analog nutritional insulin with each meal). We introduced the concept of a ward glycemic target, provided prompts for diabetes education, and generally recommended discontinuation of oral hypoglycemic agents in the inpatient setting. The hospitalists were introduced to the concepts and the algorithm via 1 of the authors (G.M.) in a 1‐hour session. The algorithm was introduced on each teaching team during routine teaching rounds with a slide set (approximately 15 slides) that outlined the basic principles of insulin dosing, and gave example cases which modeled the proper use of the algorithm. The principles were reinforced on daily patient work rounds as they were applied on inpatients with hyperglycemia. The pilot results on 25 patients, compared to 250 historical control patients, were very promising, with markedly improved glycemic control and no increase in hypoglycemia. We therefore sought to spread the use of the algorithm. In May 2005 the insulin management algorithm and teaching slide set were promoted on all 7 hospitalist‐run services, and the results of the pilot and concepts of the algorithm were shared with a variety of house staff and service leaders in approximately a dozen sessions: educational grand rounds, assorted noon lectures, and subsequently, at new intern orientations. Easy access to the algorithm was assured by providing a link to the file within the CPOE insulin order set.

Figure 2
Insulin management algorithm (front) introduced at UCSD in May 2005 (marking the onset of Time Period 3).
Figure 3
Insulin management algorithm (reverse) introduced at UCSD in May 2005 (marking the onset of Time Period 3).

Other Attempts to Improve Care

Several other issues were addressed in the context of the larger performance improvement effort by the team. In many cases, hard data were not gathered to assess the effectiveness of the interventions, or the interventions were ongoing and could be considered the background milieu for the key interventions listed above.

During each intervention, education sessions were given throughout the hospital to staff, including physicians, residents, and nurses, using departmental grand rounds, nursing rounds, and in‐services to describe the process and goals. Patient education programs were also redesigned and implemented, using preprinted brochure. Front‐line nursing staff teaching skills were bolstered via Clinical Nurse Specialist educational sessions, and the use of a template for patient teaching. The educational template assessed patient readiness to learn, home environment, current knowledge, and other factors. Approximately 6 conferences directed at various physician staff per year became part of the regular curriculum.

We recognized that there was often poor coordination between glucose monitoring, nutrition delivery, and insulin administration. The traditional nursing practice of the 6:00 AM finger stick and insulin administration was changed to match a formalized nutrition delivery schedule. Nutrition services and nursing were engaged to address timeliness of nutrition delivery, insulin administration, and POC glucose documentation in the electronic health record.

Feedback to individual medicine resident teams on reaching glycemic targets, with movie ticket/coffee coupon rewards to high performing teams, was tried from April 2004 to September 2004.

Measures and Analyses

Assessing Insulin Use Patterns

A convenience sample gathering all subcutaneous insulin orders from 4 to 5 selected days per month yielded 70 to 90 subcutaneous insulin orders for review each month. Sampling was originally performed each month, followed by less frequent sampling once stability in insulin use patterns was reached. Regimens were categorized by pharmacy and hospitalist review as to whether basal insulin was part of the insulin regimen or not. The percentage of insulin regimens incorporating basal insulin was calculated for each sampled month and followed in run charts, and comparisons between preorder set and postorder set time periods were made using Pearson's chi square statistic.

Assessing Glycemic Control

Glycemic control and hypoglycemia parameters were monitored for the entire 38‐month observation period.

Routinely monitored POC glucose values were used to assess glycemic control. During the initial data examination, it was found after 14 days of the hospital stay, there was a notable stabilization and improvement in glucose control and fewer hypoglycemic events, therefore we examined only the first 14 days of hospitalization, thereby eliminating a potential source of bias from length of stay outliers.

A mean glucose value was recorded for each patient‐day with 1 or more recorded values. Glycemic control for each patient‐stay was calculated by averaging the patient‐day mean values, which we will refer to as the day‐weighted mean. Hypoglycemic values (60 mg/dL) were excluded from calculation of the mean glucose, to avoid equating frequent hypoglycemia with optimal glycemic control. An uncontrolled patient‐day was defined as a monitored patient‐day with a mean glucose 180 mg/dL. An uncontrolled patient‐stay is defined as a patient‐stay with a day‐weighted mean glucose value 180 mg/dL.

We theorized that the greatest impact of the interventions would be realized in patients with longer monitoring periods, and that those with only a few POC glucose values could potentially misrepresent the impact of our interventions: therefore we performed a second analysis restricted to patients with 8 POC glucose values.

Assessing Hypoglycemia

Hypoglycemia was defined as a glucose 60 mg/dL, and severe hypoglycemia was defined as a glucose 40 mg/dL. These parameters were characterized by 2 methods. First, we calculated the percentage of monitored patients suffering from 1 or more hypoglycemic events or severe hypoglycemic events over the course of their entire admission. A second method tracked the percentage of monitored patient‐days with hypoglycemia and severe hypoglycemia, thereby correcting for potential misinterpretation from clustered repeated measures or variable length of stay. As with the glycemic control analysis, we repeated the hypoglycemia analysis in the subset of patients with 8 POC glucose values.

Summary Analysis of Glycemic Control and Hypoglycemia

Pearson chi square values, with relative risks (RRs) and 95% confidence intervals (CIs) were calculated to compare glycemic control and hypoglycemia in the 2 key interventions and baseline. The interventions and data reporting were grouped as follows:

  • Baseline: November 2002 to October 2003) = Time Period 1 (TP1)

  • Structured Order Set: November 2003 to April 2005) = Time Period 2 (TP2)

  • Algorithm plus Structured Order Set: May 2005 to December 2005) = Time Period 3 (TP3)

 

A P value of less than 0.05 was determined as significant and data were analyzed using STATA, Version 8 (STATA Corp., College Station, TX).

We assigned the RR of uncontrolled hyperglycemia and the RR of hypoglycemia during the baseline time (TP1) with values of 1.0, and calculated the RR and CIs for the same parameters during TP2 and TP3.

RESULTS

Just over 11,000 patients were identified for POC glucose testing over the 38 month observation period. Of these, 9314 patients had either a diagnosis of diabetes or documented hyperglycemia. The characteristics of this study population are depicted in Table 1. There were no differences between the groups and the demographics of age, gender, or length of stay (P > 0.05 for all parameters). There was a slight increase in the percent of patients with any intensive care unit days over the 3 time periods and a similar increase in the case mix index.

Population Characteristics: Patients with a Diagnosis of Diabetes Mellitus or Documented Hyperglycemia
Patients Meeting Criteria of Diabetes Mellitus Diagnosis or Hyperglycemia (n = 9,314 patients)BaselineTP2TP3
  • P < 0.02 Pearson chi square.

  • P < 0.001 analysis of variance between the 3 time periods.

Time period (TP)November 2002 to October 2003November 2003 to April 2005May 2005 to December 2005
Monitored patient days (44,232)11,57121,12611,535
Number of patients (9,314)2,5044,5152,295
Males (%)555456
Average age standard deviation56 1756 1756 16
Length of stay (excluding highest 1% of outliers)4.6 5.94.6 5.74.8 5.8
% With any intensive care unit days*182022
Case mix index score (mean SD)1.8 2.12.0 2.32.1 2.1
Case mix index (median score)1.11.31.3

Of the 9314 study patients, 5530 had 8 or more POC glucose values, and were included in a secondary analysis of glycemic control and hypoglycemia.

Insulin Use Patterns

Figure 4 demonstrates the dramatic improvement that took place with the introduction of the structured order set. In the 6 months preceding the introduction of the structured insulin order set (May‐October 2003) 72% of 477 sampled patients with insulin orders were on sliding scale‐only insulin regimens (with no basal insulin), compared to just 26% of 499 patients sampled in the March to August 2004 time period subsequent to order set implementation (P < .0001, chi square statistic). Intermittent monthly checks on insulin use patterns reveal this change has been sustained.

Figure 4
Percent of patients on subcutaneous insulin orders that are sliding scale–only, without any basal insulin component.

Glycemic Control

A total of 9314 patients with 44,232 monitored patient‐days and over 120,000 POC glucose values were analyzed to assess glycemic control, which was improved with structured insulin orders and improved incrementally with the introduction of the insulin management algorithm.

The percent of patient‐days that were uncontrolled, defined as a monitored day with a mean glucose of 180 mg/dL, was reduced over the 3 time periods (37.8% versus 33.9% versus 30.1%, P < 0.005, Pearson chi square statistic), representing a 21% RR reduction of uncontrolled patient‐days from TP1 versus TP3. Table 2 shows the summary results for glycemic control, including the RR and CIs between the 3 time periods.

Glycemic Control Summary for 9,314 Patients with a Diagnosis of Diabetes Mellitus or Documented Hyperglycemia
Time Period (TP)BaselineTP2 Structured OrdersTP3 Orders Plus AlgorithmRelative Risk TP3:TP2
  • An uncontrolled patient‐day is defined as a monitored patient day with a mean glucose of 180 mg/dL.

  • P value of <0.005.

  • An uncontrolled patient‐stay is defined as a patient‐stay with a day‐weighted mean glucose value of 180 mg/dL.

Patient‐day glucose    
Mean SD179 66170 65165 58 
Median160155151 
Uncontrolled patient‐days*4,3727,1623,465 
Monitored patient‐days11,55521,13511,531 
% Uncontrolled patient‐days37.833.930.1 
RR: uncontrolled patient‐day (95% confidence interval)1.00.89 (0.87‐0.92)0.79 (0.77‐0.82)0.89 (0.86‐0.92)
Glycemic control by patient‐stay    
Day‐weighted mean SD177 57174 54170 50 
Day‐weighted median167162158 
Uncontrolled patient‐stay (%)1,0381,696784 
Monitored patient‐stay2,5044,5152,295 
% Uncontrolled patient‐stays41.537.634.2 
RR: uncontrolled patient‐stay (95% confidence interval) 0.91 (0.85‐0.96)0.84 (0.77‐0.89)0.91 (0.85‐0.97)

In a similar fashion, the percent of patients with uncontrolled patient‐stays (day‐weighted mean glucose 180 mg/dL) was also reduced over the 3 time periods (41.5% versus 37.6% versus 34.2%, P < 0.05, Pearson chi square statistic, with an RR reduction of 16% for TP3:TP1). Figure 5 depicts a statistical process control chart of the percent of patients experiencing uncontrolled patient‐stays over time, and is more effective in displaying the temporal relationship of the interventions with the improved results.

Figure 5
Statistical process control chart, tracking percent of patient‐stays that are “uncontrolled” (day‐weighted mean ≥180 mg/dL). For complete glycemic control results see Tables 2 and 3.

Uncontrolled hyperglycemic days and stays were reduced incrementally from TP3 versus TP2, reflecting the added benefit of the insulin management algorithm, compared to the benefit enjoyed with the structured order set alone.

When the analyses were repeated after excluding patients with fewer than 8 POC glucose readings (Table 3), the findings were similar, but as predicted, the effect was slightly more pronounced, with a 23% relative reduction in uncontrolled patient‐days and a 27% reduction in uncontrolled patient‐stays of TP3 versus TP1.

Glycemic Control Summary for 5530 Patients with a Diagnosis of Diabetes Mellitus or Documented Hyperglycemia and 8 POC Glucose Values Available
Time Period (TP)BaselineTP2 Structured OrdersTP3 Orders Plus AlgorithmRelative Risk TP3:TP2
  • An uncontrolled patient‐day is defined as a monitored patient day with a mean glucose of 180 mg/dL.

  • P value of <0.005.

  • An uncontrolled patient‐stay is defined as a patient‐stay with a day‐weighted mean glucose value of 180 mg/dL.

Patient‐day glucose    
Mean SD172 65169 64163 57 
Median159154149 
Uncontrolled patient‐days*3,4695,6392,766 
Monitored patient‐days9,30417,2789,671 
% Uncontrolled patient‐days37.332.628.6 
RR: uncontrolled patient‐day (95% confidence interval)1.00.87 (0.85‐0.90)0.77 (0.74‐0.80)0.88 (0.84‐0.91)
Glycemic control by patient‐stay    
Day‐weighted mean SD175 51169 47166 45 
Day‐weighted median167158155 
Uncontrolled patient‐stay (%)588908425 
Monitored patient‐stay1,4392,6591,426 
% Uncontrolled patient‐stays40.134.129.8 
RR: Uncontrolled patient‐stay (95% confidence interval) 0.84 (0.77‐0.91)0.73 (0.66‐0.81)0.87 (0.79‐0.96)

Hypoglycemia

Table 4 summarizes the results for hypoglycemia and severe hypoglycemia in the study population, and Table 5 summarizes the secondary analyses of hypoglycemia in the subset with at least 8 POC glucose readings.

Hypoglycemia Summary for 9,314 Patients with Diabetes Mellitus or Documented Hyperglycemia
TP (Time Period)BaselineTP2TP3Relative Risk TP3:TP2
  • NOTE: Hypoglycemia is defined as a glucose 60 mg/dL, severe hypoglycemia is defined as a glucose 40 mg/dL.

  • Abbreviations: RR, relative risk; CI, 95% confidence interval.

Monitored patient‐stays250445152295 
Stays with hypoglycemia (%)296 (11.8)437 (9.7)210 (9.2) 
RR hypoglycemic stay (CI)1.00.82 (0.72‐0.94)0.77 (0.65‐0.92)0.95 (0.81‐1.10)
Stays with severe hypoglycemia (%)73 (2.9)96 (2.1)55 (2.4) 
RR severe hypoglycemic stay (CI)1.00.73 (0.54‐0.98)0.82 (0.58‐1.16)1.13 (0.81‐1.56)
Monitored patient‐days11,58421,15811,548 
Days with hypoglycemia (%)441 (3.8)623 (2.9)300 (2.6) 
RR hypoglycemic day (CI)1.00.77 (0.69‐0.87)0.68 (0.59‐0.78)0.88 (0.77‐1.01)
Days with severe hypoglycemia (%)86 (0.74)109 (0.52)66 (0.57) 
RR Severe hypoglycemic day (CI)1.00.69 (0.52‐0.92)0.77 (0.56‐1.06)1.10 (0.82‐1.5)
Hypoglycemia Summary for 5,530 Patients with Diabetes Mellitus or Documented Hyperglycemia and 8 Point of Care Glucose Values Available for Analysis
TP (Time Period)BaselineTP2TP3Relative Risk TP3:TP2
  • NOTE: Hypoglycemia is defined as a glucose 60 mg/dL and severe hypoglycemia is defined as a glucose 40 mg/dL.

  • Abbreviations: RR, relative risk; CI, 95% confidence interval.

Monitored patient‐stays144026641426 
Stays with hypoglycemia (%)237 (16.5)384 (14.4)180 (12.6) 
RR hypoglycemic stay (CI)1.00.88 (0.76‐1.02)0.77 (0.64‐0.92)0.88 (0.75‐1.03)
Stays with severe hypoglycemia (%)58 (4.0)93 (3.5)47 (3.3) 
RR severe hypoglycemic stay (CI)1.00.87 (0.63‐1.2)0.82 (0.56‐1.19)0.94 (0.67‐1.33)
Monitored patient‐days9,31717,3109,684 
Days with hypoglycemia (%)379 (4.1)569 (3.3)269 (2.7) 
RR hypoglycemic day (CI)1.00.81 (0.71‐0.92)0.68 (0.59‐0.80)0.85 (0.73‐0.98)
Days with severe hypoglycemia (%)71 (0.76)106 (0.61)58 (0.60) 
RR severe hypoglycemic day (CI)1.00.80 (0.60‐1.08)0.79 (0.56‐1.11)0.98 (0.71‐1.34)

Analysis by Patient‐Stay

The percent of patients that suffered 1 or more hypoglycemic event over the course of their inpatient stay was 11.8% in TP1, 9.7% in TP2, and 9.2% in TP3. The RR of a patient suffering from a hypoglycemic event was significantly improved in the intervention time periods compared to baseline, with the RR of TP3:TP1 = 0.77 (CI, 0.65‐0.92). There was a strong trend for incremental improvement in hypoglycemic patient‐stays for TP3 versus TP2, but the trend just missed statistical significance (P < 0.07). Similar trends in improvement were found for severe hypoglycemia by patient‐stay, but these trends were only statistically significant for TP2 versus TP1. The findings were similar in the subset of patients with at least 8 POC glucose readings (Table 5).

Analysis by Patient‐Day

Of monitored patient days in the baseline TP1, 3.8% contained a hypoglycemic value of 60 mg/dL. With the introduction of structured insulin orders in TP2, this was reduced to 2.9%, and in TP3 it was 2.6%. The RR of a hypoglycemic patient‐day of TP2 compared to TP1 was 0.77 (CI, 0.69‐0.87), whereas the cumulative impact of the structured order set and algorithm (TP3:TP1) was 0.68 (CI, 0.59‐0.78), representing a 32% reduction of the baseline risk of suffering from a hypoglycemic day. Similar reductions were seen for the risk of a severe hypoglycemic patient‐day.

The secondary analysis of hypoglycemic and severe hypoglycemic patient‐days showed very similar results, except that the TP3:TP2 RR for hypoglycemia of 0.85 (CI, 0.73‐0.98) reached statistical significance, again demonstrating the incrementally beneficial effect of the insulin management algorithm.

DISCUSSION

Our study convincingly demonstrates that significant improvement in glycemic control can be achieved with implementation of structured subcutaneous insulin orders and a simple insulin management protocol. Perhaps more importantly, these gains in glycemic control are not gained at the expense of increased iatrogenic hypoglycemia, and in fact, we observed a 32% decline in the percent of patient‐days with hypoglycemia. This is extremely important because fear of hypoglycemia is the most significant barrier to glycemic control efforts.

Strengths and Limitations

Our study has several strengths. The study is large and incorporates all patients with diabetes or hyperglycemia captured by POC glucose testing, and the observation period is long enough that bias from merely being observed is not a factor. We used metrics for glycemic control, hypoglycemia, and insulin use patterns that are of high quality and are generally in line with the Society of Hospital Medicine (SHM) Glycemic Control Task force recommendations,12, 13 and examined data by both patient‐stay and patient‐day.

The increased use of anticipatory physiologic subcutaneous insulin regimens, and the subsequent decline in the use of sliding scale insulin, is the most likely mechanism for improvement. The improvements seen are fairly dramatic for an institution in absolute terms, because inpatient hyperglycemia and hypoglycemia are so common. For example, on an annualized basis for our 400‐bed medical center, these interventions prevent 124 patients from experiencing 208 hypoglycemic days.

Other institutions should be able to replicate our results. We received administrative support to create a multidisciplinary steering committee, but we did not have incremental resources to create a dedicated team for insulin management, mandated endocrinology comanagement or consultations, or manual data collection. In fact, we had only 1 diabetes educator for 400 adult beds at 2 sites, and were relatively underresourced in this area by community standards. There was some time and expense in creating the glycemic control reports, but all of the glucose data collected were part of normal care, and the data retrieval became automated.

The main limitation of this study lies in the observational study design. There were multiple interventions in addition to structured insulin orders and the insulin management algorithm, and these educational and organizational changes undoubtedly also contributed to the overall success of our program. Since we did not perform a randomized controlled trial, the reader might reasonably question if the structured order sets and insulin management algorithm were actually the cause of the improvement seen, as opposed to these ancillary efforts or secular change. However, there are several factors that make this unlikely. First, the study population was well‐defined, having diabetes or documented hyperglycemia in all 3 time periods. Second, the demographics remained constant or actually worked against improvement trends, since the markers of patient acuity suggest increased patient acuity over the observation period. Third, the temporal relationship of the improvement to the introduction of our key interventions, as viewed on statistical process control charts shown in Figure 5, strongly suggest a causal relationship. This temporal relationship was consistently observed no matter how we chose to define uncontrolled hyperglycemia, and was also seen on hypoglycemia control charts. We view the ancillary interventions (such as educational efforts) as necessary, but not sufficient, in and of themselves, to effect major improvement.

We did not analyze the impact of the improved glycemic control on patient outcomes. In the absence of a randomized controlled trial design, controlling for the various confounders is a challenging task. Also, it is likely that not all hypoglycemic events were attributable to inpatient glycemic control regimens, though the secondary analysis probably eliminated many hypoglycemia admissions.

Lessons Learned: Implications from our study

We agree with the American Association of Clinical Endocrinologists (AACE)/American Diabetes Association (ADA)2 and the SHM Glycemic Control Task Force12 about the essential elements needed for successful implementation of inpatient glycemic control programs:

  • An appropriate level of administrative support.

  • Formation of a multidisciplinary steering committee to drive the development of initiatives, empowered to enact changes.

  • Assessment of current processes, quality of care, and barriers to practice change.

  • Development and implementation of interventions, including standardized order sets, protocols, policies, and algorithms with associated educational programs.

  • Metrics for evaluation of glycemic control, hypoglycemia, insulin use patterns, and other aspects of care.

 

Metrics to follow hypoglycemia are extremely important. The voluntary reporting on insulin‐induced hypoglycemia fluctuated widely over the course of our project. These fluctuations did not correlate well with the more objective and accurate measures we followed, and this objective data was very helpful in reducing the fear of hypoglycemia, and spreading the wider use of basal bolus insulin regimens. We strongly recommend that improvement teams formulate and follow measures of glycemic control, hypoglycemia, and insulin use, similar to those outlined in the SHM Glycemic Control Improvement Guide12 and the SHM Glycemic Control Task Force summary on glucometrics.13

Although we introduced our structured insulin order set first, with a long lag time until we introduced the insulin management algorithm, we advocate a different approach for institutions grappling with these issues. This approach is well‐described by the SHM Glycemic Control Task Force.14 An insulin management algorithm should be crafted first, integrating guidance for insulin dosing, preferred insulin regimens for different nutritional situations, a glycemic target, insulin dosing adjustment, glucose monitoring, and prompts for ordering a glycosylated hemoglobin (A1c) level. Next, the order set and the supporting educational programs should integrate this guidance as much as possible, making the key guidance available at the point of patient care.

This guidance was available in our algorithm but was not inherent in the structured insulin orders described in this report, and all basal and nutritional insulin options were offered as equally acceptable choices. This version did not calculate insulin doses or assist in the apportionment of insulin between basal and nutritional components. Only a single adjustment dose scale was offered, leaving appropriate modifications up to the end user, and from a usability standpoint, our CPOE insulin orders lacked dynamic flexibility (revising a single insulin required discontinuing all prior orders and reentering all orders). These limitations have subsequently been addressed with Version 2 of our CPOE insulin orders, and the details will soon be available in the literature.15

We are now exploring further improvement with concurrent identification and intervention of hyperglycemic patients that are not on physiologic insulin regimens or not meeting glycemic targets, and implementing protocols addressing the transition from infusion insulin.

CONCLUSION

We significantly improved glycemic control and simultaneously reduced hypoglycemia across all major medical and surgical services at our medical center, thereby addressing the number 1 barrier to improved inpatient glycemic control. We achieved this via systems changes with the introduction of structured subcutaneous insulin orders and the insulin management algorithm, along with education, but did not otherwise mandate or monitor adherence to our algorithm.

Implementing an institutional insulin management algorithm and structured insulin orders should now be viewed as a potent safety intervention as well as an intervention to enhance quality, and we have demonstrated that non‐critical care glycemic control efforts can clearly be a win‐win situation.

References
  1. Centers for Disease Control and Prevention.National Diabetes Fact Sheet: General Information and National Estimates on Diabetes in the United States, 2002.Atlanta, GA:U.S. Department of Health and Human Services, Centers for Disease Control and Prevention;2003. Available at: www.cdc.gov/diabetes/pubs/factsheet.htm. Accessed January 21, 2006.
  2. American College of Endocrinology and American Diabetes Association Consensus statement on inpatient diabetes and glycemic control: a call to action.Diabetes Care.2006;29:19551962.
  3. Umpierrez GE,Isaacs SD,Bazargan N, et al.Hyperglycemia: an independent marker of in‐hospital mortality in patients with undiagnosed diabetes.J Clin Endocrinol Metab.2002;87:978982.
  4. McAlister FA,Majumdar SR,Blitz S, et al.The relation between hyperglycemia and outcomes in 2471 patients admitted to the hospital with community‐acquired pneumonia.Diabetes Care.2005;28:810815.
  5. Weiser MA,Cabanillas ME,Konopleva M, et al.Cancer.2004;100:11791185.
  6. Thomas MC,Mathew TH,Russ GR, et al.Early perioperative glycaemic control and allograft rejection in patients with diabetes mellitus: a pilot study.Transplantation.2001;72:13211324.
  7. Pomposelli JJ,Baxter JK,Babineau TJ, et al.Early postoperative glucose control predicts nosocomial infection rate in diabetic patients.J Parenter Enteral Nutr.1998;22:7781.
  8. Zerr KJ,Furnary AP,Grunkemeier GL, et al.Glucose control lowers the risk of wound infection in diabetics after open heart operations.Ann Thorac Surg.1997;63:356361.
  9. Clement S,Braithwaite SS,Magee MF, et al.Management of diabetes and hyperglycemia in hospitals.Diabetes Care.2004;27:553591.
  10. Garber AJ,Moghissi ES,Bransome ED, et al.American College of Endocrinology position statement on inpatient diabetes and metabolic control.Endocr Pract.2004;10:7782.
  11. Umpierrez G,Maynard G.Glycemic chaos (not glycemic control) still the rule for inpatient care: how do we stop the insanity? [Editorial].J Hosp Med.2006;1:141144.
  12. Society of Hospital Medicine Glycemic Control Task Force: Optimizing Glycemic Control and Reducing Hypoglycemia at Your Medical Center. Society of Hospital Medicine, Glycemic Control Quality Improvement Resource Room. Available at: http://www.hospitalmedicine.org/ResourceRoomRedesign/GlycemicControl.cfm. Accessed October2008.
  13. Schnipper JL,Magee MF,Inzucchi SE,Magee MF,Larsen K,Maynard G.SHM Glycemic Control Task Force summary: practical recommendations for assessing the impact of glycemic control efforts.J Hosp Med.2008;3(S5):6675.
  14. Maynard G,Wesorick DH,O'Malley CW,Inzucchi SE;for the SHM Glycemic Control Task Force.Subcutaneous insulin order sets and protocols: effective design and implementation strategies.J Hosp Med.2008;3(S5):2941.
  15. Lee J,Clay B,Zelazny Z,Maynard G.Indication‐based ordering: a new paradigm for glycemic control in hospitalized inpatients.J Diabetes Sci Tech.2008;2(3):349356.
Article PDF
Issue
Journal of Hospital Medicine - 4(1)
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Page Number
3-15
Legacy Keywords
diabetes mellitus, glycemic control, hypoglycemia, insulin, patient safety, quality improvement
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Article PDF

Diabetes has reached epidemic proportions in the United States, affecting over 20 million individuals,1 and further rises are expected. A disproportionate increase in diabetes has occurred in the inpatient setting.2 Furthermore, for every 2 patients in the hospital with known diabetes, there may be an additional 1 with newly observed hyperglycemia. Both are common. In 1 report, for example, 24% of inpatients with hyperglycemia had a prior diagnosis of diabetes, whereas another 12% had hyperglycemia without a prior diagnosis of diabetes.3

Although there is a paucity of high quality randomized controlled trials to support tight glycemic control in non‐critical care inpatient settings, poor glycemic control in hospitalized patients is strongly associated with undesirable outcomes for a variety of conditions, including pneumonia,4 cancer chemotherapy,5 renal transplant,6 and postsurgical wound infections.7, 8 Hyperglycemia also induces dehydration, fluid and electrolyte imbalance, gastric motility problems, and venous thromboembolism formation.9

Structured subcutaneous insulin order sets and insulin management protocols have been widely advocated as a method to encourage basal bolus insulin regimens and enhance glycemic control,2, 9, 10 but the effect of these interventions on glycemic control, hypoglycemia, and insulin use patterns in the real world setting has not been well reported. Fear of inducing hypoglycemia is often the main barrier for initiating basal insulin containing regimens and pursuing glycemic targets.2 The evidence would suggest, however, that sliding scale regimens, as opposed to more physiologic basal bolus regimens, may actually increase both hypoglycemic and hyperglycemic excursions.11 A convincing demonstration of the efficacy (improved insulin use patterns and reduced hyperglycemia) and safety (reduced hypoglycemia) of structured insulin order sets and insulin management protocols would foster a more rapid adoption of these strategies.

PATIENTS AND METHODS

In our 400‐bed university hospital, we formed a hospitalist‐led multidisciplinary team in early 2003, with the focus of improving the care delivered to non‐critical care patients with diabetes or hyperglycemia. We used a Plan‐Do‐Study‐Act (PDSA) performance improvement framework, and conducted institutional review board (IRB)‐approved prospective observational research in parallel with the performance improvement efforts, with a waiver for individual informed consent. The study population consisted of all adult inpatients on non‐critical care units with electronically reported point of care (POC) glucose testing from November 2002 through December 2005. We excluded patients who did not have either a discharge diagnosis of Diabetes (ICD 9 codes 250‐251.XX) or demonstrated hyperglycemia (fasting POC glucose >130 mg/dL 2, or a random value of >180 mg/dL) from analysis of glycemic control and hypoglycemia. Women admitted to Obstetrics were excluded. Monthly and quarterly summaries on glycemic control, hypoglycemia, and insulin use patterns (metrics described below) were reported to the improvement team and other groups on a regular basis throughout the intervention period. POC glucose data, demographics, markers of severity of illness, and diagnosis codes were retrieved from the electronic health record.

Interventions

We introduced several interventions and educational efforts throughout the course of our improvement. The 2 key interventions were as follows:

  • Structured subcutaneous insulin order sets (November, 2003).

  • An insulin management algorithm, described below (May 2005).

 

Key Intervention #1: Structured Subcutaneous Insulin Order Set Implementation

In November 2003, we introduced a paper‐based structured subcutaneous insulin order set. This order set encouraged the use of scheduled basal and nutritional insulin, provided guidance for monitoring glucose levels, and for insulin dosing. A hypoglycemia protocol and a standardized correction insulin table were embedded in the order set. This set was similar to examples of structured insulin ordering subsequently presented in the literature.9 In a parallel effort, the University of California, San Diego Medical Center (UCSDMC) was developing a computer physician order entry (CPOE) module for our comprehensive clinical information system, Invision (Siemens Medical Systems, Malvern, PA), that heretofore had primarily focused on result review, patient schedule management, and nursing documentation. In anticipation of CPOE and for the purpose of standardization, we removed outdated sliding scale insulin regimens from a variety preexisting order sets and inserted references to the standardized subcutaneous insulin order set in their stead. The medication administration record (MAR) was changed to reflect the basal/nutritional/correction insulin terminology. It became more difficult to order a stand‐alone insulin sliding scale even before CPOE versions became available. The standardized order set was the only preprinted correction scale insulin order available, and ordering physicians have to specifically opt out of basal and nutritional insulin choices to order sliding scale only regimens. Verbal orders for correction dose scales were deemed unacceptable by medical staff committees. Correctional insulin doses could be ordered as a 1‐time order, but the pharmacy rejected ongoing insulin orders that were not entered on the structured form.

We introduced our first standardized CPOE subcutaneous insulin order set in January 2004 at the smaller of our 2 campuses, and subsequently completed full deployment across both campuses in all adult medical‐surgical care areas by September 2004.

The CPOE version, like the paper version that immediately preceded it, encouraged the use of basal/bolus insulin regimens, promoted the terms basal, nutritional or premeal, and adjustment dose insulin in the order sets and the medication administration record, and was mandatory for providers wishing to order anything but a 1‐time order of insulin. Figure 1 depicts a screen shot of the CPOE version. Similar to the paper version, the ordering physician had to specifically opt out of ordering scheduled premeal and basal insulin to order a sliding scale only regimen. The first screen also ensured that appropriate POC glucose monitoring was ordered and endorsed a standing hypoglycemia protocol order. The CPOE version had only a few additional features not possible on paper. Obvious benefits included elimination of unapproved abbreviations and handwriting errors. Nutritional and correction insulin types were forced to be identical. Fundamentally, however, both the paper and online structured ordering experiences had the same degree of control over provider ordering patterns, and there was no increment in guidance for choosing insulin regimens, hence their combined analysis as structured orders.

Figure 1
Screen shot: Computerized physician order entry version of structured insulin orders.

Key Intervention #2: Insulin Management Algorithm

The structured insulin order set had many advantages, but also had many limitations. Guidance for preferred insulin regimens for patients in different nutritional situations was not inherent in the order set, and all basal and nutritional insulin options were offered as equally acceptable choices. The order set gave very general guidance for insulin dosing, but did not calculate insulin doses or assist in the apportionment of insulin between basal and nutritional components, and guidance for setting a glycemic target or adjusting insulin was lacking.

Recognizing these limitations, we devised an insulin management algorithm to provide guidance incremental to that offered in the order set. In April 2005, 3 hospitalists piloted a paper‐based insulin management algorithm (Figure 2, front; Figure 3, reverse) on their teaching services. This 1‐page algorithm provided guidance on insulin dosing and monitoring, and provided institutionally preferred insulin regimens for patients in different nutritional situations. As an example, of the several acceptable subcutaneous insulin regimens that an eating patient might use in the inpatient setting, we advocated the use of 1 preferred regimen (a relatively peakless, long‐acting basal insulin once a day, along with a rapid acting analog nutritional insulin with each meal). We introduced the concept of a ward glycemic target, provided prompts for diabetes education, and generally recommended discontinuation of oral hypoglycemic agents in the inpatient setting. The hospitalists were introduced to the concepts and the algorithm via 1 of the authors (G.M.) in a 1‐hour session. The algorithm was introduced on each teaching team during routine teaching rounds with a slide set (approximately 15 slides) that outlined the basic principles of insulin dosing, and gave example cases which modeled the proper use of the algorithm. The principles were reinforced on daily patient work rounds as they were applied on inpatients with hyperglycemia. The pilot results on 25 patients, compared to 250 historical control patients, were very promising, with markedly improved glycemic control and no increase in hypoglycemia. We therefore sought to spread the use of the algorithm. In May 2005 the insulin management algorithm and teaching slide set were promoted on all 7 hospitalist‐run services, and the results of the pilot and concepts of the algorithm were shared with a variety of house staff and service leaders in approximately a dozen sessions: educational grand rounds, assorted noon lectures, and subsequently, at new intern orientations. Easy access to the algorithm was assured by providing a link to the file within the CPOE insulin order set.

Figure 2
Insulin management algorithm (front) introduced at UCSD in May 2005 (marking the onset of Time Period 3).
Figure 3
Insulin management algorithm (reverse) introduced at UCSD in May 2005 (marking the onset of Time Period 3).

Other Attempts to Improve Care

Several other issues were addressed in the context of the larger performance improvement effort by the team. In many cases, hard data were not gathered to assess the effectiveness of the interventions, or the interventions were ongoing and could be considered the background milieu for the key interventions listed above.

During each intervention, education sessions were given throughout the hospital to staff, including physicians, residents, and nurses, using departmental grand rounds, nursing rounds, and in‐services to describe the process and goals. Patient education programs were also redesigned and implemented, using preprinted brochure. Front‐line nursing staff teaching skills were bolstered via Clinical Nurse Specialist educational sessions, and the use of a template for patient teaching. The educational template assessed patient readiness to learn, home environment, current knowledge, and other factors. Approximately 6 conferences directed at various physician staff per year became part of the regular curriculum.

We recognized that there was often poor coordination between glucose monitoring, nutrition delivery, and insulin administration. The traditional nursing practice of the 6:00 AM finger stick and insulin administration was changed to match a formalized nutrition delivery schedule. Nutrition services and nursing were engaged to address timeliness of nutrition delivery, insulin administration, and POC glucose documentation in the electronic health record.

Feedback to individual medicine resident teams on reaching glycemic targets, with movie ticket/coffee coupon rewards to high performing teams, was tried from April 2004 to September 2004.

Measures and Analyses

Assessing Insulin Use Patterns

A convenience sample gathering all subcutaneous insulin orders from 4 to 5 selected days per month yielded 70 to 90 subcutaneous insulin orders for review each month. Sampling was originally performed each month, followed by less frequent sampling once stability in insulin use patterns was reached. Regimens were categorized by pharmacy and hospitalist review as to whether basal insulin was part of the insulin regimen or not. The percentage of insulin regimens incorporating basal insulin was calculated for each sampled month and followed in run charts, and comparisons between preorder set and postorder set time periods were made using Pearson's chi square statistic.

Assessing Glycemic Control

Glycemic control and hypoglycemia parameters were monitored for the entire 38‐month observation period.

Routinely monitored POC glucose values were used to assess glycemic control. During the initial data examination, it was found after 14 days of the hospital stay, there was a notable stabilization and improvement in glucose control and fewer hypoglycemic events, therefore we examined only the first 14 days of hospitalization, thereby eliminating a potential source of bias from length of stay outliers.

A mean glucose value was recorded for each patient‐day with 1 or more recorded values. Glycemic control for each patient‐stay was calculated by averaging the patient‐day mean values, which we will refer to as the day‐weighted mean. Hypoglycemic values (60 mg/dL) were excluded from calculation of the mean glucose, to avoid equating frequent hypoglycemia with optimal glycemic control. An uncontrolled patient‐day was defined as a monitored patient‐day with a mean glucose 180 mg/dL. An uncontrolled patient‐stay is defined as a patient‐stay with a day‐weighted mean glucose value 180 mg/dL.

We theorized that the greatest impact of the interventions would be realized in patients with longer monitoring periods, and that those with only a few POC glucose values could potentially misrepresent the impact of our interventions: therefore we performed a second analysis restricted to patients with 8 POC glucose values.

Assessing Hypoglycemia

Hypoglycemia was defined as a glucose 60 mg/dL, and severe hypoglycemia was defined as a glucose 40 mg/dL. These parameters were characterized by 2 methods. First, we calculated the percentage of monitored patients suffering from 1 or more hypoglycemic events or severe hypoglycemic events over the course of their entire admission. A second method tracked the percentage of monitored patient‐days with hypoglycemia and severe hypoglycemia, thereby correcting for potential misinterpretation from clustered repeated measures or variable length of stay. As with the glycemic control analysis, we repeated the hypoglycemia analysis in the subset of patients with 8 POC glucose values.

Summary Analysis of Glycemic Control and Hypoglycemia

Pearson chi square values, with relative risks (RRs) and 95% confidence intervals (CIs) were calculated to compare glycemic control and hypoglycemia in the 2 key interventions and baseline. The interventions and data reporting were grouped as follows:

  • Baseline: November 2002 to October 2003) = Time Period 1 (TP1)

  • Structured Order Set: November 2003 to April 2005) = Time Period 2 (TP2)

  • Algorithm plus Structured Order Set: May 2005 to December 2005) = Time Period 3 (TP3)

 

A P value of less than 0.05 was determined as significant and data were analyzed using STATA, Version 8 (STATA Corp., College Station, TX).

We assigned the RR of uncontrolled hyperglycemia and the RR of hypoglycemia during the baseline time (TP1) with values of 1.0, and calculated the RR and CIs for the same parameters during TP2 and TP3.

RESULTS

Just over 11,000 patients were identified for POC glucose testing over the 38 month observation period. Of these, 9314 patients had either a diagnosis of diabetes or documented hyperglycemia. The characteristics of this study population are depicted in Table 1. There were no differences between the groups and the demographics of age, gender, or length of stay (P > 0.05 for all parameters). There was a slight increase in the percent of patients with any intensive care unit days over the 3 time periods and a similar increase in the case mix index.

Population Characteristics: Patients with a Diagnosis of Diabetes Mellitus or Documented Hyperglycemia
Patients Meeting Criteria of Diabetes Mellitus Diagnosis or Hyperglycemia (n = 9,314 patients)BaselineTP2TP3
  • P < 0.02 Pearson chi square.

  • P < 0.001 analysis of variance between the 3 time periods.

Time period (TP)November 2002 to October 2003November 2003 to April 2005May 2005 to December 2005
Monitored patient days (44,232)11,57121,12611,535
Number of patients (9,314)2,5044,5152,295
Males (%)555456
Average age standard deviation56 1756 1756 16
Length of stay (excluding highest 1% of outliers)4.6 5.94.6 5.74.8 5.8
% With any intensive care unit days*182022
Case mix index score (mean SD)1.8 2.12.0 2.32.1 2.1
Case mix index (median score)1.11.31.3

Of the 9314 study patients, 5530 had 8 or more POC glucose values, and were included in a secondary analysis of glycemic control and hypoglycemia.

Insulin Use Patterns

Figure 4 demonstrates the dramatic improvement that took place with the introduction of the structured order set. In the 6 months preceding the introduction of the structured insulin order set (May‐October 2003) 72% of 477 sampled patients with insulin orders were on sliding scale‐only insulin regimens (with no basal insulin), compared to just 26% of 499 patients sampled in the March to August 2004 time period subsequent to order set implementation (P < .0001, chi square statistic). Intermittent monthly checks on insulin use patterns reveal this change has been sustained.

Figure 4
Percent of patients on subcutaneous insulin orders that are sliding scale–only, without any basal insulin component.

Glycemic Control

A total of 9314 patients with 44,232 monitored patient‐days and over 120,000 POC glucose values were analyzed to assess glycemic control, which was improved with structured insulin orders and improved incrementally with the introduction of the insulin management algorithm.

The percent of patient‐days that were uncontrolled, defined as a monitored day with a mean glucose of 180 mg/dL, was reduced over the 3 time periods (37.8% versus 33.9% versus 30.1%, P < 0.005, Pearson chi square statistic), representing a 21% RR reduction of uncontrolled patient‐days from TP1 versus TP3. Table 2 shows the summary results for glycemic control, including the RR and CIs between the 3 time periods.

Glycemic Control Summary for 9,314 Patients with a Diagnosis of Diabetes Mellitus or Documented Hyperglycemia
Time Period (TP)BaselineTP2 Structured OrdersTP3 Orders Plus AlgorithmRelative Risk TP3:TP2
  • An uncontrolled patient‐day is defined as a monitored patient day with a mean glucose of 180 mg/dL.

  • P value of <0.005.

  • An uncontrolled patient‐stay is defined as a patient‐stay with a day‐weighted mean glucose value of 180 mg/dL.

Patient‐day glucose    
Mean SD179 66170 65165 58 
Median160155151 
Uncontrolled patient‐days*4,3727,1623,465 
Monitored patient‐days11,55521,13511,531 
% Uncontrolled patient‐days37.833.930.1 
RR: uncontrolled patient‐day (95% confidence interval)1.00.89 (0.87‐0.92)0.79 (0.77‐0.82)0.89 (0.86‐0.92)
Glycemic control by patient‐stay    
Day‐weighted mean SD177 57174 54170 50 
Day‐weighted median167162158 
Uncontrolled patient‐stay (%)1,0381,696784 
Monitored patient‐stay2,5044,5152,295 
% Uncontrolled patient‐stays41.537.634.2 
RR: uncontrolled patient‐stay (95% confidence interval) 0.91 (0.85‐0.96)0.84 (0.77‐0.89)0.91 (0.85‐0.97)

In a similar fashion, the percent of patients with uncontrolled patient‐stays (day‐weighted mean glucose 180 mg/dL) was also reduced over the 3 time periods (41.5% versus 37.6% versus 34.2%, P < 0.05, Pearson chi square statistic, with an RR reduction of 16% for TP3:TP1). Figure 5 depicts a statistical process control chart of the percent of patients experiencing uncontrolled patient‐stays over time, and is more effective in displaying the temporal relationship of the interventions with the improved results.

Figure 5
Statistical process control chart, tracking percent of patient‐stays that are “uncontrolled” (day‐weighted mean ≥180 mg/dL). For complete glycemic control results see Tables 2 and 3.

Uncontrolled hyperglycemic days and stays were reduced incrementally from TP3 versus TP2, reflecting the added benefit of the insulin management algorithm, compared to the benefit enjoyed with the structured order set alone.

When the analyses were repeated after excluding patients with fewer than 8 POC glucose readings (Table 3), the findings were similar, but as predicted, the effect was slightly more pronounced, with a 23% relative reduction in uncontrolled patient‐days and a 27% reduction in uncontrolled patient‐stays of TP3 versus TP1.

Glycemic Control Summary for 5530 Patients with a Diagnosis of Diabetes Mellitus or Documented Hyperglycemia and 8 POC Glucose Values Available
Time Period (TP)BaselineTP2 Structured OrdersTP3 Orders Plus AlgorithmRelative Risk TP3:TP2
  • An uncontrolled patient‐day is defined as a monitored patient day with a mean glucose of 180 mg/dL.

  • P value of <0.005.

  • An uncontrolled patient‐stay is defined as a patient‐stay with a day‐weighted mean glucose value of 180 mg/dL.

Patient‐day glucose    
Mean SD172 65169 64163 57 
Median159154149 
Uncontrolled patient‐days*3,4695,6392,766 
Monitored patient‐days9,30417,2789,671 
% Uncontrolled patient‐days37.332.628.6 
RR: uncontrolled patient‐day (95% confidence interval)1.00.87 (0.85‐0.90)0.77 (0.74‐0.80)0.88 (0.84‐0.91)
Glycemic control by patient‐stay    
Day‐weighted mean SD175 51169 47166 45 
Day‐weighted median167158155 
Uncontrolled patient‐stay (%)588908425 
Monitored patient‐stay1,4392,6591,426 
% Uncontrolled patient‐stays40.134.129.8 
RR: Uncontrolled patient‐stay (95% confidence interval) 0.84 (0.77‐0.91)0.73 (0.66‐0.81)0.87 (0.79‐0.96)

Hypoglycemia

Table 4 summarizes the results for hypoglycemia and severe hypoglycemia in the study population, and Table 5 summarizes the secondary analyses of hypoglycemia in the subset with at least 8 POC glucose readings.

Hypoglycemia Summary for 9,314 Patients with Diabetes Mellitus or Documented Hyperglycemia
TP (Time Period)BaselineTP2TP3Relative Risk TP3:TP2
  • NOTE: Hypoglycemia is defined as a glucose 60 mg/dL, severe hypoglycemia is defined as a glucose 40 mg/dL.

  • Abbreviations: RR, relative risk; CI, 95% confidence interval.

Monitored patient‐stays250445152295 
Stays with hypoglycemia (%)296 (11.8)437 (9.7)210 (9.2) 
RR hypoglycemic stay (CI)1.00.82 (0.72‐0.94)0.77 (0.65‐0.92)0.95 (0.81‐1.10)
Stays with severe hypoglycemia (%)73 (2.9)96 (2.1)55 (2.4) 
RR severe hypoglycemic stay (CI)1.00.73 (0.54‐0.98)0.82 (0.58‐1.16)1.13 (0.81‐1.56)
Monitored patient‐days11,58421,15811,548 
Days with hypoglycemia (%)441 (3.8)623 (2.9)300 (2.6) 
RR hypoglycemic day (CI)1.00.77 (0.69‐0.87)0.68 (0.59‐0.78)0.88 (0.77‐1.01)
Days with severe hypoglycemia (%)86 (0.74)109 (0.52)66 (0.57) 
RR Severe hypoglycemic day (CI)1.00.69 (0.52‐0.92)0.77 (0.56‐1.06)1.10 (0.82‐1.5)
Hypoglycemia Summary for 5,530 Patients with Diabetes Mellitus or Documented Hyperglycemia and 8 Point of Care Glucose Values Available for Analysis
TP (Time Period)BaselineTP2TP3Relative Risk TP3:TP2
  • NOTE: Hypoglycemia is defined as a glucose 60 mg/dL and severe hypoglycemia is defined as a glucose 40 mg/dL.

  • Abbreviations: RR, relative risk; CI, 95% confidence interval.

Monitored patient‐stays144026641426 
Stays with hypoglycemia (%)237 (16.5)384 (14.4)180 (12.6) 
RR hypoglycemic stay (CI)1.00.88 (0.76‐1.02)0.77 (0.64‐0.92)0.88 (0.75‐1.03)
Stays with severe hypoglycemia (%)58 (4.0)93 (3.5)47 (3.3) 
RR severe hypoglycemic stay (CI)1.00.87 (0.63‐1.2)0.82 (0.56‐1.19)0.94 (0.67‐1.33)
Monitored patient‐days9,31717,3109,684 
Days with hypoglycemia (%)379 (4.1)569 (3.3)269 (2.7) 
RR hypoglycemic day (CI)1.00.81 (0.71‐0.92)0.68 (0.59‐0.80)0.85 (0.73‐0.98)
Days with severe hypoglycemia (%)71 (0.76)106 (0.61)58 (0.60) 
RR severe hypoglycemic day (CI)1.00.80 (0.60‐1.08)0.79 (0.56‐1.11)0.98 (0.71‐1.34)

Analysis by Patient‐Stay

The percent of patients that suffered 1 or more hypoglycemic event over the course of their inpatient stay was 11.8% in TP1, 9.7% in TP2, and 9.2% in TP3. The RR of a patient suffering from a hypoglycemic event was significantly improved in the intervention time periods compared to baseline, with the RR of TP3:TP1 = 0.77 (CI, 0.65‐0.92). There was a strong trend for incremental improvement in hypoglycemic patient‐stays for TP3 versus TP2, but the trend just missed statistical significance (P < 0.07). Similar trends in improvement were found for severe hypoglycemia by patient‐stay, but these trends were only statistically significant for TP2 versus TP1. The findings were similar in the subset of patients with at least 8 POC glucose readings (Table 5).

Analysis by Patient‐Day

Of monitored patient days in the baseline TP1, 3.8% contained a hypoglycemic value of 60 mg/dL. With the introduction of structured insulin orders in TP2, this was reduced to 2.9%, and in TP3 it was 2.6%. The RR of a hypoglycemic patient‐day of TP2 compared to TP1 was 0.77 (CI, 0.69‐0.87), whereas the cumulative impact of the structured order set and algorithm (TP3:TP1) was 0.68 (CI, 0.59‐0.78), representing a 32% reduction of the baseline risk of suffering from a hypoglycemic day. Similar reductions were seen for the risk of a severe hypoglycemic patient‐day.

The secondary analysis of hypoglycemic and severe hypoglycemic patient‐days showed very similar results, except that the TP3:TP2 RR for hypoglycemia of 0.85 (CI, 0.73‐0.98) reached statistical significance, again demonstrating the incrementally beneficial effect of the insulin management algorithm.

DISCUSSION

Our study convincingly demonstrates that significant improvement in glycemic control can be achieved with implementation of structured subcutaneous insulin orders and a simple insulin management protocol. Perhaps more importantly, these gains in glycemic control are not gained at the expense of increased iatrogenic hypoglycemia, and in fact, we observed a 32% decline in the percent of patient‐days with hypoglycemia. This is extremely important because fear of hypoglycemia is the most significant barrier to glycemic control efforts.

Strengths and Limitations

Our study has several strengths. The study is large and incorporates all patients with diabetes or hyperglycemia captured by POC glucose testing, and the observation period is long enough that bias from merely being observed is not a factor. We used metrics for glycemic control, hypoglycemia, and insulin use patterns that are of high quality and are generally in line with the Society of Hospital Medicine (SHM) Glycemic Control Task force recommendations,12, 13 and examined data by both patient‐stay and patient‐day.

The increased use of anticipatory physiologic subcutaneous insulin regimens, and the subsequent decline in the use of sliding scale insulin, is the most likely mechanism for improvement. The improvements seen are fairly dramatic for an institution in absolute terms, because inpatient hyperglycemia and hypoglycemia are so common. For example, on an annualized basis for our 400‐bed medical center, these interventions prevent 124 patients from experiencing 208 hypoglycemic days.

Other institutions should be able to replicate our results. We received administrative support to create a multidisciplinary steering committee, but we did not have incremental resources to create a dedicated team for insulin management, mandated endocrinology comanagement or consultations, or manual data collection. In fact, we had only 1 diabetes educator for 400 adult beds at 2 sites, and were relatively underresourced in this area by community standards. There was some time and expense in creating the glycemic control reports, but all of the glucose data collected were part of normal care, and the data retrieval became automated.

The main limitation of this study lies in the observational study design. There were multiple interventions in addition to structured insulin orders and the insulin management algorithm, and these educational and organizational changes undoubtedly also contributed to the overall success of our program. Since we did not perform a randomized controlled trial, the reader might reasonably question if the structured order sets and insulin management algorithm were actually the cause of the improvement seen, as opposed to these ancillary efforts or secular change. However, there are several factors that make this unlikely. First, the study population was well‐defined, having diabetes or documented hyperglycemia in all 3 time periods. Second, the demographics remained constant or actually worked against improvement trends, since the markers of patient acuity suggest increased patient acuity over the observation period. Third, the temporal relationship of the improvement to the introduction of our key interventions, as viewed on statistical process control charts shown in Figure 5, strongly suggest a causal relationship. This temporal relationship was consistently observed no matter how we chose to define uncontrolled hyperglycemia, and was also seen on hypoglycemia control charts. We view the ancillary interventions (such as educational efforts) as necessary, but not sufficient, in and of themselves, to effect major improvement.

We did not analyze the impact of the improved glycemic control on patient outcomes. In the absence of a randomized controlled trial design, controlling for the various confounders is a challenging task. Also, it is likely that not all hypoglycemic events were attributable to inpatient glycemic control regimens, though the secondary analysis probably eliminated many hypoglycemia admissions.

Lessons Learned: Implications from our study

We agree with the American Association of Clinical Endocrinologists (AACE)/American Diabetes Association (ADA)2 and the SHM Glycemic Control Task Force12 about the essential elements needed for successful implementation of inpatient glycemic control programs:

  • An appropriate level of administrative support.

  • Formation of a multidisciplinary steering committee to drive the development of initiatives, empowered to enact changes.

  • Assessment of current processes, quality of care, and barriers to practice change.

  • Development and implementation of interventions, including standardized order sets, protocols, policies, and algorithms with associated educational programs.

  • Metrics for evaluation of glycemic control, hypoglycemia, insulin use patterns, and other aspects of care.

 

Metrics to follow hypoglycemia are extremely important. The voluntary reporting on insulin‐induced hypoglycemia fluctuated widely over the course of our project. These fluctuations did not correlate well with the more objective and accurate measures we followed, and this objective data was very helpful in reducing the fear of hypoglycemia, and spreading the wider use of basal bolus insulin regimens. We strongly recommend that improvement teams formulate and follow measures of glycemic control, hypoglycemia, and insulin use, similar to those outlined in the SHM Glycemic Control Improvement Guide12 and the SHM Glycemic Control Task Force summary on glucometrics.13

Although we introduced our structured insulin order set first, with a long lag time until we introduced the insulin management algorithm, we advocate a different approach for institutions grappling with these issues. This approach is well‐described by the SHM Glycemic Control Task Force.14 An insulin management algorithm should be crafted first, integrating guidance for insulin dosing, preferred insulin regimens for different nutritional situations, a glycemic target, insulin dosing adjustment, glucose monitoring, and prompts for ordering a glycosylated hemoglobin (A1c) level. Next, the order set and the supporting educational programs should integrate this guidance as much as possible, making the key guidance available at the point of patient care.

This guidance was available in our algorithm but was not inherent in the structured insulin orders described in this report, and all basal and nutritional insulin options were offered as equally acceptable choices. This version did not calculate insulin doses or assist in the apportionment of insulin between basal and nutritional components. Only a single adjustment dose scale was offered, leaving appropriate modifications up to the end user, and from a usability standpoint, our CPOE insulin orders lacked dynamic flexibility (revising a single insulin required discontinuing all prior orders and reentering all orders). These limitations have subsequently been addressed with Version 2 of our CPOE insulin orders, and the details will soon be available in the literature.15

We are now exploring further improvement with concurrent identification and intervention of hyperglycemic patients that are not on physiologic insulin regimens or not meeting glycemic targets, and implementing protocols addressing the transition from infusion insulin.

CONCLUSION

We significantly improved glycemic control and simultaneously reduced hypoglycemia across all major medical and surgical services at our medical center, thereby addressing the number 1 barrier to improved inpatient glycemic control. We achieved this via systems changes with the introduction of structured subcutaneous insulin orders and the insulin management algorithm, along with education, but did not otherwise mandate or monitor adherence to our algorithm.

Implementing an institutional insulin management algorithm and structured insulin orders should now be viewed as a potent safety intervention as well as an intervention to enhance quality, and we have demonstrated that non‐critical care glycemic control efforts can clearly be a win‐win situation.

Diabetes has reached epidemic proportions in the United States, affecting over 20 million individuals,1 and further rises are expected. A disproportionate increase in diabetes has occurred in the inpatient setting.2 Furthermore, for every 2 patients in the hospital with known diabetes, there may be an additional 1 with newly observed hyperglycemia. Both are common. In 1 report, for example, 24% of inpatients with hyperglycemia had a prior diagnosis of diabetes, whereas another 12% had hyperglycemia without a prior diagnosis of diabetes.3

Although there is a paucity of high quality randomized controlled trials to support tight glycemic control in non‐critical care inpatient settings, poor glycemic control in hospitalized patients is strongly associated with undesirable outcomes for a variety of conditions, including pneumonia,4 cancer chemotherapy,5 renal transplant,6 and postsurgical wound infections.7, 8 Hyperglycemia also induces dehydration, fluid and electrolyte imbalance, gastric motility problems, and venous thromboembolism formation.9

Structured subcutaneous insulin order sets and insulin management protocols have been widely advocated as a method to encourage basal bolus insulin regimens and enhance glycemic control,2, 9, 10 but the effect of these interventions on glycemic control, hypoglycemia, and insulin use patterns in the real world setting has not been well reported. Fear of inducing hypoglycemia is often the main barrier for initiating basal insulin containing regimens and pursuing glycemic targets.2 The evidence would suggest, however, that sliding scale regimens, as opposed to more physiologic basal bolus regimens, may actually increase both hypoglycemic and hyperglycemic excursions.11 A convincing demonstration of the efficacy (improved insulin use patterns and reduced hyperglycemia) and safety (reduced hypoglycemia) of structured insulin order sets and insulin management protocols would foster a more rapid adoption of these strategies.

PATIENTS AND METHODS

In our 400‐bed university hospital, we formed a hospitalist‐led multidisciplinary team in early 2003, with the focus of improving the care delivered to non‐critical care patients with diabetes or hyperglycemia. We used a Plan‐Do‐Study‐Act (PDSA) performance improvement framework, and conducted institutional review board (IRB)‐approved prospective observational research in parallel with the performance improvement efforts, with a waiver for individual informed consent. The study population consisted of all adult inpatients on non‐critical care units with electronically reported point of care (POC) glucose testing from November 2002 through December 2005. We excluded patients who did not have either a discharge diagnosis of Diabetes (ICD 9 codes 250‐251.XX) or demonstrated hyperglycemia (fasting POC glucose >130 mg/dL 2, or a random value of >180 mg/dL) from analysis of glycemic control and hypoglycemia. Women admitted to Obstetrics were excluded. Monthly and quarterly summaries on glycemic control, hypoglycemia, and insulin use patterns (metrics described below) were reported to the improvement team and other groups on a regular basis throughout the intervention period. POC glucose data, demographics, markers of severity of illness, and diagnosis codes were retrieved from the electronic health record.

Interventions

We introduced several interventions and educational efforts throughout the course of our improvement. The 2 key interventions were as follows:

  • Structured subcutaneous insulin order sets (November, 2003).

  • An insulin management algorithm, described below (May 2005).

 

Key Intervention #1: Structured Subcutaneous Insulin Order Set Implementation

In November 2003, we introduced a paper‐based structured subcutaneous insulin order set. This order set encouraged the use of scheduled basal and nutritional insulin, provided guidance for monitoring glucose levels, and for insulin dosing. A hypoglycemia protocol and a standardized correction insulin table were embedded in the order set. This set was similar to examples of structured insulin ordering subsequently presented in the literature.9 In a parallel effort, the University of California, San Diego Medical Center (UCSDMC) was developing a computer physician order entry (CPOE) module for our comprehensive clinical information system, Invision (Siemens Medical Systems, Malvern, PA), that heretofore had primarily focused on result review, patient schedule management, and nursing documentation. In anticipation of CPOE and for the purpose of standardization, we removed outdated sliding scale insulin regimens from a variety preexisting order sets and inserted references to the standardized subcutaneous insulin order set in their stead. The medication administration record (MAR) was changed to reflect the basal/nutritional/correction insulin terminology. It became more difficult to order a stand‐alone insulin sliding scale even before CPOE versions became available. The standardized order set was the only preprinted correction scale insulin order available, and ordering physicians have to specifically opt out of basal and nutritional insulin choices to order sliding scale only regimens. Verbal orders for correction dose scales were deemed unacceptable by medical staff committees. Correctional insulin doses could be ordered as a 1‐time order, but the pharmacy rejected ongoing insulin orders that were not entered on the structured form.

We introduced our first standardized CPOE subcutaneous insulin order set in January 2004 at the smaller of our 2 campuses, and subsequently completed full deployment across both campuses in all adult medical‐surgical care areas by September 2004.

The CPOE version, like the paper version that immediately preceded it, encouraged the use of basal/bolus insulin regimens, promoted the terms basal, nutritional or premeal, and adjustment dose insulin in the order sets and the medication administration record, and was mandatory for providers wishing to order anything but a 1‐time order of insulin. Figure 1 depicts a screen shot of the CPOE version. Similar to the paper version, the ordering physician had to specifically opt out of ordering scheduled premeal and basal insulin to order a sliding scale only regimen. The first screen also ensured that appropriate POC glucose monitoring was ordered and endorsed a standing hypoglycemia protocol order. The CPOE version had only a few additional features not possible on paper. Obvious benefits included elimination of unapproved abbreviations and handwriting errors. Nutritional and correction insulin types were forced to be identical. Fundamentally, however, both the paper and online structured ordering experiences had the same degree of control over provider ordering patterns, and there was no increment in guidance for choosing insulin regimens, hence their combined analysis as structured orders.

Figure 1
Screen shot: Computerized physician order entry version of structured insulin orders.

Key Intervention #2: Insulin Management Algorithm

The structured insulin order set had many advantages, but also had many limitations. Guidance for preferred insulin regimens for patients in different nutritional situations was not inherent in the order set, and all basal and nutritional insulin options were offered as equally acceptable choices. The order set gave very general guidance for insulin dosing, but did not calculate insulin doses or assist in the apportionment of insulin between basal and nutritional components, and guidance for setting a glycemic target or adjusting insulin was lacking.

Recognizing these limitations, we devised an insulin management algorithm to provide guidance incremental to that offered in the order set. In April 2005, 3 hospitalists piloted a paper‐based insulin management algorithm (Figure 2, front; Figure 3, reverse) on their teaching services. This 1‐page algorithm provided guidance on insulin dosing and monitoring, and provided institutionally preferred insulin regimens for patients in different nutritional situations. As an example, of the several acceptable subcutaneous insulin regimens that an eating patient might use in the inpatient setting, we advocated the use of 1 preferred regimen (a relatively peakless, long‐acting basal insulin once a day, along with a rapid acting analog nutritional insulin with each meal). We introduced the concept of a ward glycemic target, provided prompts for diabetes education, and generally recommended discontinuation of oral hypoglycemic agents in the inpatient setting. The hospitalists were introduced to the concepts and the algorithm via 1 of the authors (G.M.) in a 1‐hour session. The algorithm was introduced on each teaching team during routine teaching rounds with a slide set (approximately 15 slides) that outlined the basic principles of insulin dosing, and gave example cases which modeled the proper use of the algorithm. The principles were reinforced on daily patient work rounds as they were applied on inpatients with hyperglycemia. The pilot results on 25 patients, compared to 250 historical control patients, were very promising, with markedly improved glycemic control and no increase in hypoglycemia. We therefore sought to spread the use of the algorithm. In May 2005 the insulin management algorithm and teaching slide set were promoted on all 7 hospitalist‐run services, and the results of the pilot and concepts of the algorithm were shared with a variety of house staff and service leaders in approximately a dozen sessions: educational grand rounds, assorted noon lectures, and subsequently, at new intern orientations. Easy access to the algorithm was assured by providing a link to the file within the CPOE insulin order set.

Figure 2
Insulin management algorithm (front) introduced at UCSD in May 2005 (marking the onset of Time Period 3).
Figure 3
Insulin management algorithm (reverse) introduced at UCSD in May 2005 (marking the onset of Time Period 3).

Other Attempts to Improve Care

Several other issues were addressed in the context of the larger performance improvement effort by the team. In many cases, hard data were not gathered to assess the effectiveness of the interventions, or the interventions were ongoing and could be considered the background milieu for the key interventions listed above.

During each intervention, education sessions were given throughout the hospital to staff, including physicians, residents, and nurses, using departmental grand rounds, nursing rounds, and in‐services to describe the process and goals. Patient education programs were also redesigned and implemented, using preprinted brochure. Front‐line nursing staff teaching skills were bolstered via Clinical Nurse Specialist educational sessions, and the use of a template for patient teaching. The educational template assessed patient readiness to learn, home environment, current knowledge, and other factors. Approximately 6 conferences directed at various physician staff per year became part of the regular curriculum.

We recognized that there was often poor coordination between glucose monitoring, nutrition delivery, and insulin administration. The traditional nursing practice of the 6:00 AM finger stick and insulin administration was changed to match a formalized nutrition delivery schedule. Nutrition services and nursing were engaged to address timeliness of nutrition delivery, insulin administration, and POC glucose documentation in the electronic health record.

Feedback to individual medicine resident teams on reaching glycemic targets, with movie ticket/coffee coupon rewards to high performing teams, was tried from April 2004 to September 2004.

Measures and Analyses

Assessing Insulin Use Patterns

A convenience sample gathering all subcutaneous insulin orders from 4 to 5 selected days per month yielded 70 to 90 subcutaneous insulin orders for review each month. Sampling was originally performed each month, followed by less frequent sampling once stability in insulin use patterns was reached. Regimens were categorized by pharmacy and hospitalist review as to whether basal insulin was part of the insulin regimen or not. The percentage of insulin regimens incorporating basal insulin was calculated for each sampled month and followed in run charts, and comparisons between preorder set and postorder set time periods were made using Pearson's chi square statistic.

Assessing Glycemic Control

Glycemic control and hypoglycemia parameters were monitored for the entire 38‐month observation period.

Routinely monitored POC glucose values were used to assess glycemic control. During the initial data examination, it was found after 14 days of the hospital stay, there was a notable stabilization and improvement in glucose control and fewer hypoglycemic events, therefore we examined only the first 14 days of hospitalization, thereby eliminating a potential source of bias from length of stay outliers.

A mean glucose value was recorded for each patient‐day with 1 or more recorded values. Glycemic control for each patient‐stay was calculated by averaging the patient‐day mean values, which we will refer to as the day‐weighted mean. Hypoglycemic values (60 mg/dL) were excluded from calculation of the mean glucose, to avoid equating frequent hypoglycemia with optimal glycemic control. An uncontrolled patient‐day was defined as a monitored patient‐day with a mean glucose 180 mg/dL. An uncontrolled patient‐stay is defined as a patient‐stay with a day‐weighted mean glucose value 180 mg/dL.

We theorized that the greatest impact of the interventions would be realized in patients with longer monitoring periods, and that those with only a few POC glucose values could potentially misrepresent the impact of our interventions: therefore we performed a second analysis restricted to patients with 8 POC glucose values.

Assessing Hypoglycemia

Hypoglycemia was defined as a glucose 60 mg/dL, and severe hypoglycemia was defined as a glucose 40 mg/dL. These parameters were characterized by 2 methods. First, we calculated the percentage of monitored patients suffering from 1 or more hypoglycemic events or severe hypoglycemic events over the course of their entire admission. A second method tracked the percentage of monitored patient‐days with hypoglycemia and severe hypoglycemia, thereby correcting for potential misinterpretation from clustered repeated measures or variable length of stay. As with the glycemic control analysis, we repeated the hypoglycemia analysis in the subset of patients with 8 POC glucose values.

Summary Analysis of Glycemic Control and Hypoglycemia

Pearson chi square values, with relative risks (RRs) and 95% confidence intervals (CIs) were calculated to compare glycemic control and hypoglycemia in the 2 key interventions and baseline. The interventions and data reporting were grouped as follows:

  • Baseline: November 2002 to October 2003) = Time Period 1 (TP1)

  • Structured Order Set: November 2003 to April 2005) = Time Period 2 (TP2)

  • Algorithm plus Structured Order Set: May 2005 to December 2005) = Time Period 3 (TP3)

 

A P value of less than 0.05 was determined as significant and data were analyzed using STATA, Version 8 (STATA Corp., College Station, TX).

We assigned the RR of uncontrolled hyperglycemia and the RR of hypoglycemia during the baseline time (TP1) with values of 1.0, and calculated the RR and CIs for the same parameters during TP2 and TP3.

RESULTS

Just over 11,000 patients were identified for POC glucose testing over the 38 month observation period. Of these, 9314 patients had either a diagnosis of diabetes or documented hyperglycemia. The characteristics of this study population are depicted in Table 1. There were no differences between the groups and the demographics of age, gender, or length of stay (P > 0.05 for all parameters). There was a slight increase in the percent of patients with any intensive care unit days over the 3 time periods and a similar increase in the case mix index.

Population Characteristics: Patients with a Diagnosis of Diabetes Mellitus or Documented Hyperglycemia
Patients Meeting Criteria of Diabetes Mellitus Diagnosis or Hyperglycemia (n = 9,314 patients)BaselineTP2TP3
  • P < 0.02 Pearson chi square.

  • P < 0.001 analysis of variance between the 3 time periods.

Time period (TP)November 2002 to October 2003November 2003 to April 2005May 2005 to December 2005
Monitored patient days (44,232)11,57121,12611,535
Number of patients (9,314)2,5044,5152,295
Males (%)555456
Average age standard deviation56 1756 1756 16
Length of stay (excluding highest 1% of outliers)4.6 5.94.6 5.74.8 5.8
% With any intensive care unit days*182022
Case mix index score (mean SD)1.8 2.12.0 2.32.1 2.1
Case mix index (median score)1.11.31.3

Of the 9314 study patients, 5530 had 8 or more POC glucose values, and were included in a secondary analysis of glycemic control and hypoglycemia.

Insulin Use Patterns

Figure 4 demonstrates the dramatic improvement that took place with the introduction of the structured order set. In the 6 months preceding the introduction of the structured insulin order set (May‐October 2003) 72% of 477 sampled patients with insulin orders were on sliding scale‐only insulin regimens (with no basal insulin), compared to just 26% of 499 patients sampled in the March to August 2004 time period subsequent to order set implementation (P < .0001, chi square statistic). Intermittent monthly checks on insulin use patterns reveal this change has been sustained.

Figure 4
Percent of patients on subcutaneous insulin orders that are sliding scale–only, without any basal insulin component.

Glycemic Control

A total of 9314 patients with 44,232 monitored patient‐days and over 120,000 POC glucose values were analyzed to assess glycemic control, which was improved with structured insulin orders and improved incrementally with the introduction of the insulin management algorithm.

The percent of patient‐days that were uncontrolled, defined as a monitored day with a mean glucose of 180 mg/dL, was reduced over the 3 time periods (37.8% versus 33.9% versus 30.1%, P < 0.005, Pearson chi square statistic), representing a 21% RR reduction of uncontrolled patient‐days from TP1 versus TP3. Table 2 shows the summary results for glycemic control, including the RR and CIs between the 3 time periods.

Glycemic Control Summary for 9,314 Patients with a Diagnosis of Diabetes Mellitus or Documented Hyperglycemia
Time Period (TP)BaselineTP2 Structured OrdersTP3 Orders Plus AlgorithmRelative Risk TP3:TP2
  • An uncontrolled patient‐day is defined as a monitored patient day with a mean glucose of 180 mg/dL.

  • P value of <0.005.

  • An uncontrolled patient‐stay is defined as a patient‐stay with a day‐weighted mean glucose value of 180 mg/dL.

Patient‐day glucose    
Mean SD179 66170 65165 58 
Median160155151 
Uncontrolled patient‐days*4,3727,1623,465 
Monitored patient‐days11,55521,13511,531 
% Uncontrolled patient‐days37.833.930.1 
RR: uncontrolled patient‐day (95% confidence interval)1.00.89 (0.87‐0.92)0.79 (0.77‐0.82)0.89 (0.86‐0.92)
Glycemic control by patient‐stay    
Day‐weighted mean SD177 57174 54170 50 
Day‐weighted median167162158 
Uncontrolled patient‐stay (%)1,0381,696784 
Monitored patient‐stay2,5044,5152,295 
% Uncontrolled patient‐stays41.537.634.2 
RR: uncontrolled patient‐stay (95% confidence interval) 0.91 (0.85‐0.96)0.84 (0.77‐0.89)0.91 (0.85‐0.97)

In a similar fashion, the percent of patients with uncontrolled patient‐stays (day‐weighted mean glucose 180 mg/dL) was also reduced over the 3 time periods (41.5% versus 37.6% versus 34.2%, P < 0.05, Pearson chi square statistic, with an RR reduction of 16% for TP3:TP1). Figure 5 depicts a statistical process control chart of the percent of patients experiencing uncontrolled patient‐stays over time, and is more effective in displaying the temporal relationship of the interventions with the improved results.

Figure 5
Statistical process control chart, tracking percent of patient‐stays that are “uncontrolled” (day‐weighted mean ≥180 mg/dL). For complete glycemic control results see Tables 2 and 3.

Uncontrolled hyperglycemic days and stays were reduced incrementally from TP3 versus TP2, reflecting the added benefit of the insulin management algorithm, compared to the benefit enjoyed with the structured order set alone.

When the analyses were repeated after excluding patients with fewer than 8 POC glucose readings (Table 3), the findings were similar, but as predicted, the effect was slightly more pronounced, with a 23% relative reduction in uncontrolled patient‐days and a 27% reduction in uncontrolled patient‐stays of TP3 versus TP1.

Glycemic Control Summary for 5530 Patients with a Diagnosis of Diabetes Mellitus or Documented Hyperglycemia and 8 POC Glucose Values Available
Time Period (TP)BaselineTP2 Structured OrdersTP3 Orders Plus AlgorithmRelative Risk TP3:TP2
  • An uncontrolled patient‐day is defined as a monitored patient day with a mean glucose of 180 mg/dL.

  • P value of <0.005.

  • An uncontrolled patient‐stay is defined as a patient‐stay with a day‐weighted mean glucose value of 180 mg/dL.

Patient‐day glucose    
Mean SD172 65169 64163 57 
Median159154149 
Uncontrolled patient‐days*3,4695,6392,766 
Monitored patient‐days9,30417,2789,671 
% Uncontrolled patient‐days37.332.628.6 
RR: uncontrolled patient‐day (95% confidence interval)1.00.87 (0.85‐0.90)0.77 (0.74‐0.80)0.88 (0.84‐0.91)
Glycemic control by patient‐stay    
Day‐weighted mean SD175 51169 47166 45 
Day‐weighted median167158155 
Uncontrolled patient‐stay (%)588908425 
Monitored patient‐stay1,4392,6591,426 
% Uncontrolled patient‐stays40.134.129.8 
RR: Uncontrolled patient‐stay (95% confidence interval) 0.84 (0.77‐0.91)0.73 (0.66‐0.81)0.87 (0.79‐0.96)

Hypoglycemia

Table 4 summarizes the results for hypoglycemia and severe hypoglycemia in the study population, and Table 5 summarizes the secondary analyses of hypoglycemia in the subset with at least 8 POC glucose readings.

Hypoglycemia Summary for 9,314 Patients with Diabetes Mellitus or Documented Hyperglycemia
TP (Time Period)BaselineTP2TP3Relative Risk TP3:TP2
  • NOTE: Hypoglycemia is defined as a glucose 60 mg/dL, severe hypoglycemia is defined as a glucose 40 mg/dL.

  • Abbreviations: RR, relative risk; CI, 95% confidence interval.

Monitored patient‐stays250445152295 
Stays with hypoglycemia (%)296 (11.8)437 (9.7)210 (9.2) 
RR hypoglycemic stay (CI)1.00.82 (0.72‐0.94)0.77 (0.65‐0.92)0.95 (0.81‐1.10)
Stays with severe hypoglycemia (%)73 (2.9)96 (2.1)55 (2.4) 
RR severe hypoglycemic stay (CI)1.00.73 (0.54‐0.98)0.82 (0.58‐1.16)1.13 (0.81‐1.56)
Monitored patient‐days11,58421,15811,548 
Days with hypoglycemia (%)441 (3.8)623 (2.9)300 (2.6) 
RR hypoglycemic day (CI)1.00.77 (0.69‐0.87)0.68 (0.59‐0.78)0.88 (0.77‐1.01)
Days with severe hypoglycemia (%)86 (0.74)109 (0.52)66 (0.57) 
RR Severe hypoglycemic day (CI)1.00.69 (0.52‐0.92)0.77 (0.56‐1.06)1.10 (0.82‐1.5)
Hypoglycemia Summary for 5,530 Patients with Diabetes Mellitus or Documented Hyperglycemia and 8 Point of Care Glucose Values Available for Analysis
TP (Time Period)BaselineTP2TP3Relative Risk TP3:TP2
  • NOTE: Hypoglycemia is defined as a glucose 60 mg/dL and severe hypoglycemia is defined as a glucose 40 mg/dL.

  • Abbreviations: RR, relative risk; CI, 95% confidence interval.

Monitored patient‐stays144026641426 
Stays with hypoglycemia (%)237 (16.5)384 (14.4)180 (12.6) 
RR hypoglycemic stay (CI)1.00.88 (0.76‐1.02)0.77 (0.64‐0.92)0.88 (0.75‐1.03)
Stays with severe hypoglycemia (%)58 (4.0)93 (3.5)47 (3.3) 
RR severe hypoglycemic stay (CI)1.00.87 (0.63‐1.2)0.82 (0.56‐1.19)0.94 (0.67‐1.33)
Monitored patient‐days9,31717,3109,684 
Days with hypoglycemia (%)379 (4.1)569 (3.3)269 (2.7) 
RR hypoglycemic day (CI)1.00.81 (0.71‐0.92)0.68 (0.59‐0.80)0.85 (0.73‐0.98)
Days with severe hypoglycemia (%)71 (0.76)106 (0.61)58 (0.60) 
RR severe hypoglycemic day (CI)1.00.80 (0.60‐1.08)0.79 (0.56‐1.11)0.98 (0.71‐1.34)

Analysis by Patient‐Stay

The percent of patients that suffered 1 or more hypoglycemic event over the course of their inpatient stay was 11.8% in TP1, 9.7% in TP2, and 9.2% in TP3. The RR of a patient suffering from a hypoglycemic event was significantly improved in the intervention time periods compared to baseline, with the RR of TP3:TP1 = 0.77 (CI, 0.65‐0.92). There was a strong trend for incremental improvement in hypoglycemic patient‐stays for TP3 versus TP2, but the trend just missed statistical significance (P < 0.07). Similar trends in improvement were found for severe hypoglycemia by patient‐stay, but these trends were only statistically significant for TP2 versus TP1. The findings were similar in the subset of patients with at least 8 POC glucose readings (Table 5).

Analysis by Patient‐Day

Of monitored patient days in the baseline TP1, 3.8% contained a hypoglycemic value of 60 mg/dL. With the introduction of structured insulin orders in TP2, this was reduced to 2.9%, and in TP3 it was 2.6%. The RR of a hypoglycemic patient‐day of TP2 compared to TP1 was 0.77 (CI, 0.69‐0.87), whereas the cumulative impact of the structured order set and algorithm (TP3:TP1) was 0.68 (CI, 0.59‐0.78), representing a 32% reduction of the baseline risk of suffering from a hypoglycemic day. Similar reductions were seen for the risk of a severe hypoglycemic patient‐day.

The secondary analysis of hypoglycemic and severe hypoglycemic patient‐days showed very similar results, except that the TP3:TP2 RR for hypoglycemia of 0.85 (CI, 0.73‐0.98) reached statistical significance, again demonstrating the incrementally beneficial effect of the insulin management algorithm.

DISCUSSION

Our study convincingly demonstrates that significant improvement in glycemic control can be achieved with implementation of structured subcutaneous insulin orders and a simple insulin management protocol. Perhaps more importantly, these gains in glycemic control are not gained at the expense of increased iatrogenic hypoglycemia, and in fact, we observed a 32% decline in the percent of patient‐days with hypoglycemia. This is extremely important because fear of hypoglycemia is the most significant barrier to glycemic control efforts.

Strengths and Limitations

Our study has several strengths. The study is large and incorporates all patients with diabetes or hyperglycemia captured by POC glucose testing, and the observation period is long enough that bias from merely being observed is not a factor. We used metrics for glycemic control, hypoglycemia, and insulin use patterns that are of high quality and are generally in line with the Society of Hospital Medicine (SHM) Glycemic Control Task force recommendations,12, 13 and examined data by both patient‐stay and patient‐day.

The increased use of anticipatory physiologic subcutaneous insulin regimens, and the subsequent decline in the use of sliding scale insulin, is the most likely mechanism for improvement. The improvements seen are fairly dramatic for an institution in absolute terms, because inpatient hyperglycemia and hypoglycemia are so common. For example, on an annualized basis for our 400‐bed medical center, these interventions prevent 124 patients from experiencing 208 hypoglycemic days.

Other institutions should be able to replicate our results. We received administrative support to create a multidisciplinary steering committee, but we did not have incremental resources to create a dedicated team for insulin management, mandated endocrinology comanagement or consultations, or manual data collection. In fact, we had only 1 diabetes educator for 400 adult beds at 2 sites, and were relatively underresourced in this area by community standards. There was some time and expense in creating the glycemic control reports, but all of the glucose data collected were part of normal care, and the data retrieval became automated.

The main limitation of this study lies in the observational study design. There were multiple interventions in addition to structured insulin orders and the insulin management algorithm, and these educational and organizational changes undoubtedly also contributed to the overall success of our program. Since we did not perform a randomized controlled trial, the reader might reasonably question if the structured order sets and insulin management algorithm were actually the cause of the improvement seen, as opposed to these ancillary efforts or secular change. However, there are several factors that make this unlikely. First, the study population was well‐defined, having diabetes or documented hyperglycemia in all 3 time periods. Second, the demographics remained constant or actually worked against improvement trends, since the markers of patient acuity suggest increased patient acuity over the observation period. Third, the temporal relationship of the improvement to the introduction of our key interventions, as viewed on statistical process control charts shown in Figure 5, strongly suggest a causal relationship. This temporal relationship was consistently observed no matter how we chose to define uncontrolled hyperglycemia, and was also seen on hypoglycemia control charts. We view the ancillary interventions (such as educational efforts) as necessary, but not sufficient, in and of themselves, to effect major improvement.

We did not analyze the impact of the improved glycemic control on patient outcomes. In the absence of a randomized controlled trial design, controlling for the various confounders is a challenging task. Also, it is likely that not all hypoglycemic events were attributable to inpatient glycemic control regimens, though the secondary analysis probably eliminated many hypoglycemia admissions.

Lessons Learned: Implications from our study

We agree with the American Association of Clinical Endocrinologists (AACE)/American Diabetes Association (ADA)2 and the SHM Glycemic Control Task Force12 about the essential elements needed for successful implementation of inpatient glycemic control programs:

  • An appropriate level of administrative support.

  • Formation of a multidisciplinary steering committee to drive the development of initiatives, empowered to enact changes.

  • Assessment of current processes, quality of care, and barriers to practice change.

  • Development and implementation of interventions, including standardized order sets, protocols, policies, and algorithms with associated educational programs.

  • Metrics for evaluation of glycemic control, hypoglycemia, insulin use patterns, and other aspects of care.

 

Metrics to follow hypoglycemia are extremely important. The voluntary reporting on insulin‐induced hypoglycemia fluctuated widely over the course of our project. These fluctuations did not correlate well with the more objective and accurate measures we followed, and this objective data was very helpful in reducing the fear of hypoglycemia, and spreading the wider use of basal bolus insulin regimens. We strongly recommend that improvement teams formulate and follow measures of glycemic control, hypoglycemia, and insulin use, similar to those outlined in the SHM Glycemic Control Improvement Guide12 and the SHM Glycemic Control Task Force summary on glucometrics.13

Although we introduced our structured insulin order set first, with a long lag time until we introduced the insulin management algorithm, we advocate a different approach for institutions grappling with these issues. This approach is well‐described by the SHM Glycemic Control Task Force.14 An insulin management algorithm should be crafted first, integrating guidance for insulin dosing, preferred insulin regimens for different nutritional situations, a glycemic target, insulin dosing adjustment, glucose monitoring, and prompts for ordering a glycosylated hemoglobin (A1c) level. Next, the order set and the supporting educational programs should integrate this guidance as much as possible, making the key guidance available at the point of patient care.

This guidance was available in our algorithm but was not inherent in the structured insulin orders described in this report, and all basal and nutritional insulin options were offered as equally acceptable choices. This version did not calculate insulin doses or assist in the apportionment of insulin between basal and nutritional components. Only a single adjustment dose scale was offered, leaving appropriate modifications up to the end user, and from a usability standpoint, our CPOE insulin orders lacked dynamic flexibility (revising a single insulin required discontinuing all prior orders and reentering all orders). These limitations have subsequently been addressed with Version 2 of our CPOE insulin orders, and the details will soon be available in the literature.15

We are now exploring further improvement with concurrent identification and intervention of hyperglycemic patients that are not on physiologic insulin regimens or not meeting glycemic targets, and implementing protocols addressing the transition from infusion insulin.

CONCLUSION

We significantly improved glycemic control and simultaneously reduced hypoglycemia across all major medical and surgical services at our medical center, thereby addressing the number 1 barrier to improved inpatient glycemic control. We achieved this via systems changes with the introduction of structured subcutaneous insulin orders and the insulin management algorithm, along with education, but did not otherwise mandate or monitor adherence to our algorithm.

Implementing an institutional insulin management algorithm and structured insulin orders should now be viewed as a potent safety intervention as well as an intervention to enhance quality, and we have demonstrated that non‐critical care glycemic control efforts can clearly be a win‐win situation.

References
  1. Centers for Disease Control and Prevention.National Diabetes Fact Sheet: General Information and National Estimates on Diabetes in the United States, 2002.Atlanta, GA:U.S. Department of Health and Human Services, Centers for Disease Control and Prevention;2003. Available at: www.cdc.gov/diabetes/pubs/factsheet.htm. Accessed January 21, 2006.
  2. American College of Endocrinology and American Diabetes Association Consensus statement on inpatient diabetes and glycemic control: a call to action.Diabetes Care.2006;29:19551962.
  3. Umpierrez GE,Isaacs SD,Bazargan N, et al.Hyperglycemia: an independent marker of in‐hospital mortality in patients with undiagnosed diabetes.J Clin Endocrinol Metab.2002;87:978982.
  4. McAlister FA,Majumdar SR,Blitz S, et al.The relation between hyperglycemia and outcomes in 2471 patients admitted to the hospital with community‐acquired pneumonia.Diabetes Care.2005;28:810815.
  5. Weiser MA,Cabanillas ME,Konopleva M, et al.Cancer.2004;100:11791185.
  6. Thomas MC,Mathew TH,Russ GR, et al.Early perioperative glycaemic control and allograft rejection in patients with diabetes mellitus: a pilot study.Transplantation.2001;72:13211324.
  7. Pomposelli JJ,Baxter JK,Babineau TJ, et al.Early postoperative glucose control predicts nosocomial infection rate in diabetic patients.J Parenter Enteral Nutr.1998;22:7781.
  8. Zerr KJ,Furnary AP,Grunkemeier GL, et al.Glucose control lowers the risk of wound infection in diabetics after open heart operations.Ann Thorac Surg.1997;63:356361.
  9. Clement S,Braithwaite SS,Magee MF, et al.Management of diabetes and hyperglycemia in hospitals.Diabetes Care.2004;27:553591.
  10. Garber AJ,Moghissi ES,Bransome ED, et al.American College of Endocrinology position statement on inpatient diabetes and metabolic control.Endocr Pract.2004;10:7782.
  11. Umpierrez G,Maynard G.Glycemic chaos (not glycemic control) still the rule for inpatient care: how do we stop the insanity? [Editorial].J Hosp Med.2006;1:141144.
  12. Society of Hospital Medicine Glycemic Control Task Force: Optimizing Glycemic Control and Reducing Hypoglycemia at Your Medical Center. Society of Hospital Medicine, Glycemic Control Quality Improvement Resource Room. Available at: http://www.hospitalmedicine.org/ResourceRoomRedesign/GlycemicControl.cfm. Accessed October2008.
  13. Schnipper JL,Magee MF,Inzucchi SE,Magee MF,Larsen K,Maynard G.SHM Glycemic Control Task Force summary: practical recommendations for assessing the impact of glycemic control efforts.J Hosp Med.2008;3(S5):6675.
  14. Maynard G,Wesorick DH,O'Malley CW,Inzucchi SE;for the SHM Glycemic Control Task Force.Subcutaneous insulin order sets and protocols: effective design and implementation strategies.J Hosp Med.2008;3(S5):2941.
  15. Lee J,Clay B,Zelazny Z,Maynard G.Indication‐based ordering: a new paradigm for glycemic control in hospitalized inpatients.J Diabetes Sci Tech.2008;2(3):349356.
References
  1. Centers for Disease Control and Prevention.National Diabetes Fact Sheet: General Information and National Estimates on Diabetes in the United States, 2002.Atlanta, GA:U.S. Department of Health and Human Services, Centers for Disease Control and Prevention;2003. Available at: www.cdc.gov/diabetes/pubs/factsheet.htm. Accessed January 21, 2006.
  2. American College of Endocrinology and American Diabetes Association Consensus statement on inpatient diabetes and glycemic control: a call to action.Diabetes Care.2006;29:19551962.
  3. Umpierrez GE,Isaacs SD,Bazargan N, et al.Hyperglycemia: an independent marker of in‐hospital mortality in patients with undiagnosed diabetes.J Clin Endocrinol Metab.2002;87:978982.
  4. McAlister FA,Majumdar SR,Blitz S, et al.The relation between hyperglycemia and outcomes in 2471 patients admitted to the hospital with community‐acquired pneumonia.Diabetes Care.2005;28:810815.
  5. Weiser MA,Cabanillas ME,Konopleva M, et al.Cancer.2004;100:11791185.
  6. Thomas MC,Mathew TH,Russ GR, et al.Early perioperative glycaemic control and allograft rejection in patients with diabetes mellitus: a pilot study.Transplantation.2001;72:13211324.
  7. Pomposelli JJ,Baxter JK,Babineau TJ, et al.Early postoperative glucose control predicts nosocomial infection rate in diabetic patients.J Parenter Enteral Nutr.1998;22:7781.
  8. Zerr KJ,Furnary AP,Grunkemeier GL, et al.Glucose control lowers the risk of wound infection in diabetics after open heart operations.Ann Thorac Surg.1997;63:356361.
  9. Clement S,Braithwaite SS,Magee MF, et al.Management of diabetes and hyperglycemia in hospitals.Diabetes Care.2004;27:553591.
  10. Garber AJ,Moghissi ES,Bransome ED, et al.American College of Endocrinology position statement on inpatient diabetes and metabolic control.Endocr Pract.2004;10:7782.
  11. Umpierrez G,Maynard G.Glycemic chaos (not glycemic control) still the rule for inpatient care: how do we stop the insanity? [Editorial].J Hosp Med.2006;1:141144.
  12. Society of Hospital Medicine Glycemic Control Task Force: Optimizing Glycemic Control and Reducing Hypoglycemia at Your Medical Center. Society of Hospital Medicine, Glycemic Control Quality Improvement Resource Room. Available at: http://www.hospitalmedicine.org/ResourceRoomRedesign/GlycemicControl.cfm. Accessed October2008.
  13. Schnipper JL,Magee MF,Inzucchi SE,Magee MF,Larsen K,Maynard G.SHM Glycemic Control Task Force summary: practical recommendations for assessing the impact of glycemic control efforts.J Hosp Med.2008;3(S5):6675.
  14. Maynard G,Wesorick DH,O'Malley CW,Inzucchi SE;for the SHM Glycemic Control Task Force.Subcutaneous insulin order sets and protocols: effective design and implementation strategies.J Hosp Med.2008;3(S5):2941.
  15. Lee J,Clay B,Zelazny Z,Maynard G.Indication‐based ordering: a new paradigm for glycemic control in hospitalized inpatients.J Diabetes Sci Tech.2008;2(3):349356.
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Journal of Hospital Medicine - 4(1)
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Journal of Hospital Medicine - 4(1)
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3-15
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Improved inpatient use of basal insulin, reduced hypoglycemia, and improved glycemic control: Effect of structured subcutaneous insulin orders and an insulin management algorithm
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Improved inpatient use of basal insulin, reduced hypoglycemia, and improved glycemic control: Effect of structured subcutaneous insulin orders and an insulin management algorithm
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diabetes mellitus, glycemic control, hypoglycemia, insulin, patient safety, quality improvement
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