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Email
anamin@hs.uci.edu
Given name(s)
Alpesh
Family name
Amin
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MD

Low Rates of Stethoscope Hygiene

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The third hand: Low rates of stethoscope hygiene on general medical services

Hand hygiene is a proven and guideline‐recommended safety practice, although clinicians and particularly physicians are unreliable at performing it.[1] Like hands, stethoscopes can carry pathogens from patient to patient. In 1 study, stethoscopes were as likely to be contaminated after use with methicillin‐resistant Staphylococcus aureuspositive patients as the provider's hands.[2] Furthermore, like hands, stethoscopes can be effectively decolonized with alcohol.[3, 4] However, although hand hygiene rates have been extensively studied,[1] and hand hygiene has been linked to reductions in nosocomial infection,[5] stethoscope hygiene is less well studied and emphasized less by guidelines.[6] Several surveys have documented low self‐reported compliance with stethoscope hygiene.[7, 8, 9, 10] Of 150 healthcare workers, 48% reported stethoscope hygiene between daily and weekly, 37% did stethoscope hygiene monthly, and 7% did stethoscope hygiene annually or never.[8] Of 1401 doctors asked about their stethoscope hygiene beliefs and practices, 76% believed that stethoscopes could transmit infection, but only 24% reported cleaning their scopes regularly.[9] Moreover, of 308 students, 22% had never done stethoscope hygiene, and <4% did it consistently.[10] However, we were unable to find any data on observed rates of stethoscope hygiene. Thus, we observed student and trainee physician stethoscope hygiene performance during hospital medicine rotations as part of the baseline data‐collection phase of a quality‐improvement effort linked to hand hygiene efforts.

METHODS

Attending hospitalists (I.H.J., B.M., and A.A.) and 1 graduate assistant (J.W.) at 3 sites observed stethoscope hygiene opportunities over an 11‐month period. Stethoscope hygiene was counted as performed if a patient‐specific stethoscope was used in an isolation room, or if any type of cleaning (alcohol gel, alcohol wipe, or cleansing cloth) was performed on a stethoscope carried out of the room. Observers also recorded whether stethoscope hygiene opportunities occurred in isolation rooms or nonisolation rooms, and noted if stethoscope hygiene was obviously triggered by an attending's stethoscope hygiene behavior (eg, a trainee asked an attending why he performed stethoscope hygiene, then performed it him or herself). Trainees were not aware that their stethoscope hygiene behaviors were being recorded.

RESULTS

We observed 352 opportunities for stethoscope hygiene, in which doctors or students used stethoscope hygiene in 58 encounters (16%). Twenty of the 58 stethoscope hygiene events occurred only after a trainee observed an attending physician perform stethoscope hygiene. Eliminating stethoscope hygiene events that were triggered by attending physicians, stethoscope hygiene was performed in 38 of 332 opportunities (11%). There was a significant difference between the rate of stethoscope hygiene performed in isolation versus nonisolation rooms: 24/29 (82.7%) versus 14 of 303 (4.6%) (P<0.001 by Pearson 2 statistic). In isolation room stethoscope hygiene, in which the type of hygiene was recorded, 18 of 20 (90%) involved use of an isolation stethoscope, and 2 of 20 (10%) involved cleaning of a personal stethoscope.

DISCUSSION

Stethoscope hygiene is rarely performed by trainees. Stethoscope hygiene performance depends on the isolation status of the patient, with more than 80% performance in isolated patients and <5% in nonisolated patients.

Although little is known about the rate of infection related to stethoscopes, colonization of stethoscopes with nosocomial bacteria is well described.[2] Transmission of pathogens from patient to patient by stethoscopes could undermine the benefits of hand hygiene programs, as patients are commonly exposed to unclean stethoscopes.

Our observations are limited by several factors. We used a convenience sample of general medicine trainee behavior at academic medical centers; the behavior of attending physicians, ancillary staff, and nonacademic physicians may be different. Moreover, attending behavior may have prompted more episodes of stethoscope hygiene performance than we recorded, because we only noted when stethoscope hygiene was clearly related to attending behavior. The very low rate of stethoscope hygiene after contact with nonisolation patients represents a current and potentially serious safety threat. Future research might be able to quantify the risk associated with uncleaned stethoscopes or demonstrate the effectiveness of stethoscope hygiene programs. The effect of modeling on hand hygiene and stethoscope hygiene[10, 11] and on stethoscope hygiene in our data suggests a method for improving stethoscope hygiene.

Disclosure

Nothing to report.

Files
References
  1. World Health Organization. WHO Guidelines on Hand Hygiene in HealthCare. Global Patient Safety Challenge 2005‐2006: Clean Care Is Safer Care. Geneva, Switzerland: WHO Press; 2009.
  2. Longtin Y, Schneider A, Tschopp C, et al. Contamination of stethoscopes and physicians' hands after a physical examination. Mayo Clin Proc. 2014;89:291299.
  3. Bernard L, Kereveur A, Durand D, et al. Bacterial contamination of hospital physicians’ stethoscopes. Infect Control Hosp Epidemiol. 1999;20:626628.
  4. Schroeder A, Schroeder MA, D'Amico F. What's growing on your stethoscope? (and what you can do about it). J Fam Pract. 2009;58(8):404409.
  5. Pittet D, Hugonnet S, Harbarth S, et al. Effectiveness of a hospital wide program aimed at improving compliance with hand hygiene. Lancet. 2000;356:13071312.
  6. Bearman G, Bryant K, Leekha S, et al. Healthcare personnel attire in non‐operating‐room settings. Infect Control Hosp Epidemiol. 2014;35:107121.
  7. Breathnach AS, Jenkins DR, Pedler SJ. Stethoscopes as possible vectors of infection by staphylococci. BMJ. 1992;305:15731574.
  8. Jones JS, Hoerle D, Riekse R. Stethoscopes: a potential vector of infection? Ann Emerg Med. 1995;26:296299.
  9. Muniz J, Sethi RK, Zaghi J, Ziniel SI, Sandora TJ. Predictors of stethoscope disinfection among pediatric health care providers. Am J Infect Control. 2012;40:922925.
  10. Saunders C, Hryhorskyj L, Skinner J. Factors influencing stethoscope cleanliness among clinical medical students. J Hosp Infect. 2013;84(3):242244.
  11. Jumaa PA. Hand hygiene: simple and complex. Int J Infect Dis. 2005;9:314.
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Hand hygiene is a proven and guideline‐recommended safety practice, although clinicians and particularly physicians are unreliable at performing it.[1] Like hands, stethoscopes can carry pathogens from patient to patient. In 1 study, stethoscopes were as likely to be contaminated after use with methicillin‐resistant Staphylococcus aureuspositive patients as the provider's hands.[2] Furthermore, like hands, stethoscopes can be effectively decolonized with alcohol.[3, 4] However, although hand hygiene rates have been extensively studied,[1] and hand hygiene has been linked to reductions in nosocomial infection,[5] stethoscope hygiene is less well studied and emphasized less by guidelines.[6] Several surveys have documented low self‐reported compliance with stethoscope hygiene.[7, 8, 9, 10] Of 150 healthcare workers, 48% reported stethoscope hygiene between daily and weekly, 37% did stethoscope hygiene monthly, and 7% did stethoscope hygiene annually or never.[8] Of 1401 doctors asked about their stethoscope hygiene beliefs and practices, 76% believed that stethoscopes could transmit infection, but only 24% reported cleaning their scopes regularly.[9] Moreover, of 308 students, 22% had never done stethoscope hygiene, and <4% did it consistently.[10] However, we were unable to find any data on observed rates of stethoscope hygiene. Thus, we observed student and trainee physician stethoscope hygiene performance during hospital medicine rotations as part of the baseline data‐collection phase of a quality‐improvement effort linked to hand hygiene efforts.

METHODS

Attending hospitalists (I.H.J., B.M., and A.A.) and 1 graduate assistant (J.W.) at 3 sites observed stethoscope hygiene opportunities over an 11‐month period. Stethoscope hygiene was counted as performed if a patient‐specific stethoscope was used in an isolation room, or if any type of cleaning (alcohol gel, alcohol wipe, or cleansing cloth) was performed on a stethoscope carried out of the room. Observers also recorded whether stethoscope hygiene opportunities occurred in isolation rooms or nonisolation rooms, and noted if stethoscope hygiene was obviously triggered by an attending's stethoscope hygiene behavior (eg, a trainee asked an attending why he performed stethoscope hygiene, then performed it him or herself). Trainees were not aware that their stethoscope hygiene behaviors were being recorded.

RESULTS

We observed 352 opportunities for stethoscope hygiene, in which doctors or students used stethoscope hygiene in 58 encounters (16%). Twenty of the 58 stethoscope hygiene events occurred only after a trainee observed an attending physician perform stethoscope hygiene. Eliminating stethoscope hygiene events that were triggered by attending physicians, stethoscope hygiene was performed in 38 of 332 opportunities (11%). There was a significant difference between the rate of stethoscope hygiene performed in isolation versus nonisolation rooms: 24/29 (82.7%) versus 14 of 303 (4.6%) (P<0.001 by Pearson 2 statistic). In isolation room stethoscope hygiene, in which the type of hygiene was recorded, 18 of 20 (90%) involved use of an isolation stethoscope, and 2 of 20 (10%) involved cleaning of a personal stethoscope.

DISCUSSION

Stethoscope hygiene is rarely performed by trainees. Stethoscope hygiene performance depends on the isolation status of the patient, with more than 80% performance in isolated patients and <5% in nonisolated patients.

Although little is known about the rate of infection related to stethoscopes, colonization of stethoscopes with nosocomial bacteria is well described.[2] Transmission of pathogens from patient to patient by stethoscopes could undermine the benefits of hand hygiene programs, as patients are commonly exposed to unclean stethoscopes.

Our observations are limited by several factors. We used a convenience sample of general medicine trainee behavior at academic medical centers; the behavior of attending physicians, ancillary staff, and nonacademic physicians may be different. Moreover, attending behavior may have prompted more episodes of stethoscope hygiene performance than we recorded, because we only noted when stethoscope hygiene was clearly related to attending behavior. The very low rate of stethoscope hygiene after contact with nonisolation patients represents a current and potentially serious safety threat. Future research might be able to quantify the risk associated with uncleaned stethoscopes or demonstrate the effectiveness of stethoscope hygiene programs. The effect of modeling on hand hygiene and stethoscope hygiene[10, 11] and on stethoscope hygiene in our data suggests a method for improving stethoscope hygiene.

Disclosure

Nothing to report.

Hand hygiene is a proven and guideline‐recommended safety practice, although clinicians and particularly physicians are unreliable at performing it.[1] Like hands, stethoscopes can carry pathogens from patient to patient. In 1 study, stethoscopes were as likely to be contaminated after use with methicillin‐resistant Staphylococcus aureuspositive patients as the provider's hands.[2] Furthermore, like hands, stethoscopes can be effectively decolonized with alcohol.[3, 4] However, although hand hygiene rates have been extensively studied,[1] and hand hygiene has been linked to reductions in nosocomial infection,[5] stethoscope hygiene is less well studied and emphasized less by guidelines.[6] Several surveys have documented low self‐reported compliance with stethoscope hygiene.[7, 8, 9, 10] Of 150 healthcare workers, 48% reported stethoscope hygiene between daily and weekly, 37% did stethoscope hygiene monthly, and 7% did stethoscope hygiene annually or never.[8] Of 1401 doctors asked about their stethoscope hygiene beliefs and practices, 76% believed that stethoscopes could transmit infection, but only 24% reported cleaning their scopes regularly.[9] Moreover, of 308 students, 22% had never done stethoscope hygiene, and <4% did it consistently.[10] However, we were unable to find any data on observed rates of stethoscope hygiene. Thus, we observed student and trainee physician stethoscope hygiene performance during hospital medicine rotations as part of the baseline data‐collection phase of a quality‐improvement effort linked to hand hygiene efforts.

METHODS

Attending hospitalists (I.H.J., B.M., and A.A.) and 1 graduate assistant (J.W.) at 3 sites observed stethoscope hygiene opportunities over an 11‐month period. Stethoscope hygiene was counted as performed if a patient‐specific stethoscope was used in an isolation room, or if any type of cleaning (alcohol gel, alcohol wipe, or cleansing cloth) was performed on a stethoscope carried out of the room. Observers also recorded whether stethoscope hygiene opportunities occurred in isolation rooms or nonisolation rooms, and noted if stethoscope hygiene was obviously triggered by an attending's stethoscope hygiene behavior (eg, a trainee asked an attending why he performed stethoscope hygiene, then performed it him or herself). Trainees were not aware that their stethoscope hygiene behaviors were being recorded.

RESULTS

We observed 352 opportunities for stethoscope hygiene, in which doctors or students used stethoscope hygiene in 58 encounters (16%). Twenty of the 58 stethoscope hygiene events occurred only after a trainee observed an attending physician perform stethoscope hygiene. Eliminating stethoscope hygiene events that were triggered by attending physicians, stethoscope hygiene was performed in 38 of 332 opportunities (11%). There was a significant difference between the rate of stethoscope hygiene performed in isolation versus nonisolation rooms: 24/29 (82.7%) versus 14 of 303 (4.6%) (P<0.001 by Pearson 2 statistic). In isolation room stethoscope hygiene, in which the type of hygiene was recorded, 18 of 20 (90%) involved use of an isolation stethoscope, and 2 of 20 (10%) involved cleaning of a personal stethoscope.

DISCUSSION

Stethoscope hygiene is rarely performed by trainees. Stethoscope hygiene performance depends on the isolation status of the patient, with more than 80% performance in isolated patients and <5% in nonisolated patients.

Although little is known about the rate of infection related to stethoscopes, colonization of stethoscopes with nosocomial bacteria is well described.[2] Transmission of pathogens from patient to patient by stethoscopes could undermine the benefits of hand hygiene programs, as patients are commonly exposed to unclean stethoscopes.

Our observations are limited by several factors. We used a convenience sample of general medicine trainee behavior at academic medical centers; the behavior of attending physicians, ancillary staff, and nonacademic physicians may be different. Moreover, attending behavior may have prompted more episodes of stethoscope hygiene performance than we recorded, because we only noted when stethoscope hygiene was clearly related to attending behavior. The very low rate of stethoscope hygiene after contact with nonisolation patients represents a current and potentially serious safety threat. Future research might be able to quantify the risk associated with uncleaned stethoscopes or demonstrate the effectiveness of stethoscope hygiene programs. The effect of modeling on hand hygiene and stethoscope hygiene[10, 11] and on stethoscope hygiene in our data suggests a method for improving stethoscope hygiene.

Disclosure

Nothing to report.

References
  1. World Health Organization. WHO Guidelines on Hand Hygiene in HealthCare. Global Patient Safety Challenge 2005‐2006: Clean Care Is Safer Care. Geneva, Switzerland: WHO Press; 2009.
  2. Longtin Y, Schneider A, Tschopp C, et al. Contamination of stethoscopes and physicians' hands after a physical examination. Mayo Clin Proc. 2014;89:291299.
  3. Bernard L, Kereveur A, Durand D, et al. Bacterial contamination of hospital physicians’ stethoscopes. Infect Control Hosp Epidemiol. 1999;20:626628.
  4. Schroeder A, Schroeder MA, D'Amico F. What's growing on your stethoscope? (and what you can do about it). J Fam Pract. 2009;58(8):404409.
  5. Pittet D, Hugonnet S, Harbarth S, et al. Effectiveness of a hospital wide program aimed at improving compliance with hand hygiene. Lancet. 2000;356:13071312.
  6. Bearman G, Bryant K, Leekha S, et al. Healthcare personnel attire in non‐operating‐room settings. Infect Control Hosp Epidemiol. 2014;35:107121.
  7. Breathnach AS, Jenkins DR, Pedler SJ. Stethoscopes as possible vectors of infection by staphylococci. BMJ. 1992;305:15731574.
  8. Jones JS, Hoerle D, Riekse R. Stethoscopes: a potential vector of infection? Ann Emerg Med. 1995;26:296299.
  9. Muniz J, Sethi RK, Zaghi J, Ziniel SI, Sandora TJ. Predictors of stethoscope disinfection among pediatric health care providers. Am J Infect Control. 2012;40:922925.
  10. Saunders C, Hryhorskyj L, Skinner J. Factors influencing stethoscope cleanliness among clinical medical students. J Hosp Infect. 2013;84(3):242244.
  11. Jumaa PA. Hand hygiene: simple and complex. Int J Infect Dis. 2005;9:314.
References
  1. World Health Organization. WHO Guidelines on Hand Hygiene in HealthCare. Global Patient Safety Challenge 2005‐2006: Clean Care Is Safer Care. Geneva, Switzerland: WHO Press; 2009.
  2. Longtin Y, Schneider A, Tschopp C, et al. Contamination of stethoscopes and physicians' hands after a physical examination. Mayo Clin Proc. 2014;89:291299.
  3. Bernard L, Kereveur A, Durand D, et al. Bacterial contamination of hospital physicians’ stethoscopes. Infect Control Hosp Epidemiol. 1999;20:626628.
  4. Schroeder A, Schroeder MA, D'Amico F. What's growing on your stethoscope? (and what you can do about it). J Fam Pract. 2009;58(8):404409.
  5. Pittet D, Hugonnet S, Harbarth S, et al. Effectiveness of a hospital wide program aimed at improving compliance with hand hygiene. Lancet. 2000;356:13071312.
  6. Bearman G, Bryant K, Leekha S, et al. Healthcare personnel attire in non‐operating‐room settings. Infect Control Hosp Epidemiol. 2014;35:107121.
  7. Breathnach AS, Jenkins DR, Pedler SJ. Stethoscopes as possible vectors of infection by staphylococci. BMJ. 1992;305:15731574.
  8. Jones JS, Hoerle D, Riekse R. Stethoscopes: a potential vector of infection? Ann Emerg Med. 1995;26:296299.
  9. Muniz J, Sethi RK, Zaghi J, Ziniel SI, Sandora TJ. Predictors of stethoscope disinfection among pediatric health care providers. Am J Infect Control. 2012;40:922925.
  10. Saunders C, Hryhorskyj L, Skinner J. Factors influencing stethoscope cleanliness among clinical medical students. J Hosp Infect. 2013;84(3):242244.
  11. Jumaa PA. Hand hygiene: simple and complex. Int J Infect Dis. 2005;9:314.
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The third hand: Low rates of stethoscope hygiene on general medical services
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Address for correspondence and reprint requests: Ian Harold Jenkins, MD, Department of Medicine, University of California, San Diego, San Diego, CA 92103; E‐mail: Ihjenkins@ucsd.edu
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HN‐Associated Healthcare Burden

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Evaluation of incremental healthcare resource burden and readmission rates associated with hospitalized hyponatremic patients in the US

Hyponatremia is an electrolyte disorder most commonly defined as a serum sodium concentration <135 mEq/L.1 Its exact definition can vary across studies, but typically ranges between <130 and <138 mEq/L.2, 3 Signs and symptoms of hyponatremia can include malaise, headache, disorientation, confusion, muscle weakness, and cramps. If severe, seizures, respiratory arrest, brainstem herniation, coma, and death may result.

The incidence of hyponatremia in the general hospitalized population has been reported to range between 1% and 6% when defined as <130135 mEq/L,4, 5 and its occurrence increases with a more prolonged hospital stay to 13%.6 A recent study reported that when hyponatremia was defined with a less stringent threshold of <138 mEq/L, the incidence at admission rose to 38%.3 Hyponatremia is a comorbid condition of multiple diseases, occurring in approximately 20% of patients with heart failure,7, 8 and 40% to 57% of patients with advanced cirrhosis.9, 10 The syndrome of the inappropriate release of antidiuretic hormone (SIADH) is additionally a predominant cause of hyponatremia, with a prevalence reported as high as 35% in hospitalized patients.11

Hyponatremia is not only widespread, but also an independent predictor of mortality. In a retrospective cohort analysis, Waikar et al reported that in comparison to patients who were normonatremic, patients with serum sodium concentrations <135 mEq/L had a risk of in‐hospital mortality as high as 47%, and that this risk doubled for patients with serum sodium concentrations between 125 and 129 mEq/L.6 In the study by Wald et al, which defined hyponatremia as <138 mEq/L, the risk of in‐hospital mortality was similar.3 In both of these studies, even mild hyponatremia (130137 mEq/L) was associated with increased risk of in‐hospital mortality.3, 6

The overall cost of hyponatremia is estimated to range between $1.6 and $3.6 billion for 2011.12, 13 Hospital readmissions are a significant contributor to total healthcare costs, with some being entirely avoidable with increased standards of care. The Centers for Medicare and Medicaid Services has begun to not only publicly report hospital readmission rates, but also penalize hospitals for early readmissions.14 Strategies to reduce hospital readmissions are currently being integrated into healthcare reform policy.14 In the present study, the incremental burden of hospitalized hyponatremic (HN) versus non‐HN patients in terms of hospital resource utilization, costs, and early hospital readmission in the real‐world was evaluated.

METHODS

Study Design

This study was a retrospective analysis that examined healthcare utilization and costs among HN patients using the Premier Hospital Database. The database contains over 310 million hospital encounters from more than 700 US hospitals, or 1 out of every 4 discharges in the US. The administrative data available included patient and provider demographics, diagnoses and procedures, as well as date‐stamped billing records for all pharmacy, laboratory, imaging, procedures, and supplies.

Patient Selection and Matching

HN patients were eligible for study inclusion if they were a hospital inpatient discharged between January 1, 2007 and March 31, 2010, were >18 years of age at admission, and had either a primary or secondary diagnosis of hyponatremia or hyposmolality (defined as International Classification of Diseases, Ninth Revision (ICD‐9‐CM) code: 276.1x). Patients were excluded if they had been transferred from another acute care facility, transferred to another acute care or critical access facility, or left against medical advice. Labor and delivery patients (ICD‐9‐CM codes: 72.xx‐74.xx, V22.x, V23.x, V27.x, and V28.x), and patients classified as observational were also excluded. A second cohort of non‐hyponatremic patients was created using the same inclusion and exclusion criteria, with the exception that patients not have a primary or secondary diagnosis of hyponatremia or hyposmolality (defined as ICD‐9‐CM code: 276.1x).

The matching of hyponatremia (HN) and control (non‐HN) cohorts was accomplished using a combination of exact and propensity score matching techniques. Patients were first exact matched on age, gender, Medicare Severity‐Diagnosis Related Group (MS‐DRG) assignment, and hospital geographic region. Propensity score matching was further utilized to create the final study cohorts for outcomes comparisons. Propensity score matching is commonly used in retrospective cohort studies to correct for sample selection bias due to observable differences between groups.15, 16 The propensity score was generated using logistic regression with the dependent variable as hyponatremia (yes vs no) and the following covariates: age, race, admission source, attending physician specialty, 3M All Patient Refined‐Diagnosis Related Group (APR‐DRG) Severity of Illness and Risk of Mortality index scores, Deyo‐Charlson Comorbidity Index score, selected hyponatremia‐related comorbidity conditions, and hospital size, region, and urban/rural designation. These covariates were initially selected by an expert panel of physicians, and backward selection was utilized in the logistic regression using the most parsimonious model.17

Following generation of the propensity scores, HN patients were matched to non‐HN patients 1:1 using a nearest neighbor matching algorithm, including hospital identification and propensity score.18 Inclusion of hospital identification in the matching sequence, as well as provider characteristics, especially hospital size, attending physician specialty, and geographic region in the propensity score, was used to control for potential clustering effects at the physician and hospital level.19 During the propensity score matching process, likelihood‐ratio test, Hosmer‐Lemshow goodness of fit, and concordance c statistics were utilized to assess the fitness of the models.20 The final propensity score model produced a concordance c statistic of 0.8.

Outcome Measures and Statistical Analyses

The following outcome measures were compared between the matched HN and non‐HN patient cohorts: total and intensive care unit (ICU) hospitalization costs, total and ICU length of stay (LOS), ICU admission, and 30‐day hospital readmission. Bivariate descriptive statistics were employed to test for significant differences in demographics, patient clinical characteristics, and unadjusted costs and healthcare resource utilization and readmission rates between patient cohorts. To detect statistically significant differences in continuous and categorical variables, respectively, t tests and chi‐square tests were performed.

Multivariate analysis of outcome measures utilized generalized linear models. Due to the skewed nature of LOS and cost data, LOS was analyzed using multivariate negative binomial regression and cost was analyzed using multivariate gamma regression.21 Binary outcomes (ICU admission and 30‐day readmission) were analyzed using multivariate logistic regression. The analysis accounted for potential confounding factors by inclusion of the following covariates: age group, gender, race, admission source, and Deyo‐Charlson Comorbidity Index score. These covariates were previously identified in the Wald et al hyponatremia study,3 and were verified using likelihood‐ratio, Hosmer‐Lemshow goodness of fit, and concordance c statistics.

Subgroup Sensitivity Analysis

For the subgroup sensitivity analyses, patients were identified as having community‐acquired pneumonia (CAP), congestive heart failure (CHF), urinary tract infection (UTI), or chronic obstructive pulmonary disease (COPD) based upon principal diagnosis codes. Patients were categorized according to these subgroup definitions, and then previously matched patients with the same subgroup classification constituted the final analysis set for each subgroup. The methods that were used for the overall matched analysis were then applied to each subgroup to evaluate the incremental burden of overall cost and LOS associated with hyponatremia.

RESULTS

Patient Population

Of the 606,057 HN patients eligible for matching, a total of 558,815 HN patients were matched to 558,815 non‐HN patients, a 92% match ratio. Table 1 describes the overall characteristics of the patient populations. For both cohorts, median age was 70 years, 57% of patients were female, and approximately 67% were white. The majority of patients in either cohort had Medicare coverage (55%), and approximately 75% of patients entered the hospital via the emergency room with nearly 70% having a 3M APR‐DRG disease severity level of major or extreme. Patients of both cohorts were most often attended by an internist or a hospitalist, with a combined percentage of approximately 60%. A small, but greater proportion of HN patients had comorbidities of cancer, pulmonary disease, and SIADH. Comorbid conditions of liver cirrhosis/hepatic disease and human immunodeficiency virus (HIV) were similarly distributed among both patient cohorts.

Baseline Demographics and Clinical and Hospital Characteristics for Matched Cohorts of Hyponatremic and Non‐Hyponatremic Patients
 HyponatremicNon‐Hyponatremic
Discharges N (%)Discharges N (%)
  • Abbreviations: APR‐DRG, all patient refined diagnosis‐related groups; CCI, Charlson Comorbidity Index; COPD, chronic obstructive pulmonary disease; IQR, interquartile range; SIADH, syndrome of inappropriate antidiuretic hormone hypersecretion.

Sample Discharges558,815 (100.0%)558,815 (100.0%)
Age median (IQR)70.00 (57.081.0)70.00 (57.081.0)
Gender
Female319,069 (57.1%)319,069 (57.1%)
Male239,746 (42.9%)239,746 (42.9%)
Race
American Indian3,465 (0.6%)3,448 (0.6%)
Asian/Pacific10,065 (1.8%)9,690 (1.7%)
Black63,776 (11.4%)66,233 (11.9%)
Hispanic24,341 (4.4%)24,426 (4.4%)
White377,434 (67.5%)376,639 (67.4%)
Other/unknown79,734 (14.3%)78,379 (14.0%)
Primary payer
Medicaretraditional310,312 (55.5%)310,643 (55.6%)
Managed care75,476 (13.5%)78,184 (14.0%)
Medicaremanaged care45,439 (8.1%)47,947 (8.6%)
Non‐cap
Medicaid44,690 (8.0%)43,767 (7.8%)
Other82,898 (14.8%)78,274 (14.0%)
Admission source
Physician referral5,022 (0.9%)4,636 (0.8%)
Transfer from another nonacute health facility18,031 (3.2%)18,163 (3.3%)
Emergency room417,556 (74.7%)420,401 (75.2%)
Other/unknown118,206 (21.2%)115,615 (20.7%)
APR‐DRG severity of illness
1Minor14,257 (2.6%)12,993 (2.3%)
2Moderate174,859 (31.3%)179,356 (32.1%)
3Major263,814 (47.2%)265,422 (47.5%)
4Extreme105,885 (19.0%)101,044 (18.1%)
Attending physician specialty
Internal Medicine235,628 (42.1%)240,875 (43.1%)
Hospitalist88,250 (15.8%)87,720 (15.7%)
Family Practice (FP)73,346 (13.1%)72,828 (13.0%)
Orthopedic Surgery (ORS)23,595 (4.2%)22,949 (4.1%)
Cardiovascular Diseases (CD)19,521 (3.5%)18,057 (3.2%)
Comorbidities
Human immunodeficiency virus3,971 (0.7%)4,018 (0.7%)
Cancer/neoplasm/malignancy107,851 (19.3%)105,199 (18.8%)
Pulmonary disease77,849 (13.9%)77,184 (13.8%)
Cirrhosis/hepatic disease23,038 (4.1%)23,418 (4.2%)
SIADH1,972 (0.4%)1,278 (0.2%)
Subgroup populations
Community‐acquired pneumonia26,291 (4.7%)26,291 (4.7%)
Congestive heart failure23,020 (4.1%)23,020 (4.1%)
Urinary tract infection14,238 (2.6%)14,238 (2.6%)
COPD8,696 (1.6%)8,696 (1.6%)
CCI median (IQR)3.0 (1.05.0)3.0 (1.05.0)
No. of premier hospitals459
Provider region
East North Central78,332 (14.0%)78,332 (14.0%)
East South Central32,122 (5.8%)32,122 (5.8%)
Middle Atlantic73,846 (13.2%)73,846 (13.2%)
Mountain23,761 (4.3%)23,761 (4.3%)
New England9,493 (1.7%)9,493 (1.7%)
Pacific73,059 (13.1%)73,059 (13.1%)
South Atlantic175,194 (31.4%)175,194 (31.4%)
West North Central37,913 (6.8%)37,913 (6.8%)
West South Central55,095 (9.9%)55,095 (9.9%)
Population served
Rural69,749 (12.5%)68,414 (12.2%)
Urban489,066 (87.5%)490,401 (87.8%)
Teaching status
Non‐teaching337,620 (60.4%)337,513 (60.4%)
Teaching221,195 (39.6%)221,302 (39.6%)
No. of hospital beds
69922,067 (4.0%)21,777 (3.9%)
10019957,367 (10.3%)56,097 (10.0%)
20029987,563 (15.7%)86,639 (15.5%)
300499218,834 (39.2%)220,248 (39.4%)
500+172,984 (31.0%)174,054 (31.2%)

Hospital Characteristics

Patient cohorts had similar distributions with respect to hospital characteristics (Table 1). Approximately 30% of patients were provided care from hospitals located in the South Atlantic region, and between 10% and 15% were serviced from hospitals in the East North Central, Middle Atlantic, Pacific, and West South Central regions. Most hospitals providing care for patient cohorts served urban populations (88%) and were large, with hospital bed numbers 300. Approximately 60% of hospitals were non‐teaching hospitals.

Healthcare Utilization, Readmission, and Cost Differences Among Patient Cohorts

The mean LOS (8.8 10.3 vs 7.7 8.5, P < 0.001), a difference of 1.1 days and mean ICU LOS (5.5 7.9 vs 4.9 7.1 days, P < 0.001), a difference of 0.6 days were significantly greater for the HN cohort in comparison to the non‐HN cohort (Table 2). The increase in healthcare resource utilization of patients with HN was reflected in their significantly higher mean total hospital costs per admission ($15,281 $24,054 vs $13,439 $22,198, P < 0.001), a difference of $1842; and mean costs incurred in the ICU ($8525 $13,342 vs $7597 $12,695, P < 0.001), a difference of $928 (Table 2). Furthermore, patients in the HN cohort were significantly more likely to be readmitted to the hospital for any cause (17.5% vs 16.4%, P < 0.001) (Table 2).

Outcome Measurements for Matched Cohorts of Hyponatremic and Non‐Hyponatremic Patients
 HyponatremicNon‐HyponatremicP Value
  • Abbreviations: ICU, intensive care unit; LOS, length of stay; SD, standard deviation.

Total LOS (mean SD)8.8 10.37.7 8.5<0.001
Total hospitalization cost (mean SD)$15,281 $24,054$13,439 $22,198<0.001
ICU admission (N, %)129,235 (23.1%)123,502 (22.1%)<0.001
ICU LOS (mean SD)5.5 7.94.9 7.1<0.001
ICU cost (mean SD)$8,525 $13,342$7,597 $12,695<0.001
30‐Day all cause readmission (N, %)96,063 (17.5%)87,058 (16.4%)<0.001

Multivariate Analysis

Multivariate analysis demonstrated hyponatremia was associated with an increase in mean hospital LOS of 10.9%, [95% confidence interval: 10.4%11.5%], (P < 0.0001) and an increase in mean total hospital costs of 8.2%, [7.4%9.0%], (P < 0.0001) (Table 3). Additionally, hyponatremia was associated with an increase in ICU LOS of 10.2%, [8.7%11.8%], (P < 0.0001), and a higher ICU cost of 8.9%, [7.2%10.7%], (P < 0.0001) (Table 3). Hyponatremia was not associated with a greater likelihood of ICU admission (odds ratio = 1.0; [1.01.0], P =.5760). However, the condition was associated with a significantly greater chance of hospital readmission (odds ratio = 1.2, [1.11.2], P < 0.0001) within 30 days postdischarge (Table 3).

Relative Difference (Mean [CI]) in Healthcare Utilization, Costs, and Odds for ICU and Early Readmission Based on Multivariate Analysis for Hyponatremic Patients vs Non‐Hyponatremic Patients
 Overall Cohort N = 1,117,630CAP N = 52,582CHF N = 46,040UTI N = 28,746COPD N = 17,392
  • Abbreviations: CAP, community‐acquired pneumonia; CHF, congestive heart failure; CI, confidence interval [lower, upper]; COPD, chronic obstructive pulmonary disease; ICU, intensive care unit; LOS, length of stay; UTI, urinary tract infection. *The number and percentage of patients, hyponatremic and non‐hyponatremic in each group, admitted to the ICU were the following: overall: 252,737 (22.6%); CAP: 6321 (12.0%); CHF: 8293 (18.0%); UTI: 1243 (4.4%); COPD: 1687 (9.7%). The number and percentage of patients, hyponatremic and non‐hyponatremic in each group, admitted to the hospital within 30 days of discharge were the following: overall: 183,121 (16.4%); CAP: 7310 (14.6%); CHF: 10,466 (24.0%); UTI: 4306 (15.3%); COPD: 3346 (19.7%).

Total LOS difference10.9% [10.4%, 11.5%] P < 0.00015.4% [4.4%, 6.5%] P < 0.000120.6% [19.0%, 22.2%] P < 0.00012.7% [1.2%, 4.2%] P = 0.00036.7% [4.8%, 8.5%] P < 0.0001
Total cost difference8.2% [7.4%, 9.0%] P < 0.00015.6% [4.0%, 7.0%] P < 0.000119.2% [17.1%, 21.4%] P < 0.00012.2% [1.4%, 4.4%] P = 0.05825.6% [3.0%, 8.2%] P < 0.0001
ICU admission* odds ratio1.0 [1.0, 1.0] P = 0.57601.0 [1.0, 1.1] P = 0.83631.2 [1.1, 1.3] P < 0.00011.2 [1.1, 1.3] P = 0.00381.0 [0.9, 1.1] P = 0.5995
ICU LOS difference10.2% [8.7%, 11.8%] P < 0.00018.4% [3.4%, 13.7%] P = 0.000824.8% [19.9%, 29.9%] P < 0.00014.9% [4.0%, 14.7%] P = 0.289310.1% [1.5%, 19.3%] P = 0.0204
ICU cost difference8.9% [7.2%, 10.7%] P < 0.00018.2% [2.7%, 14.0%] P = 0.002924.1% [18.9%, 29.7%] P < 0.00012.8% [6.7%, 13.4%] P = 0.57398.4% [0.6%, 18.1%] P = 0.6680
30‐Day readmission odds ratio1.2 [1.1, 1.2] P < 0.00011.0 [0.9, 1.0] P = 0.51411.1 [1.1, 1.2] P < 0.00011.0 [1.0, 1.1] P = 0.52111.0 [1.0, 1.1] P = 0.6119

Subgroup Sensitivity Analyses

For patients with CAP (n = 52,582), CHF (n = 46,040), a UTI (n = 28,476), or COPD (n = 17,392), the percent increases in LOS and all cause hospitalization cost of the HN cohort in comparison to the non‐HN cohort were 5.4% (P < 0.0001) and 5.6% (P < 0.0001), 20.6% (P < 0.0001) and 19.2% (P < 0.0001), 2.7% (P = 0.0003) and 2.2% (P = 0.0582), and 6.7% (P < 0.0001) and 5.6% (P < 0.0001), respectively (Table 3).

DISCUSSION

This large‐scale, real‐world hospital database study provides healthcare utilization and cost data on the largest population of HN patients that has currently been studied. The results of this study are consistent with others in showing that HN patients use healthcare services more extensively, and represent a patient population which is more expensive to treat in the inpatient setting.5, 22 Additionally, this study yields new findings in that patients in the real‐world with hyponatremia resulting from various etiologies are more likely to be readmitted to the hospital than patients with similar demographics and characteristics who do not have hyponatremia. The results of the subgroup analysis were generally consistent with the results for the overall matched population, as the incremental burden estimates were directionally consistent. However, among the specific subgroups of patients, although it appears that hyponatremia is predictive of hospital readmission in patients with CHF, it did not necessarily correspond with hospital readmission rates among patients with CAP, UTI, or COPD. Therefore, hyponatremia, at least for patients with these latter conditions, may not be predictive of readmission, but remains associated with increased healthcare utilization during the initial hospitalization. Further evaluation of the incremental burden of hyponatremia among patients with specific disease conditions is needed to validate the findings.

The difference in hospital LOS at first admission between HN and non‐HN patients in this study was 1.1 days, and comparable to that reported by Shorr et al.23 In the study by Shorr et al, patients hospitalized for congestive heart failure (CHF), who were HN, had a 0.5 day increased LOS, and those who were severely HN had a 1.3 day increased LOS.23 Two other retrospective cohort analyses reported a 1.4 day5 and 2.0 day22 increased LOS for HN patients in comparison to non‐HN patients. Zilberberg et al and Callahan et al additionally reported that HN patients had a significantly greater need for ICU (4%10%).5, 22 In the present study, LOS in the ICU and associated costs were also compared among HN and non‐HN cohorts and, after adjustment for key patient characteristics, hyponatremia was associated with an incremental increase of 10.2% for ICU LOS and an 8.9% increase in ICU cost.

In this study, patients with hyponatremia of any severity were found to have a mean increase in hospital cost per admission of $1842, with multivariate analysis demonstrating an associated incremental increase of 8.2%. These results are also comparable to that reported in other retrospective cohort analyses. Shorr et al reported that in‐hospital costs attributed to hyponatremia were $509, and for severe hyponatremia were $1132.23 Callahan et al reported a $1200 increase in costs per admission for patients with mild‐to‐moderate hyponatremia, and a $3540 increase for patients with moderate‐to‐severe hyponatremia, in comparison to non‐HN patients.22

The Institute for Healthcare Improvement reports that hospitalizations account for nearly one‐third of the $882 billion spent on healthcare in the US in 2011, and that a substantial fraction of all hospitalizations are readmissions.13, 24 A recent meta‐analysis of 30‐day hospital readmission rates found 23.1% were classified as avoidable.25 Readmission rates of HN patients in comparison to non‐HN patients have been reported in 3 other studies, all of which were conducted on clinical trial patients with heart failure.7, 8, 26 Two of these studies, which evaluated only patients with acute class IV heart failure,8, 26 reported that hyponatremia was associated with a significant increase in readmission rates (up to 20% higher), and the other, which evaluated any patients hospitalized for heart failure, did not.7 The differences there may be partially attributed to the differences in heart failure severity among patient populations, as hyponatremia is an independent predictor of worsening heart failure. The only published study to date on the influence of hyponatremia on readmission rates in the real‐world was conducted by Scherz et al, who reported that the co‐occurrence of hyponatremia in patients with acute pulmonary embolism, discharged from 185 hospitals in Pennsylvania, was independently associated with an increased readmission rate of 19.3%.27 In the present study, hyponatremia was associated with an incremental increase ranging between 14% and 17% for hospital readmission for any cause. It was conducted on a patient population in which hyponatremia was resultant from many causes, and not all patients had a serious comorbid condition. Also, the HN and non‐HN cohorts were matched for comorbidity prevalence and disease severity, and in other studies they were not. Therefore, the results of this study importantly imply that hyponatremia, whether it is resultant from a serious disease or any other cause substantially increases the healthcare burden. The implementation of strategies to prevent hospital readmissions may play an important role in reducing the healthcare burden of hyponatremia, and future studies are warranted to evaluate this hypothesis alongside evaluation of the outpatient hyponatremia burden.

The limitations of this study include, firstly, that it is only representative of inpatient hospital costs, and excludes outpatient healthcare utilization and costs. Secondly, this study utilized the Premier Hospital Database for patient selection, and laboratory testing data for serum sodium level are not available in this database; therefore, the severity of hyponatremia could not be accurately established in the HN patient population. Thirdly, the occurrence of hyponatremia in patients with some diseases is a marker of disease severity, as is the case with congestive heart failure and cirrhosis.23, 28 Our study did not adjust for the specific disease (eg, CHF) severity, which may influence the results. Future research is needed to evaluate the impact of hyponatremia on underlying disease severity of other diseases, and how its co‐occurrence may influence healthcare resource utilization and cost in each case. Although the Premier Hospital Database contains information from a large number of hospitals across the US, it is possible that it may not be representative of the entire US population of HN patients. Additionally, billing and coding errors and missing data could potentially have occurred, although the large patient population size likely precludes a large impact on the results of this study. Finally, the frequency of use of fluid restriction in these hospital settings could not be chronicled, thus limiting the ability to assess therapies and treatment modalities in use.

Acknowledgements

The authors acknowledge Melissa Lingohr‐Smith from Novosys Health in the editorial support and review of this manuscript.

Disclosures: This research was supported by Otsuka America Pharmaceutical, Inc, Princeton, NJ, which manufactures tolvaptan for the treatment of hyponatremia. Drs Amin and Deitelzweig are consultants for, and have received honoraria from, Otsuka America Pharmaceutical in connection with conducting this study. Drs Christian and Friend are employees of Otsuka America Pharmaceutical. Dr Lin is an employee of Novosys Health, which has received research funds from Otsuka America Pharmaceutical in connection with conducting this study and development of this manuscript. D. Baumer and Dr. Lowe are employees of Premier Inc, which has received research funds from Otsuka America Pharmaceutical. K. Belk was previously employed with Premier Inc.

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Hyponatremia is an electrolyte disorder most commonly defined as a serum sodium concentration <135 mEq/L.1 Its exact definition can vary across studies, but typically ranges between <130 and <138 mEq/L.2, 3 Signs and symptoms of hyponatremia can include malaise, headache, disorientation, confusion, muscle weakness, and cramps. If severe, seizures, respiratory arrest, brainstem herniation, coma, and death may result.

The incidence of hyponatremia in the general hospitalized population has been reported to range between 1% and 6% when defined as <130135 mEq/L,4, 5 and its occurrence increases with a more prolonged hospital stay to 13%.6 A recent study reported that when hyponatremia was defined with a less stringent threshold of <138 mEq/L, the incidence at admission rose to 38%.3 Hyponatremia is a comorbid condition of multiple diseases, occurring in approximately 20% of patients with heart failure,7, 8 and 40% to 57% of patients with advanced cirrhosis.9, 10 The syndrome of the inappropriate release of antidiuretic hormone (SIADH) is additionally a predominant cause of hyponatremia, with a prevalence reported as high as 35% in hospitalized patients.11

Hyponatremia is not only widespread, but also an independent predictor of mortality. In a retrospective cohort analysis, Waikar et al reported that in comparison to patients who were normonatremic, patients with serum sodium concentrations <135 mEq/L had a risk of in‐hospital mortality as high as 47%, and that this risk doubled for patients with serum sodium concentrations between 125 and 129 mEq/L.6 In the study by Wald et al, which defined hyponatremia as <138 mEq/L, the risk of in‐hospital mortality was similar.3 In both of these studies, even mild hyponatremia (130137 mEq/L) was associated with increased risk of in‐hospital mortality.3, 6

The overall cost of hyponatremia is estimated to range between $1.6 and $3.6 billion for 2011.12, 13 Hospital readmissions are a significant contributor to total healthcare costs, with some being entirely avoidable with increased standards of care. The Centers for Medicare and Medicaid Services has begun to not only publicly report hospital readmission rates, but also penalize hospitals for early readmissions.14 Strategies to reduce hospital readmissions are currently being integrated into healthcare reform policy.14 In the present study, the incremental burden of hospitalized hyponatremic (HN) versus non‐HN patients in terms of hospital resource utilization, costs, and early hospital readmission in the real‐world was evaluated.

METHODS

Study Design

This study was a retrospective analysis that examined healthcare utilization and costs among HN patients using the Premier Hospital Database. The database contains over 310 million hospital encounters from more than 700 US hospitals, or 1 out of every 4 discharges in the US. The administrative data available included patient and provider demographics, diagnoses and procedures, as well as date‐stamped billing records for all pharmacy, laboratory, imaging, procedures, and supplies.

Patient Selection and Matching

HN patients were eligible for study inclusion if they were a hospital inpatient discharged between January 1, 2007 and March 31, 2010, were >18 years of age at admission, and had either a primary or secondary diagnosis of hyponatremia or hyposmolality (defined as International Classification of Diseases, Ninth Revision (ICD‐9‐CM) code: 276.1x). Patients were excluded if they had been transferred from another acute care facility, transferred to another acute care or critical access facility, or left against medical advice. Labor and delivery patients (ICD‐9‐CM codes: 72.xx‐74.xx, V22.x, V23.x, V27.x, and V28.x), and patients classified as observational were also excluded. A second cohort of non‐hyponatremic patients was created using the same inclusion and exclusion criteria, with the exception that patients not have a primary or secondary diagnosis of hyponatremia or hyposmolality (defined as ICD‐9‐CM code: 276.1x).

The matching of hyponatremia (HN) and control (non‐HN) cohorts was accomplished using a combination of exact and propensity score matching techniques. Patients were first exact matched on age, gender, Medicare Severity‐Diagnosis Related Group (MS‐DRG) assignment, and hospital geographic region. Propensity score matching was further utilized to create the final study cohorts for outcomes comparisons. Propensity score matching is commonly used in retrospective cohort studies to correct for sample selection bias due to observable differences between groups.15, 16 The propensity score was generated using logistic regression with the dependent variable as hyponatremia (yes vs no) and the following covariates: age, race, admission source, attending physician specialty, 3M All Patient Refined‐Diagnosis Related Group (APR‐DRG) Severity of Illness and Risk of Mortality index scores, Deyo‐Charlson Comorbidity Index score, selected hyponatremia‐related comorbidity conditions, and hospital size, region, and urban/rural designation. These covariates were initially selected by an expert panel of physicians, and backward selection was utilized in the logistic regression using the most parsimonious model.17

Following generation of the propensity scores, HN patients were matched to non‐HN patients 1:1 using a nearest neighbor matching algorithm, including hospital identification and propensity score.18 Inclusion of hospital identification in the matching sequence, as well as provider characteristics, especially hospital size, attending physician specialty, and geographic region in the propensity score, was used to control for potential clustering effects at the physician and hospital level.19 During the propensity score matching process, likelihood‐ratio test, Hosmer‐Lemshow goodness of fit, and concordance c statistics were utilized to assess the fitness of the models.20 The final propensity score model produced a concordance c statistic of 0.8.

Outcome Measures and Statistical Analyses

The following outcome measures were compared between the matched HN and non‐HN patient cohorts: total and intensive care unit (ICU) hospitalization costs, total and ICU length of stay (LOS), ICU admission, and 30‐day hospital readmission. Bivariate descriptive statistics were employed to test for significant differences in demographics, patient clinical characteristics, and unadjusted costs and healthcare resource utilization and readmission rates between patient cohorts. To detect statistically significant differences in continuous and categorical variables, respectively, t tests and chi‐square tests were performed.

Multivariate analysis of outcome measures utilized generalized linear models. Due to the skewed nature of LOS and cost data, LOS was analyzed using multivariate negative binomial regression and cost was analyzed using multivariate gamma regression.21 Binary outcomes (ICU admission and 30‐day readmission) were analyzed using multivariate logistic regression. The analysis accounted for potential confounding factors by inclusion of the following covariates: age group, gender, race, admission source, and Deyo‐Charlson Comorbidity Index score. These covariates were previously identified in the Wald et al hyponatremia study,3 and were verified using likelihood‐ratio, Hosmer‐Lemshow goodness of fit, and concordance c statistics.

Subgroup Sensitivity Analysis

For the subgroup sensitivity analyses, patients were identified as having community‐acquired pneumonia (CAP), congestive heart failure (CHF), urinary tract infection (UTI), or chronic obstructive pulmonary disease (COPD) based upon principal diagnosis codes. Patients were categorized according to these subgroup definitions, and then previously matched patients with the same subgroup classification constituted the final analysis set for each subgroup. The methods that were used for the overall matched analysis were then applied to each subgroup to evaluate the incremental burden of overall cost and LOS associated with hyponatremia.

RESULTS

Patient Population

Of the 606,057 HN patients eligible for matching, a total of 558,815 HN patients were matched to 558,815 non‐HN patients, a 92% match ratio. Table 1 describes the overall characteristics of the patient populations. For both cohorts, median age was 70 years, 57% of patients were female, and approximately 67% were white. The majority of patients in either cohort had Medicare coverage (55%), and approximately 75% of patients entered the hospital via the emergency room with nearly 70% having a 3M APR‐DRG disease severity level of major or extreme. Patients of both cohorts were most often attended by an internist or a hospitalist, with a combined percentage of approximately 60%. A small, but greater proportion of HN patients had comorbidities of cancer, pulmonary disease, and SIADH. Comorbid conditions of liver cirrhosis/hepatic disease and human immunodeficiency virus (HIV) were similarly distributed among both patient cohorts.

Baseline Demographics and Clinical and Hospital Characteristics for Matched Cohorts of Hyponatremic and Non‐Hyponatremic Patients
 HyponatremicNon‐Hyponatremic
Discharges N (%)Discharges N (%)
  • Abbreviations: APR‐DRG, all patient refined diagnosis‐related groups; CCI, Charlson Comorbidity Index; COPD, chronic obstructive pulmonary disease; IQR, interquartile range; SIADH, syndrome of inappropriate antidiuretic hormone hypersecretion.

Sample Discharges558,815 (100.0%)558,815 (100.0%)
Age median (IQR)70.00 (57.081.0)70.00 (57.081.0)
Gender
Female319,069 (57.1%)319,069 (57.1%)
Male239,746 (42.9%)239,746 (42.9%)
Race
American Indian3,465 (0.6%)3,448 (0.6%)
Asian/Pacific10,065 (1.8%)9,690 (1.7%)
Black63,776 (11.4%)66,233 (11.9%)
Hispanic24,341 (4.4%)24,426 (4.4%)
White377,434 (67.5%)376,639 (67.4%)
Other/unknown79,734 (14.3%)78,379 (14.0%)
Primary payer
Medicaretraditional310,312 (55.5%)310,643 (55.6%)
Managed care75,476 (13.5%)78,184 (14.0%)
Medicaremanaged care45,439 (8.1%)47,947 (8.6%)
Non‐cap
Medicaid44,690 (8.0%)43,767 (7.8%)
Other82,898 (14.8%)78,274 (14.0%)
Admission source
Physician referral5,022 (0.9%)4,636 (0.8%)
Transfer from another nonacute health facility18,031 (3.2%)18,163 (3.3%)
Emergency room417,556 (74.7%)420,401 (75.2%)
Other/unknown118,206 (21.2%)115,615 (20.7%)
APR‐DRG severity of illness
1Minor14,257 (2.6%)12,993 (2.3%)
2Moderate174,859 (31.3%)179,356 (32.1%)
3Major263,814 (47.2%)265,422 (47.5%)
4Extreme105,885 (19.0%)101,044 (18.1%)
Attending physician specialty
Internal Medicine235,628 (42.1%)240,875 (43.1%)
Hospitalist88,250 (15.8%)87,720 (15.7%)
Family Practice (FP)73,346 (13.1%)72,828 (13.0%)
Orthopedic Surgery (ORS)23,595 (4.2%)22,949 (4.1%)
Cardiovascular Diseases (CD)19,521 (3.5%)18,057 (3.2%)
Comorbidities
Human immunodeficiency virus3,971 (0.7%)4,018 (0.7%)
Cancer/neoplasm/malignancy107,851 (19.3%)105,199 (18.8%)
Pulmonary disease77,849 (13.9%)77,184 (13.8%)
Cirrhosis/hepatic disease23,038 (4.1%)23,418 (4.2%)
SIADH1,972 (0.4%)1,278 (0.2%)
Subgroup populations
Community‐acquired pneumonia26,291 (4.7%)26,291 (4.7%)
Congestive heart failure23,020 (4.1%)23,020 (4.1%)
Urinary tract infection14,238 (2.6%)14,238 (2.6%)
COPD8,696 (1.6%)8,696 (1.6%)
CCI median (IQR)3.0 (1.05.0)3.0 (1.05.0)
No. of premier hospitals459
Provider region
East North Central78,332 (14.0%)78,332 (14.0%)
East South Central32,122 (5.8%)32,122 (5.8%)
Middle Atlantic73,846 (13.2%)73,846 (13.2%)
Mountain23,761 (4.3%)23,761 (4.3%)
New England9,493 (1.7%)9,493 (1.7%)
Pacific73,059 (13.1%)73,059 (13.1%)
South Atlantic175,194 (31.4%)175,194 (31.4%)
West North Central37,913 (6.8%)37,913 (6.8%)
West South Central55,095 (9.9%)55,095 (9.9%)
Population served
Rural69,749 (12.5%)68,414 (12.2%)
Urban489,066 (87.5%)490,401 (87.8%)
Teaching status
Non‐teaching337,620 (60.4%)337,513 (60.4%)
Teaching221,195 (39.6%)221,302 (39.6%)
No. of hospital beds
69922,067 (4.0%)21,777 (3.9%)
10019957,367 (10.3%)56,097 (10.0%)
20029987,563 (15.7%)86,639 (15.5%)
300499218,834 (39.2%)220,248 (39.4%)
500+172,984 (31.0%)174,054 (31.2%)

Hospital Characteristics

Patient cohorts had similar distributions with respect to hospital characteristics (Table 1). Approximately 30% of patients were provided care from hospitals located in the South Atlantic region, and between 10% and 15% were serviced from hospitals in the East North Central, Middle Atlantic, Pacific, and West South Central regions. Most hospitals providing care for patient cohorts served urban populations (88%) and were large, with hospital bed numbers 300. Approximately 60% of hospitals were non‐teaching hospitals.

Healthcare Utilization, Readmission, and Cost Differences Among Patient Cohorts

The mean LOS (8.8 10.3 vs 7.7 8.5, P < 0.001), a difference of 1.1 days and mean ICU LOS (5.5 7.9 vs 4.9 7.1 days, P < 0.001), a difference of 0.6 days were significantly greater for the HN cohort in comparison to the non‐HN cohort (Table 2). The increase in healthcare resource utilization of patients with HN was reflected in their significantly higher mean total hospital costs per admission ($15,281 $24,054 vs $13,439 $22,198, P < 0.001), a difference of $1842; and mean costs incurred in the ICU ($8525 $13,342 vs $7597 $12,695, P < 0.001), a difference of $928 (Table 2). Furthermore, patients in the HN cohort were significantly more likely to be readmitted to the hospital for any cause (17.5% vs 16.4%, P < 0.001) (Table 2).

Outcome Measurements for Matched Cohorts of Hyponatremic and Non‐Hyponatremic Patients
 HyponatremicNon‐HyponatremicP Value
  • Abbreviations: ICU, intensive care unit; LOS, length of stay; SD, standard deviation.

Total LOS (mean SD)8.8 10.37.7 8.5<0.001
Total hospitalization cost (mean SD)$15,281 $24,054$13,439 $22,198<0.001
ICU admission (N, %)129,235 (23.1%)123,502 (22.1%)<0.001
ICU LOS (mean SD)5.5 7.94.9 7.1<0.001
ICU cost (mean SD)$8,525 $13,342$7,597 $12,695<0.001
30‐Day all cause readmission (N, %)96,063 (17.5%)87,058 (16.4%)<0.001

Multivariate Analysis

Multivariate analysis demonstrated hyponatremia was associated with an increase in mean hospital LOS of 10.9%, [95% confidence interval: 10.4%11.5%], (P < 0.0001) and an increase in mean total hospital costs of 8.2%, [7.4%9.0%], (P < 0.0001) (Table 3). Additionally, hyponatremia was associated with an increase in ICU LOS of 10.2%, [8.7%11.8%], (P < 0.0001), and a higher ICU cost of 8.9%, [7.2%10.7%], (P < 0.0001) (Table 3). Hyponatremia was not associated with a greater likelihood of ICU admission (odds ratio = 1.0; [1.01.0], P =.5760). However, the condition was associated with a significantly greater chance of hospital readmission (odds ratio = 1.2, [1.11.2], P < 0.0001) within 30 days postdischarge (Table 3).

Relative Difference (Mean [CI]) in Healthcare Utilization, Costs, and Odds for ICU and Early Readmission Based on Multivariate Analysis for Hyponatremic Patients vs Non‐Hyponatremic Patients
 Overall Cohort N = 1,117,630CAP N = 52,582CHF N = 46,040UTI N = 28,746COPD N = 17,392
  • Abbreviations: CAP, community‐acquired pneumonia; CHF, congestive heart failure; CI, confidence interval [lower, upper]; COPD, chronic obstructive pulmonary disease; ICU, intensive care unit; LOS, length of stay; UTI, urinary tract infection. *The number and percentage of patients, hyponatremic and non‐hyponatremic in each group, admitted to the ICU were the following: overall: 252,737 (22.6%); CAP: 6321 (12.0%); CHF: 8293 (18.0%); UTI: 1243 (4.4%); COPD: 1687 (9.7%). The number and percentage of patients, hyponatremic and non‐hyponatremic in each group, admitted to the hospital within 30 days of discharge were the following: overall: 183,121 (16.4%); CAP: 7310 (14.6%); CHF: 10,466 (24.0%); UTI: 4306 (15.3%); COPD: 3346 (19.7%).

Total LOS difference10.9% [10.4%, 11.5%] P < 0.00015.4% [4.4%, 6.5%] P < 0.000120.6% [19.0%, 22.2%] P < 0.00012.7% [1.2%, 4.2%] P = 0.00036.7% [4.8%, 8.5%] P < 0.0001
Total cost difference8.2% [7.4%, 9.0%] P < 0.00015.6% [4.0%, 7.0%] P < 0.000119.2% [17.1%, 21.4%] P < 0.00012.2% [1.4%, 4.4%] P = 0.05825.6% [3.0%, 8.2%] P < 0.0001
ICU admission* odds ratio1.0 [1.0, 1.0] P = 0.57601.0 [1.0, 1.1] P = 0.83631.2 [1.1, 1.3] P < 0.00011.2 [1.1, 1.3] P = 0.00381.0 [0.9, 1.1] P = 0.5995
ICU LOS difference10.2% [8.7%, 11.8%] P < 0.00018.4% [3.4%, 13.7%] P = 0.000824.8% [19.9%, 29.9%] P < 0.00014.9% [4.0%, 14.7%] P = 0.289310.1% [1.5%, 19.3%] P = 0.0204
ICU cost difference8.9% [7.2%, 10.7%] P < 0.00018.2% [2.7%, 14.0%] P = 0.002924.1% [18.9%, 29.7%] P < 0.00012.8% [6.7%, 13.4%] P = 0.57398.4% [0.6%, 18.1%] P = 0.6680
30‐Day readmission odds ratio1.2 [1.1, 1.2] P < 0.00011.0 [0.9, 1.0] P = 0.51411.1 [1.1, 1.2] P < 0.00011.0 [1.0, 1.1] P = 0.52111.0 [1.0, 1.1] P = 0.6119

Subgroup Sensitivity Analyses

For patients with CAP (n = 52,582), CHF (n = 46,040), a UTI (n = 28,476), or COPD (n = 17,392), the percent increases in LOS and all cause hospitalization cost of the HN cohort in comparison to the non‐HN cohort were 5.4% (P < 0.0001) and 5.6% (P < 0.0001), 20.6% (P < 0.0001) and 19.2% (P < 0.0001), 2.7% (P = 0.0003) and 2.2% (P = 0.0582), and 6.7% (P < 0.0001) and 5.6% (P < 0.0001), respectively (Table 3).

DISCUSSION

This large‐scale, real‐world hospital database study provides healthcare utilization and cost data on the largest population of HN patients that has currently been studied. The results of this study are consistent with others in showing that HN patients use healthcare services more extensively, and represent a patient population which is more expensive to treat in the inpatient setting.5, 22 Additionally, this study yields new findings in that patients in the real‐world with hyponatremia resulting from various etiologies are more likely to be readmitted to the hospital than patients with similar demographics and characteristics who do not have hyponatremia. The results of the subgroup analysis were generally consistent with the results for the overall matched population, as the incremental burden estimates were directionally consistent. However, among the specific subgroups of patients, although it appears that hyponatremia is predictive of hospital readmission in patients with CHF, it did not necessarily correspond with hospital readmission rates among patients with CAP, UTI, or COPD. Therefore, hyponatremia, at least for patients with these latter conditions, may not be predictive of readmission, but remains associated with increased healthcare utilization during the initial hospitalization. Further evaluation of the incremental burden of hyponatremia among patients with specific disease conditions is needed to validate the findings.

The difference in hospital LOS at first admission between HN and non‐HN patients in this study was 1.1 days, and comparable to that reported by Shorr et al.23 In the study by Shorr et al, patients hospitalized for congestive heart failure (CHF), who were HN, had a 0.5 day increased LOS, and those who were severely HN had a 1.3 day increased LOS.23 Two other retrospective cohort analyses reported a 1.4 day5 and 2.0 day22 increased LOS for HN patients in comparison to non‐HN patients. Zilberberg et al and Callahan et al additionally reported that HN patients had a significantly greater need for ICU (4%10%).5, 22 In the present study, LOS in the ICU and associated costs were also compared among HN and non‐HN cohorts and, after adjustment for key patient characteristics, hyponatremia was associated with an incremental increase of 10.2% for ICU LOS and an 8.9% increase in ICU cost.

In this study, patients with hyponatremia of any severity were found to have a mean increase in hospital cost per admission of $1842, with multivariate analysis demonstrating an associated incremental increase of 8.2%. These results are also comparable to that reported in other retrospective cohort analyses. Shorr et al reported that in‐hospital costs attributed to hyponatremia were $509, and for severe hyponatremia were $1132.23 Callahan et al reported a $1200 increase in costs per admission for patients with mild‐to‐moderate hyponatremia, and a $3540 increase for patients with moderate‐to‐severe hyponatremia, in comparison to non‐HN patients.22

The Institute for Healthcare Improvement reports that hospitalizations account for nearly one‐third of the $882 billion spent on healthcare in the US in 2011, and that a substantial fraction of all hospitalizations are readmissions.13, 24 A recent meta‐analysis of 30‐day hospital readmission rates found 23.1% were classified as avoidable.25 Readmission rates of HN patients in comparison to non‐HN patients have been reported in 3 other studies, all of which were conducted on clinical trial patients with heart failure.7, 8, 26 Two of these studies, which evaluated only patients with acute class IV heart failure,8, 26 reported that hyponatremia was associated with a significant increase in readmission rates (up to 20% higher), and the other, which evaluated any patients hospitalized for heart failure, did not.7 The differences there may be partially attributed to the differences in heart failure severity among patient populations, as hyponatremia is an independent predictor of worsening heart failure. The only published study to date on the influence of hyponatremia on readmission rates in the real‐world was conducted by Scherz et al, who reported that the co‐occurrence of hyponatremia in patients with acute pulmonary embolism, discharged from 185 hospitals in Pennsylvania, was independently associated with an increased readmission rate of 19.3%.27 In the present study, hyponatremia was associated with an incremental increase ranging between 14% and 17% for hospital readmission for any cause. It was conducted on a patient population in which hyponatremia was resultant from many causes, and not all patients had a serious comorbid condition. Also, the HN and non‐HN cohorts were matched for comorbidity prevalence and disease severity, and in other studies they were not. Therefore, the results of this study importantly imply that hyponatremia, whether it is resultant from a serious disease or any other cause substantially increases the healthcare burden. The implementation of strategies to prevent hospital readmissions may play an important role in reducing the healthcare burden of hyponatremia, and future studies are warranted to evaluate this hypothesis alongside evaluation of the outpatient hyponatremia burden.

The limitations of this study include, firstly, that it is only representative of inpatient hospital costs, and excludes outpatient healthcare utilization and costs. Secondly, this study utilized the Premier Hospital Database for patient selection, and laboratory testing data for serum sodium level are not available in this database; therefore, the severity of hyponatremia could not be accurately established in the HN patient population. Thirdly, the occurrence of hyponatremia in patients with some diseases is a marker of disease severity, as is the case with congestive heart failure and cirrhosis.23, 28 Our study did not adjust for the specific disease (eg, CHF) severity, which may influence the results. Future research is needed to evaluate the impact of hyponatremia on underlying disease severity of other diseases, and how its co‐occurrence may influence healthcare resource utilization and cost in each case. Although the Premier Hospital Database contains information from a large number of hospitals across the US, it is possible that it may not be representative of the entire US population of HN patients. Additionally, billing and coding errors and missing data could potentially have occurred, although the large patient population size likely precludes a large impact on the results of this study. Finally, the frequency of use of fluid restriction in these hospital settings could not be chronicled, thus limiting the ability to assess therapies and treatment modalities in use.

Acknowledgements

The authors acknowledge Melissa Lingohr‐Smith from Novosys Health in the editorial support and review of this manuscript.

Disclosures: This research was supported by Otsuka America Pharmaceutical, Inc, Princeton, NJ, which manufactures tolvaptan for the treatment of hyponatremia. Drs Amin and Deitelzweig are consultants for, and have received honoraria from, Otsuka America Pharmaceutical in connection with conducting this study. Drs Christian and Friend are employees of Otsuka America Pharmaceutical. Dr Lin is an employee of Novosys Health, which has received research funds from Otsuka America Pharmaceutical in connection with conducting this study and development of this manuscript. D. Baumer and Dr. Lowe are employees of Premier Inc, which has received research funds from Otsuka America Pharmaceutical. K. Belk was previously employed with Premier Inc.

Hyponatremia is an electrolyte disorder most commonly defined as a serum sodium concentration <135 mEq/L.1 Its exact definition can vary across studies, but typically ranges between <130 and <138 mEq/L.2, 3 Signs and symptoms of hyponatremia can include malaise, headache, disorientation, confusion, muscle weakness, and cramps. If severe, seizures, respiratory arrest, brainstem herniation, coma, and death may result.

The incidence of hyponatremia in the general hospitalized population has been reported to range between 1% and 6% when defined as <130135 mEq/L,4, 5 and its occurrence increases with a more prolonged hospital stay to 13%.6 A recent study reported that when hyponatremia was defined with a less stringent threshold of <138 mEq/L, the incidence at admission rose to 38%.3 Hyponatremia is a comorbid condition of multiple diseases, occurring in approximately 20% of patients with heart failure,7, 8 and 40% to 57% of patients with advanced cirrhosis.9, 10 The syndrome of the inappropriate release of antidiuretic hormone (SIADH) is additionally a predominant cause of hyponatremia, with a prevalence reported as high as 35% in hospitalized patients.11

Hyponatremia is not only widespread, but also an independent predictor of mortality. In a retrospective cohort analysis, Waikar et al reported that in comparison to patients who were normonatremic, patients with serum sodium concentrations <135 mEq/L had a risk of in‐hospital mortality as high as 47%, and that this risk doubled for patients with serum sodium concentrations between 125 and 129 mEq/L.6 In the study by Wald et al, which defined hyponatremia as <138 mEq/L, the risk of in‐hospital mortality was similar.3 In both of these studies, even mild hyponatremia (130137 mEq/L) was associated with increased risk of in‐hospital mortality.3, 6

The overall cost of hyponatremia is estimated to range between $1.6 and $3.6 billion for 2011.12, 13 Hospital readmissions are a significant contributor to total healthcare costs, with some being entirely avoidable with increased standards of care. The Centers for Medicare and Medicaid Services has begun to not only publicly report hospital readmission rates, but also penalize hospitals for early readmissions.14 Strategies to reduce hospital readmissions are currently being integrated into healthcare reform policy.14 In the present study, the incremental burden of hospitalized hyponatremic (HN) versus non‐HN patients in terms of hospital resource utilization, costs, and early hospital readmission in the real‐world was evaluated.

METHODS

Study Design

This study was a retrospective analysis that examined healthcare utilization and costs among HN patients using the Premier Hospital Database. The database contains over 310 million hospital encounters from more than 700 US hospitals, or 1 out of every 4 discharges in the US. The administrative data available included patient and provider demographics, diagnoses and procedures, as well as date‐stamped billing records for all pharmacy, laboratory, imaging, procedures, and supplies.

Patient Selection and Matching

HN patients were eligible for study inclusion if they were a hospital inpatient discharged between January 1, 2007 and March 31, 2010, were >18 years of age at admission, and had either a primary or secondary diagnosis of hyponatremia or hyposmolality (defined as International Classification of Diseases, Ninth Revision (ICD‐9‐CM) code: 276.1x). Patients were excluded if they had been transferred from another acute care facility, transferred to another acute care or critical access facility, or left against medical advice. Labor and delivery patients (ICD‐9‐CM codes: 72.xx‐74.xx, V22.x, V23.x, V27.x, and V28.x), and patients classified as observational were also excluded. A second cohort of non‐hyponatremic patients was created using the same inclusion and exclusion criteria, with the exception that patients not have a primary or secondary diagnosis of hyponatremia or hyposmolality (defined as ICD‐9‐CM code: 276.1x).

The matching of hyponatremia (HN) and control (non‐HN) cohorts was accomplished using a combination of exact and propensity score matching techniques. Patients were first exact matched on age, gender, Medicare Severity‐Diagnosis Related Group (MS‐DRG) assignment, and hospital geographic region. Propensity score matching was further utilized to create the final study cohorts for outcomes comparisons. Propensity score matching is commonly used in retrospective cohort studies to correct for sample selection bias due to observable differences between groups.15, 16 The propensity score was generated using logistic regression with the dependent variable as hyponatremia (yes vs no) and the following covariates: age, race, admission source, attending physician specialty, 3M All Patient Refined‐Diagnosis Related Group (APR‐DRG) Severity of Illness and Risk of Mortality index scores, Deyo‐Charlson Comorbidity Index score, selected hyponatremia‐related comorbidity conditions, and hospital size, region, and urban/rural designation. These covariates were initially selected by an expert panel of physicians, and backward selection was utilized in the logistic regression using the most parsimonious model.17

Following generation of the propensity scores, HN patients were matched to non‐HN patients 1:1 using a nearest neighbor matching algorithm, including hospital identification and propensity score.18 Inclusion of hospital identification in the matching sequence, as well as provider characteristics, especially hospital size, attending physician specialty, and geographic region in the propensity score, was used to control for potential clustering effects at the physician and hospital level.19 During the propensity score matching process, likelihood‐ratio test, Hosmer‐Lemshow goodness of fit, and concordance c statistics were utilized to assess the fitness of the models.20 The final propensity score model produced a concordance c statistic of 0.8.

Outcome Measures and Statistical Analyses

The following outcome measures were compared between the matched HN and non‐HN patient cohorts: total and intensive care unit (ICU) hospitalization costs, total and ICU length of stay (LOS), ICU admission, and 30‐day hospital readmission. Bivariate descriptive statistics were employed to test for significant differences in demographics, patient clinical characteristics, and unadjusted costs and healthcare resource utilization and readmission rates between patient cohorts. To detect statistically significant differences in continuous and categorical variables, respectively, t tests and chi‐square tests were performed.

Multivariate analysis of outcome measures utilized generalized linear models. Due to the skewed nature of LOS and cost data, LOS was analyzed using multivariate negative binomial regression and cost was analyzed using multivariate gamma regression.21 Binary outcomes (ICU admission and 30‐day readmission) were analyzed using multivariate logistic regression. The analysis accounted for potential confounding factors by inclusion of the following covariates: age group, gender, race, admission source, and Deyo‐Charlson Comorbidity Index score. These covariates were previously identified in the Wald et al hyponatremia study,3 and were verified using likelihood‐ratio, Hosmer‐Lemshow goodness of fit, and concordance c statistics.

Subgroup Sensitivity Analysis

For the subgroup sensitivity analyses, patients were identified as having community‐acquired pneumonia (CAP), congestive heart failure (CHF), urinary tract infection (UTI), or chronic obstructive pulmonary disease (COPD) based upon principal diagnosis codes. Patients were categorized according to these subgroup definitions, and then previously matched patients with the same subgroup classification constituted the final analysis set for each subgroup. The methods that were used for the overall matched analysis were then applied to each subgroup to evaluate the incremental burden of overall cost and LOS associated with hyponatremia.

RESULTS

Patient Population

Of the 606,057 HN patients eligible for matching, a total of 558,815 HN patients were matched to 558,815 non‐HN patients, a 92% match ratio. Table 1 describes the overall characteristics of the patient populations. For both cohorts, median age was 70 years, 57% of patients were female, and approximately 67% were white. The majority of patients in either cohort had Medicare coverage (55%), and approximately 75% of patients entered the hospital via the emergency room with nearly 70% having a 3M APR‐DRG disease severity level of major or extreme. Patients of both cohorts were most often attended by an internist or a hospitalist, with a combined percentage of approximately 60%. A small, but greater proportion of HN patients had comorbidities of cancer, pulmonary disease, and SIADH. Comorbid conditions of liver cirrhosis/hepatic disease and human immunodeficiency virus (HIV) were similarly distributed among both patient cohorts.

Baseline Demographics and Clinical and Hospital Characteristics for Matched Cohorts of Hyponatremic and Non‐Hyponatremic Patients
 HyponatremicNon‐Hyponatremic
Discharges N (%)Discharges N (%)
  • Abbreviations: APR‐DRG, all patient refined diagnosis‐related groups; CCI, Charlson Comorbidity Index; COPD, chronic obstructive pulmonary disease; IQR, interquartile range; SIADH, syndrome of inappropriate antidiuretic hormone hypersecretion.

Sample Discharges558,815 (100.0%)558,815 (100.0%)
Age median (IQR)70.00 (57.081.0)70.00 (57.081.0)
Gender
Female319,069 (57.1%)319,069 (57.1%)
Male239,746 (42.9%)239,746 (42.9%)
Race
American Indian3,465 (0.6%)3,448 (0.6%)
Asian/Pacific10,065 (1.8%)9,690 (1.7%)
Black63,776 (11.4%)66,233 (11.9%)
Hispanic24,341 (4.4%)24,426 (4.4%)
White377,434 (67.5%)376,639 (67.4%)
Other/unknown79,734 (14.3%)78,379 (14.0%)
Primary payer
Medicaretraditional310,312 (55.5%)310,643 (55.6%)
Managed care75,476 (13.5%)78,184 (14.0%)
Medicaremanaged care45,439 (8.1%)47,947 (8.6%)
Non‐cap
Medicaid44,690 (8.0%)43,767 (7.8%)
Other82,898 (14.8%)78,274 (14.0%)
Admission source
Physician referral5,022 (0.9%)4,636 (0.8%)
Transfer from another nonacute health facility18,031 (3.2%)18,163 (3.3%)
Emergency room417,556 (74.7%)420,401 (75.2%)
Other/unknown118,206 (21.2%)115,615 (20.7%)
APR‐DRG severity of illness
1Minor14,257 (2.6%)12,993 (2.3%)
2Moderate174,859 (31.3%)179,356 (32.1%)
3Major263,814 (47.2%)265,422 (47.5%)
4Extreme105,885 (19.0%)101,044 (18.1%)
Attending physician specialty
Internal Medicine235,628 (42.1%)240,875 (43.1%)
Hospitalist88,250 (15.8%)87,720 (15.7%)
Family Practice (FP)73,346 (13.1%)72,828 (13.0%)
Orthopedic Surgery (ORS)23,595 (4.2%)22,949 (4.1%)
Cardiovascular Diseases (CD)19,521 (3.5%)18,057 (3.2%)
Comorbidities
Human immunodeficiency virus3,971 (0.7%)4,018 (0.7%)
Cancer/neoplasm/malignancy107,851 (19.3%)105,199 (18.8%)
Pulmonary disease77,849 (13.9%)77,184 (13.8%)
Cirrhosis/hepatic disease23,038 (4.1%)23,418 (4.2%)
SIADH1,972 (0.4%)1,278 (0.2%)
Subgroup populations
Community‐acquired pneumonia26,291 (4.7%)26,291 (4.7%)
Congestive heart failure23,020 (4.1%)23,020 (4.1%)
Urinary tract infection14,238 (2.6%)14,238 (2.6%)
COPD8,696 (1.6%)8,696 (1.6%)
CCI median (IQR)3.0 (1.05.0)3.0 (1.05.0)
No. of premier hospitals459
Provider region
East North Central78,332 (14.0%)78,332 (14.0%)
East South Central32,122 (5.8%)32,122 (5.8%)
Middle Atlantic73,846 (13.2%)73,846 (13.2%)
Mountain23,761 (4.3%)23,761 (4.3%)
New England9,493 (1.7%)9,493 (1.7%)
Pacific73,059 (13.1%)73,059 (13.1%)
South Atlantic175,194 (31.4%)175,194 (31.4%)
West North Central37,913 (6.8%)37,913 (6.8%)
West South Central55,095 (9.9%)55,095 (9.9%)
Population served
Rural69,749 (12.5%)68,414 (12.2%)
Urban489,066 (87.5%)490,401 (87.8%)
Teaching status
Non‐teaching337,620 (60.4%)337,513 (60.4%)
Teaching221,195 (39.6%)221,302 (39.6%)
No. of hospital beds
69922,067 (4.0%)21,777 (3.9%)
10019957,367 (10.3%)56,097 (10.0%)
20029987,563 (15.7%)86,639 (15.5%)
300499218,834 (39.2%)220,248 (39.4%)
500+172,984 (31.0%)174,054 (31.2%)

Hospital Characteristics

Patient cohorts had similar distributions with respect to hospital characteristics (Table 1). Approximately 30% of patients were provided care from hospitals located in the South Atlantic region, and between 10% and 15% were serviced from hospitals in the East North Central, Middle Atlantic, Pacific, and West South Central regions. Most hospitals providing care for patient cohorts served urban populations (88%) and were large, with hospital bed numbers 300. Approximately 60% of hospitals were non‐teaching hospitals.

Healthcare Utilization, Readmission, and Cost Differences Among Patient Cohorts

The mean LOS (8.8 10.3 vs 7.7 8.5, P < 0.001), a difference of 1.1 days and mean ICU LOS (5.5 7.9 vs 4.9 7.1 days, P < 0.001), a difference of 0.6 days were significantly greater for the HN cohort in comparison to the non‐HN cohort (Table 2). The increase in healthcare resource utilization of patients with HN was reflected in their significantly higher mean total hospital costs per admission ($15,281 $24,054 vs $13,439 $22,198, P < 0.001), a difference of $1842; and mean costs incurred in the ICU ($8525 $13,342 vs $7597 $12,695, P < 0.001), a difference of $928 (Table 2). Furthermore, patients in the HN cohort were significantly more likely to be readmitted to the hospital for any cause (17.5% vs 16.4%, P < 0.001) (Table 2).

Outcome Measurements for Matched Cohorts of Hyponatremic and Non‐Hyponatremic Patients
 HyponatremicNon‐HyponatremicP Value
  • Abbreviations: ICU, intensive care unit; LOS, length of stay; SD, standard deviation.

Total LOS (mean SD)8.8 10.37.7 8.5<0.001
Total hospitalization cost (mean SD)$15,281 $24,054$13,439 $22,198<0.001
ICU admission (N, %)129,235 (23.1%)123,502 (22.1%)<0.001
ICU LOS (mean SD)5.5 7.94.9 7.1<0.001
ICU cost (mean SD)$8,525 $13,342$7,597 $12,695<0.001
30‐Day all cause readmission (N, %)96,063 (17.5%)87,058 (16.4%)<0.001

Multivariate Analysis

Multivariate analysis demonstrated hyponatremia was associated with an increase in mean hospital LOS of 10.9%, [95% confidence interval: 10.4%11.5%], (P < 0.0001) and an increase in mean total hospital costs of 8.2%, [7.4%9.0%], (P < 0.0001) (Table 3). Additionally, hyponatremia was associated with an increase in ICU LOS of 10.2%, [8.7%11.8%], (P < 0.0001), and a higher ICU cost of 8.9%, [7.2%10.7%], (P < 0.0001) (Table 3). Hyponatremia was not associated with a greater likelihood of ICU admission (odds ratio = 1.0; [1.01.0], P =.5760). However, the condition was associated with a significantly greater chance of hospital readmission (odds ratio = 1.2, [1.11.2], P < 0.0001) within 30 days postdischarge (Table 3).

Relative Difference (Mean [CI]) in Healthcare Utilization, Costs, and Odds for ICU and Early Readmission Based on Multivariate Analysis for Hyponatremic Patients vs Non‐Hyponatremic Patients
 Overall Cohort N = 1,117,630CAP N = 52,582CHF N = 46,040UTI N = 28,746COPD N = 17,392
  • Abbreviations: CAP, community‐acquired pneumonia; CHF, congestive heart failure; CI, confidence interval [lower, upper]; COPD, chronic obstructive pulmonary disease; ICU, intensive care unit; LOS, length of stay; UTI, urinary tract infection. *The number and percentage of patients, hyponatremic and non‐hyponatremic in each group, admitted to the ICU were the following: overall: 252,737 (22.6%); CAP: 6321 (12.0%); CHF: 8293 (18.0%); UTI: 1243 (4.4%); COPD: 1687 (9.7%). The number and percentage of patients, hyponatremic and non‐hyponatremic in each group, admitted to the hospital within 30 days of discharge were the following: overall: 183,121 (16.4%); CAP: 7310 (14.6%); CHF: 10,466 (24.0%); UTI: 4306 (15.3%); COPD: 3346 (19.7%).

Total LOS difference10.9% [10.4%, 11.5%] P < 0.00015.4% [4.4%, 6.5%] P < 0.000120.6% [19.0%, 22.2%] P < 0.00012.7% [1.2%, 4.2%] P = 0.00036.7% [4.8%, 8.5%] P < 0.0001
Total cost difference8.2% [7.4%, 9.0%] P < 0.00015.6% [4.0%, 7.0%] P < 0.000119.2% [17.1%, 21.4%] P < 0.00012.2% [1.4%, 4.4%] P = 0.05825.6% [3.0%, 8.2%] P < 0.0001
ICU admission* odds ratio1.0 [1.0, 1.0] P = 0.57601.0 [1.0, 1.1] P = 0.83631.2 [1.1, 1.3] P < 0.00011.2 [1.1, 1.3] P = 0.00381.0 [0.9, 1.1] P = 0.5995
ICU LOS difference10.2% [8.7%, 11.8%] P < 0.00018.4% [3.4%, 13.7%] P = 0.000824.8% [19.9%, 29.9%] P < 0.00014.9% [4.0%, 14.7%] P = 0.289310.1% [1.5%, 19.3%] P = 0.0204
ICU cost difference8.9% [7.2%, 10.7%] P < 0.00018.2% [2.7%, 14.0%] P = 0.002924.1% [18.9%, 29.7%] P < 0.00012.8% [6.7%, 13.4%] P = 0.57398.4% [0.6%, 18.1%] P = 0.6680
30‐Day readmission odds ratio1.2 [1.1, 1.2] P < 0.00011.0 [0.9, 1.0] P = 0.51411.1 [1.1, 1.2] P < 0.00011.0 [1.0, 1.1] P = 0.52111.0 [1.0, 1.1] P = 0.6119

Subgroup Sensitivity Analyses

For patients with CAP (n = 52,582), CHF (n = 46,040), a UTI (n = 28,476), or COPD (n = 17,392), the percent increases in LOS and all cause hospitalization cost of the HN cohort in comparison to the non‐HN cohort were 5.4% (P < 0.0001) and 5.6% (P < 0.0001), 20.6% (P < 0.0001) and 19.2% (P < 0.0001), 2.7% (P = 0.0003) and 2.2% (P = 0.0582), and 6.7% (P < 0.0001) and 5.6% (P < 0.0001), respectively (Table 3).

DISCUSSION

This large‐scale, real‐world hospital database study provides healthcare utilization and cost data on the largest population of HN patients that has currently been studied. The results of this study are consistent with others in showing that HN patients use healthcare services more extensively, and represent a patient population which is more expensive to treat in the inpatient setting.5, 22 Additionally, this study yields new findings in that patients in the real‐world with hyponatremia resulting from various etiologies are more likely to be readmitted to the hospital than patients with similar demographics and characteristics who do not have hyponatremia. The results of the subgroup analysis were generally consistent with the results for the overall matched population, as the incremental burden estimates were directionally consistent. However, among the specific subgroups of patients, although it appears that hyponatremia is predictive of hospital readmission in patients with CHF, it did not necessarily correspond with hospital readmission rates among patients with CAP, UTI, or COPD. Therefore, hyponatremia, at least for patients with these latter conditions, may not be predictive of readmission, but remains associated with increased healthcare utilization during the initial hospitalization. Further evaluation of the incremental burden of hyponatremia among patients with specific disease conditions is needed to validate the findings.

The difference in hospital LOS at first admission between HN and non‐HN patients in this study was 1.1 days, and comparable to that reported by Shorr et al.23 In the study by Shorr et al, patients hospitalized for congestive heart failure (CHF), who were HN, had a 0.5 day increased LOS, and those who were severely HN had a 1.3 day increased LOS.23 Two other retrospective cohort analyses reported a 1.4 day5 and 2.0 day22 increased LOS for HN patients in comparison to non‐HN patients. Zilberberg et al and Callahan et al additionally reported that HN patients had a significantly greater need for ICU (4%10%).5, 22 In the present study, LOS in the ICU and associated costs were also compared among HN and non‐HN cohorts and, after adjustment for key patient characteristics, hyponatremia was associated with an incremental increase of 10.2% for ICU LOS and an 8.9% increase in ICU cost.

In this study, patients with hyponatremia of any severity were found to have a mean increase in hospital cost per admission of $1842, with multivariate analysis demonstrating an associated incremental increase of 8.2%. These results are also comparable to that reported in other retrospective cohort analyses. Shorr et al reported that in‐hospital costs attributed to hyponatremia were $509, and for severe hyponatremia were $1132.23 Callahan et al reported a $1200 increase in costs per admission for patients with mild‐to‐moderate hyponatremia, and a $3540 increase for patients with moderate‐to‐severe hyponatremia, in comparison to non‐HN patients.22

The Institute for Healthcare Improvement reports that hospitalizations account for nearly one‐third of the $882 billion spent on healthcare in the US in 2011, and that a substantial fraction of all hospitalizations are readmissions.13, 24 A recent meta‐analysis of 30‐day hospital readmission rates found 23.1% were classified as avoidable.25 Readmission rates of HN patients in comparison to non‐HN patients have been reported in 3 other studies, all of which were conducted on clinical trial patients with heart failure.7, 8, 26 Two of these studies, which evaluated only patients with acute class IV heart failure,8, 26 reported that hyponatremia was associated with a significant increase in readmission rates (up to 20% higher), and the other, which evaluated any patients hospitalized for heart failure, did not.7 The differences there may be partially attributed to the differences in heart failure severity among patient populations, as hyponatremia is an independent predictor of worsening heart failure. The only published study to date on the influence of hyponatremia on readmission rates in the real‐world was conducted by Scherz et al, who reported that the co‐occurrence of hyponatremia in patients with acute pulmonary embolism, discharged from 185 hospitals in Pennsylvania, was independently associated with an increased readmission rate of 19.3%.27 In the present study, hyponatremia was associated with an incremental increase ranging between 14% and 17% for hospital readmission for any cause. It was conducted on a patient population in which hyponatremia was resultant from many causes, and not all patients had a serious comorbid condition. Also, the HN and non‐HN cohorts were matched for comorbidity prevalence and disease severity, and in other studies they were not. Therefore, the results of this study importantly imply that hyponatremia, whether it is resultant from a serious disease or any other cause substantially increases the healthcare burden. The implementation of strategies to prevent hospital readmissions may play an important role in reducing the healthcare burden of hyponatremia, and future studies are warranted to evaluate this hypothesis alongside evaluation of the outpatient hyponatremia burden.

The limitations of this study include, firstly, that it is only representative of inpatient hospital costs, and excludes outpatient healthcare utilization and costs. Secondly, this study utilized the Premier Hospital Database for patient selection, and laboratory testing data for serum sodium level are not available in this database; therefore, the severity of hyponatremia could not be accurately established in the HN patient population. Thirdly, the occurrence of hyponatremia in patients with some diseases is a marker of disease severity, as is the case with congestive heart failure and cirrhosis.23, 28 Our study did not adjust for the specific disease (eg, CHF) severity, which may influence the results. Future research is needed to evaluate the impact of hyponatremia on underlying disease severity of other diseases, and how its co‐occurrence may influence healthcare resource utilization and cost in each case. Although the Premier Hospital Database contains information from a large number of hospitals across the US, it is possible that it may not be representative of the entire US population of HN patients. Additionally, billing and coding errors and missing data could potentially have occurred, although the large patient population size likely precludes a large impact on the results of this study. Finally, the frequency of use of fluid restriction in these hospital settings could not be chronicled, thus limiting the ability to assess therapies and treatment modalities in use.

Acknowledgements

The authors acknowledge Melissa Lingohr‐Smith from Novosys Health in the editorial support and review of this manuscript.

Disclosures: This research was supported by Otsuka America Pharmaceutical, Inc, Princeton, NJ, which manufactures tolvaptan for the treatment of hyponatremia. Drs Amin and Deitelzweig are consultants for, and have received honoraria from, Otsuka America Pharmaceutical in connection with conducting this study. Drs Christian and Friend are employees of Otsuka America Pharmaceutical. Dr Lin is an employee of Novosys Health, which has received research funds from Otsuka America Pharmaceutical in connection with conducting this study and development of this manuscript. D. Baumer and Dr. Lowe are employees of Premier Inc, which has received research funds from Otsuka America Pharmaceutical. K. Belk was previously employed with Premier Inc.

References
  1. Vaidya C,Ho W,Freda BJ.Management of hyponatremia: providing treatment and avoiding harm.Cleve Clin J Med.2010;77(10):715726.
  2. Palmer BF,Gates JR,Lader M.Causes and management of hyponatremia.Ann Pharmacother.2003;37(11):16941702.
  3. Wald R,Jaber BL,Price LL,Upadhyay A,Madias NE.Impact of hospital‐associated hyponatremia on selected outcomes.Arch Intern Med.2010;170(3):294302.
  4. Anderson RJ,Chung HM,Kluge R,Schrier RW.Hyponatremia: a prospective analysis of its epidemiology and the pathogenetic role of vasopressin.Ann Intern Med.1985;102(2):164168.
  5. Zilberberg MD,Exuzides A,Spalding J, et al.Epidemiology, clinical and economic outcomes of admission hyponatremia among hospitalized patients.Curr Med Res Opin.2008;24(6):16011608.
  6. Waikar SS,Mount DB,Curhan GC.Mortality after hospitalization with mild, moderate, and severe hyponatremia.Am J Med.2009;122(9):857865.
  7. Gheorghiade M,Abraham WT,Albert NM, et al.Relationship between admission serum sodium concentration and clinical outcomes in patients hospitalized for heart failure: an analysis from the OPTIMIZE‐HF registry.Eur Heart J.2007;28(8):980988.
  8. Gheorghiade M,Rossi JS,Cotts W, et al.Characterization and prognostic value of persistent hyponatremia in patients with severe heart failure in the ESCAPE Trial.Arch Intern Med.2007;167(18):19982005.
  9. Angeli P,Wong F,Watson H,Ginès P.Hyponatremia in cirrhosis: results of a patient population survey.Hepatology.2006;44(6):15351542.
  10. Ginés P,Berl T,Bernardi M, et al.Hyponatremia in cirrhosis: from pathogenesis to treatment.Hepatology.1998;28(3):851864.
  11. Esposito P,Piotti G,Bianzina S,Malul Y,Dal Canton A.The syndrome of inappropriate antidiuresis: pathophysiology, clinical management and new therapeutic options.Nephron Clin Pract.2011;119(1):c62c73.
  12. Boscoe A,Paramore C,Verbalis JG.Cost of illness of hyponatremia in the United States.Cost Eff Resour Alloc.2006;4:10.
  13. US Health Care Budget: US Budget Breakdown for FY12—Charts. Available at: http://www.usgovernmentspending.com/health_care_budget_2012_1.html. Accessed December 19, 2011.
  14. 111th Congress. Patient Protection and Affordable Care Act. Public Law 111–148.1–906.
  15. Rogers WL.An evaluation of statistical matching.JBES.1984;2(1):91102.
  16. Dehejia RH,Wahba S.Propensity score‐matching methods for nonexperimental causal studies.Rev Econ Stat.2002;84(1):151161.
  17. Menard S. Applied Logistic Regression Analysis. Quantitative Applications in the Social Sciences, Vol106.2nd ed.Thousand Oaks, CA:Sage;2002.
  18. Parsons LS.Reducing bias in a propensity score matched‐pair sample using greedy matching techniques. Paper presented at: Proceedings of the Twenty‐Sixth Annual SAS Users Group International Conference; April 22–25,2001; Long Beach, CA.
  19. Panageas KS,Schrag D,Riedel E,Bach PB,Begg CB.The effect of clustering of outcomes on the association of procedure volume and surgical outcomes.Ann Intern Med.2003;139(8):658665.
  20. Harrell FE.Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis.New York, NY:Springer‐Verlag,2001.
  21. Manning WG,Basu A,Mullahy J.Generalized Modeling Approaches to Risk Adjustment of Skewed Outcomes Data.Cambridge, MA:National Bureau of Economic Research, Inc;2003.
  22. Callahan MA,Do HT,Caplan DW,Yoon‐Flannery K.Economic impact of hyponatremia in hospitalized patients: a retrospective cohort study.Postgrad Med.2009;121(2):186191.
  23. Shorr AF,Tabak YP,Johannes RS, et al.Burden of sodium abnormalities in patients hospitalized for heart failure.Congest Heart Fail.2011;17(1):17.
  24. Institute for Healthcare Improvement. Reduce Avoidable Hospital Readmissions. Available at: http://www.ihi.org/explore/readmissions/Pages/default.aspx. Accessed December 18, 2011.
  25. van Walraven C,Jennings A,Forster AJ.A meta‐analysis of hospital 30‐day avoidable readmission rates.J Eval Clin Pract.2011 Nov 9. doi: 10.1111/j.1365–2753.2011.01773.x. Published online August 17, 2012.
  26. Dunlay SM,Gheorghiade M,Reid KJ, et al.Critical elements of clinical follow‐up after hospital discharge for heart failure: insights from the EVEREST trial.Eur J Heart Fail.2010;12(4):367374.
  27. Scherz N,Labarère J,Méan M, et al.Prognostic importance of hyponatremia in patients with acute pulmonary embolism.Am J Respir Crit Care Med.2010;182(9):11781183.
  28. Jenq CC,Tsai MH,Tian YC, et al.Serum sodium predicts prognosis in critically ill cirrhotic patients.J Clin Gastroenterol.2010;44(3):220226.
References
  1. Vaidya C,Ho W,Freda BJ.Management of hyponatremia: providing treatment and avoiding harm.Cleve Clin J Med.2010;77(10):715726.
  2. Palmer BF,Gates JR,Lader M.Causes and management of hyponatremia.Ann Pharmacother.2003;37(11):16941702.
  3. Wald R,Jaber BL,Price LL,Upadhyay A,Madias NE.Impact of hospital‐associated hyponatremia on selected outcomes.Arch Intern Med.2010;170(3):294302.
  4. Anderson RJ,Chung HM,Kluge R,Schrier RW.Hyponatremia: a prospective analysis of its epidemiology and the pathogenetic role of vasopressin.Ann Intern Med.1985;102(2):164168.
  5. Zilberberg MD,Exuzides A,Spalding J, et al.Epidemiology, clinical and economic outcomes of admission hyponatremia among hospitalized patients.Curr Med Res Opin.2008;24(6):16011608.
  6. Waikar SS,Mount DB,Curhan GC.Mortality after hospitalization with mild, moderate, and severe hyponatremia.Am J Med.2009;122(9):857865.
  7. Gheorghiade M,Abraham WT,Albert NM, et al.Relationship between admission serum sodium concentration and clinical outcomes in patients hospitalized for heart failure: an analysis from the OPTIMIZE‐HF registry.Eur Heart J.2007;28(8):980988.
  8. Gheorghiade M,Rossi JS,Cotts W, et al.Characterization and prognostic value of persistent hyponatremia in patients with severe heart failure in the ESCAPE Trial.Arch Intern Med.2007;167(18):19982005.
  9. Angeli P,Wong F,Watson H,Ginès P.Hyponatremia in cirrhosis: results of a patient population survey.Hepatology.2006;44(6):15351542.
  10. Ginés P,Berl T,Bernardi M, et al.Hyponatremia in cirrhosis: from pathogenesis to treatment.Hepatology.1998;28(3):851864.
  11. Esposito P,Piotti G,Bianzina S,Malul Y,Dal Canton A.The syndrome of inappropriate antidiuresis: pathophysiology, clinical management and new therapeutic options.Nephron Clin Pract.2011;119(1):c62c73.
  12. Boscoe A,Paramore C,Verbalis JG.Cost of illness of hyponatremia in the United States.Cost Eff Resour Alloc.2006;4:10.
  13. US Health Care Budget: US Budget Breakdown for FY12—Charts. Available at: http://www.usgovernmentspending.com/health_care_budget_2012_1.html. Accessed December 19, 2011.
  14. 111th Congress. Patient Protection and Affordable Care Act. Public Law 111–148.1–906.
  15. Rogers WL.An evaluation of statistical matching.JBES.1984;2(1):91102.
  16. Dehejia RH,Wahba S.Propensity score‐matching methods for nonexperimental causal studies.Rev Econ Stat.2002;84(1):151161.
  17. Menard S. Applied Logistic Regression Analysis. Quantitative Applications in the Social Sciences, Vol106.2nd ed.Thousand Oaks, CA:Sage;2002.
  18. Parsons LS.Reducing bias in a propensity score matched‐pair sample using greedy matching techniques. Paper presented at: Proceedings of the Twenty‐Sixth Annual SAS Users Group International Conference; April 22–25,2001; Long Beach, CA.
  19. Panageas KS,Schrag D,Riedel E,Bach PB,Begg CB.The effect of clustering of outcomes on the association of procedure volume and surgical outcomes.Ann Intern Med.2003;139(8):658665.
  20. Harrell FE.Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis.New York, NY:Springer‐Verlag,2001.
  21. Manning WG,Basu A,Mullahy J.Generalized Modeling Approaches to Risk Adjustment of Skewed Outcomes Data.Cambridge, MA:National Bureau of Economic Research, Inc;2003.
  22. Callahan MA,Do HT,Caplan DW,Yoon‐Flannery K.Economic impact of hyponatremia in hospitalized patients: a retrospective cohort study.Postgrad Med.2009;121(2):186191.
  23. Shorr AF,Tabak YP,Johannes RS, et al.Burden of sodium abnormalities in patients hospitalized for heart failure.Congest Heart Fail.2011;17(1):17.
  24. Institute for Healthcare Improvement. Reduce Avoidable Hospital Readmissions. Available at: http://www.ihi.org/explore/readmissions/Pages/default.aspx. Accessed December 18, 2011.
  25. van Walraven C,Jennings A,Forster AJ.A meta‐analysis of hospital 30‐day avoidable readmission rates.J Eval Clin Pract.2011 Nov 9. doi: 10.1111/j.1365–2753.2011.01773.x. Published online August 17, 2012.
  26. Dunlay SM,Gheorghiade M,Reid KJ, et al.Critical elements of clinical follow‐up after hospital discharge for heart failure: insights from the EVEREST trial.Eur J Heart Fail.2010;12(4):367374.
  27. Scherz N,Labarère J,Méan M, et al.Prognostic importance of hyponatremia in patients with acute pulmonary embolism.Am J Respir Crit Care Med.2010;182(9):11781183.
  28. Jenq CC,Tsai MH,Tian YC, et al.Serum sodium predicts prognosis in critically ill cirrhotic patients.J Clin Gastroenterol.2010;44(3):220226.
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The Venous Thromboembolism Quality Improvement Resource Room

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Curriculum development: The venous thromboembolism quality improvement resource room

The goal of this article is to explain how the first in a series of online resource rooms provides trainees and hospitalists with quality improvement tools that can be applied locally to improve inpatient care.1 During the emergence and explosive growth of hospital medicine, the SHM recognized the need to revise training relating to inpatient care and hospital process design to meet the evolving expectation of hospitalists that their performance will be measured, to actively set quality parameters, and to lead multidisciplinary teams to improve hospital performance.2 Armed with the appropriate skill set, hospitalists would be uniquely situated to lead and manage improvements in processes in the hospitals in which they work.

The content of the first Society of Hospital Medicine (SHM) Quality Improvement Resource Room (QI RR) supports hospitalists leading a multidisciplinary team dedicated to improving inpatient outcomes by preventing hospital‐acquired venous thromboembolism (VTE), a common cause of morbidity and mortality in hospitalized patients.3 The SHM developed this educational resource in the context of numerous reports on the incidence of medical errors in US hospitals and calls for action to improve the quality of health care.'47 Hospital report cards on quality measures are now public record, and hospitals will require uniformity in practice among physicians. Hospitalists are increasingly expected to lead initiatives that will implement national standards in key practices such as VTE prophylaxis2.

The QI RRs of the SHM are a collection of electronic tools accessible through the SHM Web site. They are designed to enhance the readiness of hospitalists and members of the multidisciplinary inpatient team to redesign care at the institutional level. Although all performance improvement is ultimately occurs locally, many QI methods and tools transcend hospital geography and disease topic. Leveraging a Web‐based platform, the SHM QI RRs present hospitalists with a general approach to QI, enriched by customizable workbooks that can be downloaded to best meet user needs. This resource is an innovation in practice‐based learning, quality improvement, and systems‐based practice.

METHODS

Development of the first QI RR followed a series of steps described in Curriculum Development for Medical Education8 (for process and timeline, see Table 1). Inadequate VTE prophylaxis was identified as an ongoing widespread problem of health care underutilization despite randomized clinical trials supporting the efficacy of prophylaxis.9, 10 Mirroring the AHRQ's assessment of underutilization of VTE prophylaxis as the single most important safety priority,6 the first QI RR focused on VTE, with plans to cover additional clinical conditions over time. As experts in the care of inpatients, hospitalists should be able to take custody of predictable complications of serious illness, identify and lower barriers to prevention, critically review prophylaxis options, utilize hospital‐specific data, and devise strategies to bridge the gap between knowledge and practice. Already leaders of multidisciplinary care teams, hospitalists are primed to lead multidisciplinary improvement teams as well.

Process and Timelines
Phase 1 (January 2005April 2005): Executing the educational strategy
One‐hour conference calls
Curricular, clinical, technical, and creative aspects of production
Additional communication between members of working group between calls
Development of questionnaire for SHM membership, board, education, and hospital quality patient safety (HQPS) committees
Content freeze: fourth month of development
Implementation of revisions prior to April 2005 SHM Annual Meeting
Phase 2 (April 2005August 2005): revision based on feedback
Analysis of formative evaluation from Phase 1
Launch of the VTE QI RR August 2005
Secondary phases and venues for implementation
Workshops at hospital medicine educational events
SHM Quality course
Formal recognition of the learning, experience, or proficiency acquired by users
The working editorial team for the first resource room
Dedicated project manager (SHM staff)
Senior adviser for planning and development (SHM staff)
Senior adviser for education (SHM staff)
Content expert
Education editor
Hospital quality editor
Managing editor

Available data on the demographics of hospitalists and feedback from the SHM membership, leadership, and committees indicated that most learners would have minimal previous exposure to QI concepts and only a few years of management experience. Any previous quality improvement initiatives would tend to have been isolated, experimental, or smaller in scale. The resource rooms are designed to facilitate quality improvement learning among hospitalists that is practice‐based and immediately relevant to patient care. Measurable improvement in particular care processes or outcomes should correlate with actual learning.

The educational strategy of the SHM was predicated on ensuring that a quality and patient safety curriculum would retain clinical applicability in the hospital setting. This approach, grounded in adult learning principles and common to medical education, teaches general principles by framing the learning experience as problem centered.11 Several domains were identified as universally important to any quality improvement effort: raising awareness of a local performance gap, applying the best current evidence to practice, tapping the experience of others leading QI efforts, and using measurements derived from rapid‐cycle tests of change. Such a template delineates the components of successful QI planning, implementation, and evaluation and provides users with a familiar RR format applicable to improving any care process, not just VTE.

The Internet was chosen as the mechanism for delivering training on the basis of previous surveys of the SHM membership in which members expressed a preference for electronic and Web‐based forms of educational content delivery. Drawing from the example of other organizations teaching quality improvement, including the Institute for Healthcare Improvement and Intermountain Health Care, the SHM valued the ubiquity of a Web‐based educational resource. To facilitate on‐the‐job training, the first SHM QI RR provides a comprehensive tool kit to guide hospitalists through the process of advocating, developing, implementing, and evaluating a QI initiative for VTE.

Prior to launching the resource room, formative input was collected from SHM leaders, a panel of education and QI experts, and attendees of the society's annual meetings. Such input followed each significant step in the development of the RR curricula. For example, visitors at a kiosk at the 2005 SHM annual meeting completed surveys as they navigated through the VTE QI RR. This focused feedback shaped prelaunch development. The ultimate performance evaluation and feedback for the QI RR curricula will be gauged by user reports of measurable improvement in specific hospital process or outcomes measures. The VTE QI RR was launched in August 2005 and promoted at the SHM Web site.

RESULTS

The content and layout of the VTE QI RR are depicted in Figure 1. The self‐directed learner may navigate through the entire resource room or just select areas for study. Those likely to visit only a single area are individuals looking for guidance to support discrete roles on the improvement team: champion, clinical leader, facilitator of the QI process, or educator of staff or patient audiences (see Figure 2).

Figure 1
QI Resource Room Landing Page.
Figure 2
Suggested uses of content areas in the VTE QI Resource Room.

Why Should You Act?

The visual center of the QI RR layout presents sobering statisticsalthough pulmonary embolism from deep vein thrombosis is the most common cause of preventable hospital death, most hospitalized medical patients at risk do not receive appropriate prophylaxisand then encourages hospitalist‐led action to reduce hospital‐acquired VTE. The role of the hospitalist is extracted from the competencies articulated in the Venous Thromboembolism, Quality Improvement, and Hospitalist as Teacher chapters of The Core Competencies in Hospital Medicine.2

Awareness

In the Awareness area of the VTE QI RR, materials to raise clinician, hospital staff, and patient awareness are suggested and made available. Through the SHM's lead sponsorship of the national DVT Awareness Month campaign, suggested Steps to Action depict exactly how a hospital medicine service can use the campaign's materials to raise institutional support for tackling this preventable problem.

Evidence

The Evidence section aggregates a list of the most pertinent VTE prophylaxis literature to help ground any QI effort firmly in the evidence base. Through an agreement with the American College of Physicians (ACP), VTE prophylaxis articles reviewed in the ACP Journal Club are presented here.12 Although the listed literature focuses on prophylaxis, plans are in place to include references on diagnosis and treatment.

Experience

Resource room visitors interested in tapping into the experience of hospitalists and other leaders of QI efforts can navigate directly to this area. Interactive resources here include downloadable and adaptable protocols for VTE prophylaxis and, most importantly, improvement stories profiling actual QI successes. The Experience section features comments from an author of a seminal trial that studied computer alerts for high‐risk patients not receiving prophylaxis.10 The educational goal of this section of the QI RR is to provide opportunities to learn from successful QI projects, from the composition of the improvement team to the relevant metrics, implementation plan, and next steps.

Ask the Expert

The most interactive part of the resource room, the Ask the Expert forum, provides a hybrid of experience and evidence. A visitor who posts a clinical or improvement question to this discussion community receives a multidisciplinary response. For each question posted, a hospitalist moderator collects and aggregates responses from a panel of VTE experts, QI experts, hospitalist teachers, and pharmacists. The online exchange permitted by this forum promotes wider debate and learning. The questions and responses are archived and thus are available for subsequent users to read.

Improve

This area features the focal point of the entire resource room, the VTE QI workbook, which was written and designed to provide action‐oriented learning in quality improvement. The workbook is a downloadable project outline to guide and document efforts aimed at reducing rates of hospital‐acquired VTE. Hospitalists who complete the workbook should have acquired familiarity with and a working proficiency in leading system‐level efforts to drive better patient care. Users new to the theory and practice of QI can also review key concepts from a slide presentation in this part of the resource room.

Educate

This content area profiles the hospital medicine core competencies that relate to VTE and QI while also offering teaching materials and advice for teachers of VTE or QI. Teaching resources for clinician educators include online CME and an up‐to‐date slide lecture about VTE prophylaxis. The lecture presentation can be downloaded and customized to serve the needs of the speaker and the audience, whether students, residents, or other hospital staff. Clinician educators can also share or review teaching pearls used by hospitalist colleagues who serve as ward attendings.

DISCUSSION

A case example, shown in Figure 3, demonstrates how content accessible through the SHM VTE QI RR may be used to catalyze a local quality improvement effort.

Figure 3
Case example: the need for quality improvement.

Hospitals will be measured on rates of VTE prophylaxis on medical and surgical services. Failure to standardize prophylaxis among different physician groups may adversely affect overall performance, with implications for both patient care and accreditation. The lack of a agreed‐on gold standard of what constitutes appropriate prophylaxis for a given patient does not absolve an institution of the duty to implement its own standards. The challenge of achieving local consensus on appropriate prophylaxis should not outweigh the urgency to address preventable in‐hospital deaths. In caring for increasing numbers of general medical and surgical patients, hospitalists are likely to be asked to develop and implement a protocol for VTE prophylaxis that can be used hospitalwide. In many instances hospitalists will accept this charge in the aftermath of previous hospital failures in which admission order sets or VTE assessment protocols were launched but never widely implemented. As the National Quality Forum or JCAHO regulations for uniformity among hospitals shift VTE prophylaxis from being voluntary to compulsory, hospitalists will need to develop improvement strategies that have greater reliability.

Hospitalists with no formal training in either vascular medicine or quality improvement may not be able to immediately cite the most current data about VTE prophylaxis rates and regimens and may not have the time to enroll in a training course on quality improvement. How would hospitalists determine baseline rates of appropriate VTE prophylaxis? How can medical education be used to build consensus and recruit support from other physicians? What should be the scope of the QI initiative, and what patient population should be targeted for intervention?

The goal of the SHM QI RR is to provide the tools and the framework to help hospitalists develop, implement, and manage a VTE prophylaxis quality improvement initiative. Suggested Steps to Action in the Awareness section depict exactly how a hospital medicine service can use the campaign's materials to raise institutional support for tackling this preventable problem. Hospital quality officers can direct the hospital's public relations department to the Awareness section for DVT Awareness Month materials, including public service announcements in audio, visual, and print formats. The hold music at the hospital can be temporarily replaced, television kiosks can be set up to run video loops, and banners can be printed and hung in central locations, all to get out the message simultaneously to patients and medical staff.

The Evidence section of the VTE QI RR references a key benchmark study, the DVT‐Free Prospective Registry.9 This study reported that at 183 sites in North America and Europe, more than twice as many medical patients as surgical patients failed to receive prophylaxis. The Evidence section includes the 7th American College of Chest Physicians Consensus Conference on Antithrombotic and Thrombolytic Therapy and also highlights 3 randomized placebo‐controlled clinical trials (MEDENOX 1999, ARTEMIS 2003, and PREVENT 2004) that have reported significant reduction of risk of VTE (50%‐60%) from pharmacologic prophylaxis in moderate‐risk medical inpatients.1315 Review of the data helps to determine which patient population to study first, which prophylaxis options a hospital could deploy appropriately, and the expected magnitude of the effect. Because the literature has already been narrowed and is kept current, hospitalists can save time in answering a range of questions, from the most commonly agreed‐on factors to stratify risk to which populations require alternative interventions.

The Experience section references the first clinical trial demonstrating improved patient outcomes from a quality improvement initiative aimed at improving utilization of VTE prophylaxis.10 At the large teaching hospital where the electronic alerts were studied, a preexisting wealth of educational information on the hospital Web site, in the form of multiple seminars and lectures on VTE prophylaxis by opinion leaders and international experts, had little impact on practice. For this reason, the investigators implemented a trial of how to change physician behavior by introducing a point‐of‐care intervention, the computer alerts. Clinicians prompted by an electronic alert to consider DVT prophylaxis for at‐risk patients employed nearly double the rate of pharmacologic prophylaxis and reduced the incidence of DVT or pulmonary embolism (PE) by 41%. This study suggests that a change introduced to the clinical workflow can improve evidence‐based VTE prophylaxis and also can reduce the incidence of VTE in acutely ill hospitalized patients.

We believe that if hospitalists use the current evidence and experience assembled in the VTE QI RR, they could develop and lead a systematic approach to improving utilization of VTE prophylaxis. Although there is no gold standard method for integrating VTE risk assessment into clinical workflow, the VTE QI RR presents key lessons both from the literature and real world experiences. The crucial take‐home message is that hospitalists can facilitate implementation of VTE risk assessments if they stress simplicity (ie, the sick, old, surgery benefit), link the risk assessment to a menu of evidence‐based prophylaxis options, and require assessment of VTE risk as part of a regular routine (on admission and at regular intervals). Although many hospitals do not yet have computerized entry of physician orders, the simple 4‐point VTE risk assessment described by Kucher et al might be applied to other hospitals.10 The 4‐point system would identify the patients at highest risk, a reasonable starting point for a QI initiative. Whatever the modelCPOE alerts of very high‐risk patients, CPOE‐forced VTE risk assessments, nursing assessments, or paper‐based order setsregular VTE risk assessment can be incorporated into the daily routine of hospital care.

The QI workbook sequences the steps of a multidisciplinary improvement team and prompts users to set specific goals, collect practical metrics, and conduct plan‐do‐study‐act (PDSA) cycles of learning and action (Figure 4). Hospitalists and other team members can use the information in the workbook to estimate the prevalence of use of the appropriate VTE prophylaxis and the incidence of hospital‐acquired VTE at their medical centers, develop a suitable VTE risk assessment model, and plan interventions. Starting with all patients admitted to one nurse on one unit, then expanding to an entire nursing unit, an improvement team could implement rapid PDSA cycles to iron out the wrinkles of a risk assessment protocol. After demonstrating a measurable benefit for the patients at highest risk, the team would then be expected to capture more patients at risk for VTE by modifying the risk assessment protocol to identify moderate‐risk patients (hospitalized patients with one risk factor), as in the MEDENOX, ARTEMIS, and PREVENT clinical trials. Within the first several months, the QI intervention could be expanded to more nursing units. An improvement report profiling a clinically important increase in the rate of appropriate VTE prophylaxis would advocate for additional local resources and projects.

Figure 4
Table of contents of the VTE QI workbook, by Greg Maynard.

As questions arise in assembling an improvement team, setting useful aims and metrics, choosing interventions, implementing and studying change, or collecting performance data, hospitalists can review answers to questions already posted and post their own questions in the Ask the Expert area. For example, one user asked whether there was a standard risk assessment tool for identifying patients at high risk of VTE. Another asked about the use of unfractionated heparin as a low‐cost alternative to low‐molecular‐weight heparin. Both these questions were answered within 24 hours by the content editor of the VTE QI RR and, for one question, also by 2 pharmacists and an international expert in VTE.

As other hospitalists begin de novo efforts of their own, success stories and strategies posted in the online forums of the VTE QI RR will be an evolving resource for basic know‐how and innovation.

Suggestions from a community of resource room users will be solicited, evaluated, and incorporated into the QI RR in order to improve its educational value and utility. The curricula could also be adapted or refined by others with an interest in systems‐based care or practice‐based learning, such as directors of residency training programs.

CONCLUSIONS

The QI RRs bring QI theory and practice to the hospitalist, when and wherever it is wanted, minimizing time away from patient care. The workbook links theory to practice and can be used to launch, sustain, and document a local VTE‐specific QI initiative. A range of experience is accommodated. Content is provided in a way that enables the user to immediately apply and adapt it to a local contextusers can access and download the subset of tools that best meet their needs. For practicing hospitalists, this QI resource offers an opportunity to bridge the training gap in systems‐based hospital care and should increase the quality and quantity of and support for opportunities to lead successful QI projects.

The Accreditation Council of Graduate Medical Education (ACGME) now requires education in health care systems, a requirement not previously mandated for traditional medical residency programs.17 Because the resource rooms should increase the number of hospitalists competently leading local efforts that achieve measurable gains in hospital outcomes, a wider potential constituency also includes residency program directors, internal medicine residents, physician assistants and nurse‐practitioners, nurses, hospital quality officers, and hospital medicine practice leaders.

Further research is needed to determine the clinical impact of the VTE QI workbook on outcomes for hospitalized patients. The effectiveness of such an educational method should be evaluated, at least in part, by documenting changes in clinically important process and outcome measures, in this case those specific to hospital‐acquired VTE. Investigation also will need to generate an impact assessment to see if the curricula are effective in meeting the strategic educational goals of the Society of Hospital Medicine. Further investigation will examine whether this resource can help residency training programs achieve ACGME goals for practice‐based learning and systems‐based care.

References
  1. Society of Hospital Medicine Available at: http://www.hospitalmedicine.org/AM/Template.cfm?Section=Quality_Improvement_Resource_Rooms1(suppl 1).
  2. Anderson FA,Wheeler HB,Goldberg RJ,Hosmer DW,Forcier A,Patwardham NA.Physician practices in the prevention of venous thromboembolism.Arch Intern Med.1991;151:933938.
  3. Kohn LT,Corrigan JM,Donaldson MS, eds.To Err Is Human.Washington, DC:National Academy Press;2000.
  4. Institute of Medicinehttp://www.iom.edu/CMS/3718.aspx
  5. Shojania KG,Duncan BW,McDonald KM,Wachter RM, eds.Making health care safer: a critical analysis of patient safety practices.Agency for Healthcare Research and Quality, Publication 01‐E058;2001.
  6. Joint Commission on the Accreditation of Health Care Organizations. Public policy initiatives. Available at: http://www.jcaho.org/about+us/public+policy+initiatives/pay_for_performance.htm
  7. Kern DE.Curriculum Development for Medical Education: A Six‐Step Approach.Baltimore, Md:Johns Hopkins University Press;1998.
  8. Goldhaber SZ,Tapson VF;DVT FREE Steering Committee.A prospective registry of 5,451 patients with ultrasound‐confirmed deep vein thrombosis.Am J Cardiol.2004;93:259.
  9. Kucher N,Koo S,Quiroz R, et al.Electronic alerts to prevent venous thromboembolism among hospitalized patients.N Engl J Med.2005;352:969.
  10. Barnes LB,Christensen CR,Hersent AJ.Teaching the Case Method.3rd ed.Cambridge, Mass :Harvard Business School.
  11. American College of Physicians. Available at: http://www.acpjc.org/?hp
  12. Samama MM,Cohen AT,Darmon JY, et al.MEDENOX trial.N Engl J Med.1999;341:793800.
  13. Cohen A,Gallus AS,Lassen MR.Fondaparinux versus placebo for the prevention of VTE in acutely ill medical patients (ARTEMIS).J Thromb Haemost.2003;1(suppl 1):2046.
  14. Leizorovicz A,Cohen AT,Turpie AG,Olsson CG,Vaitkus PT,Goldhaber SZ.PREVENT Medical Thromboprophylaxis Study Group.Circulation.2004;110:874879.
  15. Avorn J,Winkelmayer W.Comparing the costs, risks and benefits of competing strategies for the primary prevention of VTE.Circulation.2004;110:IV25IV32.
  16. Accreditation Council for Graduate Medical Education. Available at: http://www.acgme.org/acWebsite/programDir/pd_index.asp.
Article PDF
Issue
Journal of Hospital Medicine - 1(2)
Publications
Page Number
124-132
Legacy Keywords
curriculum development, quality improvement, web‐based education, hospitalist
Sections
Article PDF
Article PDF

The goal of this article is to explain how the first in a series of online resource rooms provides trainees and hospitalists with quality improvement tools that can be applied locally to improve inpatient care.1 During the emergence and explosive growth of hospital medicine, the SHM recognized the need to revise training relating to inpatient care and hospital process design to meet the evolving expectation of hospitalists that their performance will be measured, to actively set quality parameters, and to lead multidisciplinary teams to improve hospital performance.2 Armed with the appropriate skill set, hospitalists would be uniquely situated to lead and manage improvements in processes in the hospitals in which they work.

The content of the first Society of Hospital Medicine (SHM) Quality Improvement Resource Room (QI RR) supports hospitalists leading a multidisciplinary team dedicated to improving inpatient outcomes by preventing hospital‐acquired venous thromboembolism (VTE), a common cause of morbidity and mortality in hospitalized patients.3 The SHM developed this educational resource in the context of numerous reports on the incidence of medical errors in US hospitals and calls for action to improve the quality of health care.'47 Hospital report cards on quality measures are now public record, and hospitals will require uniformity in practice among physicians. Hospitalists are increasingly expected to lead initiatives that will implement national standards in key practices such as VTE prophylaxis2.

The QI RRs of the SHM are a collection of electronic tools accessible through the SHM Web site. They are designed to enhance the readiness of hospitalists and members of the multidisciplinary inpatient team to redesign care at the institutional level. Although all performance improvement is ultimately occurs locally, many QI methods and tools transcend hospital geography and disease topic. Leveraging a Web‐based platform, the SHM QI RRs present hospitalists with a general approach to QI, enriched by customizable workbooks that can be downloaded to best meet user needs. This resource is an innovation in practice‐based learning, quality improvement, and systems‐based practice.

METHODS

Development of the first QI RR followed a series of steps described in Curriculum Development for Medical Education8 (for process and timeline, see Table 1). Inadequate VTE prophylaxis was identified as an ongoing widespread problem of health care underutilization despite randomized clinical trials supporting the efficacy of prophylaxis.9, 10 Mirroring the AHRQ's assessment of underutilization of VTE prophylaxis as the single most important safety priority,6 the first QI RR focused on VTE, with plans to cover additional clinical conditions over time. As experts in the care of inpatients, hospitalists should be able to take custody of predictable complications of serious illness, identify and lower barriers to prevention, critically review prophylaxis options, utilize hospital‐specific data, and devise strategies to bridge the gap between knowledge and practice. Already leaders of multidisciplinary care teams, hospitalists are primed to lead multidisciplinary improvement teams as well.

Process and Timelines
Phase 1 (January 2005April 2005): Executing the educational strategy
One‐hour conference calls
Curricular, clinical, technical, and creative aspects of production
Additional communication between members of working group between calls
Development of questionnaire for SHM membership, board, education, and hospital quality patient safety (HQPS) committees
Content freeze: fourth month of development
Implementation of revisions prior to April 2005 SHM Annual Meeting
Phase 2 (April 2005August 2005): revision based on feedback
Analysis of formative evaluation from Phase 1
Launch of the VTE QI RR August 2005
Secondary phases and venues for implementation
Workshops at hospital medicine educational events
SHM Quality course
Formal recognition of the learning, experience, or proficiency acquired by users
The working editorial team for the first resource room
Dedicated project manager (SHM staff)
Senior adviser for planning and development (SHM staff)
Senior adviser for education (SHM staff)
Content expert
Education editor
Hospital quality editor
Managing editor

Available data on the demographics of hospitalists and feedback from the SHM membership, leadership, and committees indicated that most learners would have minimal previous exposure to QI concepts and only a few years of management experience. Any previous quality improvement initiatives would tend to have been isolated, experimental, or smaller in scale. The resource rooms are designed to facilitate quality improvement learning among hospitalists that is practice‐based and immediately relevant to patient care. Measurable improvement in particular care processes or outcomes should correlate with actual learning.

The educational strategy of the SHM was predicated on ensuring that a quality and patient safety curriculum would retain clinical applicability in the hospital setting. This approach, grounded in adult learning principles and common to medical education, teaches general principles by framing the learning experience as problem centered.11 Several domains were identified as universally important to any quality improvement effort: raising awareness of a local performance gap, applying the best current evidence to practice, tapping the experience of others leading QI efforts, and using measurements derived from rapid‐cycle tests of change. Such a template delineates the components of successful QI planning, implementation, and evaluation and provides users with a familiar RR format applicable to improving any care process, not just VTE.

The Internet was chosen as the mechanism for delivering training on the basis of previous surveys of the SHM membership in which members expressed a preference for electronic and Web‐based forms of educational content delivery. Drawing from the example of other organizations teaching quality improvement, including the Institute for Healthcare Improvement and Intermountain Health Care, the SHM valued the ubiquity of a Web‐based educational resource. To facilitate on‐the‐job training, the first SHM QI RR provides a comprehensive tool kit to guide hospitalists through the process of advocating, developing, implementing, and evaluating a QI initiative for VTE.

Prior to launching the resource room, formative input was collected from SHM leaders, a panel of education and QI experts, and attendees of the society's annual meetings. Such input followed each significant step in the development of the RR curricula. For example, visitors at a kiosk at the 2005 SHM annual meeting completed surveys as they navigated through the VTE QI RR. This focused feedback shaped prelaunch development. The ultimate performance evaluation and feedback for the QI RR curricula will be gauged by user reports of measurable improvement in specific hospital process or outcomes measures. The VTE QI RR was launched in August 2005 and promoted at the SHM Web site.

RESULTS

The content and layout of the VTE QI RR are depicted in Figure 1. The self‐directed learner may navigate through the entire resource room or just select areas for study. Those likely to visit only a single area are individuals looking for guidance to support discrete roles on the improvement team: champion, clinical leader, facilitator of the QI process, or educator of staff or patient audiences (see Figure 2).

Figure 1
QI Resource Room Landing Page.
Figure 2
Suggested uses of content areas in the VTE QI Resource Room.

Why Should You Act?

The visual center of the QI RR layout presents sobering statisticsalthough pulmonary embolism from deep vein thrombosis is the most common cause of preventable hospital death, most hospitalized medical patients at risk do not receive appropriate prophylaxisand then encourages hospitalist‐led action to reduce hospital‐acquired VTE. The role of the hospitalist is extracted from the competencies articulated in the Venous Thromboembolism, Quality Improvement, and Hospitalist as Teacher chapters of The Core Competencies in Hospital Medicine.2

Awareness

In the Awareness area of the VTE QI RR, materials to raise clinician, hospital staff, and patient awareness are suggested and made available. Through the SHM's lead sponsorship of the national DVT Awareness Month campaign, suggested Steps to Action depict exactly how a hospital medicine service can use the campaign's materials to raise institutional support for tackling this preventable problem.

Evidence

The Evidence section aggregates a list of the most pertinent VTE prophylaxis literature to help ground any QI effort firmly in the evidence base. Through an agreement with the American College of Physicians (ACP), VTE prophylaxis articles reviewed in the ACP Journal Club are presented here.12 Although the listed literature focuses on prophylaxis, plans are in place to include references on diagnosis and treatment.

Experience

Resource room visitors interested in tapping into the experience of hospitalists and other leaders of QI efforts can navigate directly to this area. Interactive resources here include downloadable and adaptable protocols for VTE prophylaxis and, most importantly, improvement stories profiling actual QI successes. The Experience section features comments from an author of a seminal trial that studied computer alerts for high‐risk patients not receiving prophylaxis.10 The educational goal of this section of the QI RR is to provide opportunities to learn from successful QI projects, from the composition of the improvement team to the relevant metrics, implementation plan, and next steps.

Ask the Expert

The most interactive part of the resource room, the Ask the Expert forum, provides a hybrid of experience and evidence. A visitor who posts a clinical or improvement question to this discussion community receives a multidisciplinary response. For each question posted, a hospitalist moderator collects and aggregates responses from a panel of VTE experts, QI experts, hospitalist teachers, and pharmacists. The online exchange permitted by this forum promotes wider debate and learning. The questions and responses are archived and thus are available for subsequent users to read.

Improve

This area features the focal point of the entire resource room, the VTE QI workbook, which was written and designed to provide action‐oriented learning in quality improvement. The workbook is a downloadable project outline to guide and document efforts aimed at reducing rates of hospital‐acquired VTE. Hospitalists who complete the workbook should have acquired familiarity with and a working proficiency in leading system‐level efforts to drive better patient care. Users new to the theory and practice of QI can also review key concepts from a slide presentation in this part of the resource room.

Educate

This content area profiles the hospital medicine core competencies that relate to VTE and QI while also offering teaching materials and advice for teachers of VTE or QI. Teaching resources for clinician educators include online CME and an up‐to‐date slide lecture about VTE prophylaxis. The lecture presentation can be downloaded and customized to serve the needs of the speaker and the audience, whether students, residents, or other hospital staff. Clinician educators can also share or review teaching pearls used by hospitalist colleagues who serve as ward attendings.

DISCUSSION

A case example, shown in Figure 3, demonstrates how content accessible through the SHM VTE QI RR may be used to catalyze a local quality improvement effort.

Figure 3
Case example: the need for quality improvement.

Hospitals will be measured on rates of VTE prophylaxis on medical and surgical services. Failure to standardize prophylaxis among different physician groups may adversely affect overall performance, with implications for both patient care and accreditation. The lack of a agreed‐on gold standard of what constitutes appropriate prophylaxis for a given patient does not absolve an institution of the duty to implement its own standards. The challenge of achieving local consensus on appropriate prophylaxis should not outweigh the urgency to address preventable in‐hospital deaths. In caring for increasing numbers of general medical and surgical patients, hospitalists are likely to be asked to develop and implement a protocol for VTE prophylaxis that can be used hospitalwide. In many instances hospitalists will accept this charge in the aftermath of previous hospital failures in which admission order sets or VTE assessment protocols were launched but never widely implemented. As the National Quality Forum or JCAHO regulations for uniformity among hospitals shift VTE prophylaxis from being voluntary to compulsory, hospitalists will need to develop improvement strategies that have greater reliability.

Hospitalists with no formal training in either vascular medicine or quality improvement may not be able to immediately cite the most current data about VTE prophylaxis rates and regimens and may not have the time to enroll in a training course on quality improvement. How would hospitalists determine baseline rates of appropriate VTE prophylaxis? How can medical education be used to build consensus and recruit support from other physicians? What should be the scope of the QI initiative, and what patient population should be targeted for intervention?

The goal of the SHM QI RR is to provide the tools and the framework to help hospitalists develop, implement, and manage a VTE prophylaxis quality improvement initiative. Suggested Steps to Action in the Awareness section depict exactly how a hospital medicine service can use the campaign's materials to raise institutional support for tackling this preventable problem. Hospital quality officers can direct the hospital's public relations department to the Awareness section for DVT Awareness Month materials, including public service announcements in audio, visual, and print formats. The hold music at the hospital can be temporarily replaced, television kiosks can be set up to run video loops, and banners can be printed and hung in central locations, all to get out the message simultaneously to patients and medical staff.

The Evidence section of the VTE QI RR references a key benchmark study, the DVT‐Free Prospective Registry.9 This study reported that at 183 sites in North America and Europe, more than twice as many medical patients as surgical patients failed to receive prophylaxis. The Evidence section includes the 7th American College of Chest Physicians Consensus Conference on Antithrombotic and Thrombolytic Therapy and also highlights 3 randomized placebo‐controlled clinical trials (MEDENOX 1999, ARTEMIS 2003, and PREVENT 2004) that have reported significant reduction of risk of VTE (50%‐60%) from pharmacologic prophylaxis in moderate‐risk medical inpatients.1315 Review of the data helps to determine which patient population to study first, which prophylaxis options a hospital could deploy appropriately, and the expected magnitude of the effect. Because the literature has already been narrowed and is kept current, hospitalists can save time in answering a range of questions, from the most commonly agreed‐on factors to stratify risk to which populations require alternative interventions.

The Experience section references the first clinical trial demonstrating improved patient outcomes from a quality improvement initiative aimed at improving utilization of VTE prophylaxis.10 At the large teaching hospital where the electronic alerts were studied, a preexisting wealth of educational information on the hospital Web site, in the form of multiple seminars and lectures on VTE prophylaxis by opinion leaders and international experts, had little impact on practice. For this reason, the investigators implemented a trial of how to change physician behavior by introducing a point‐of‐care intervention, the computer alerts. Clinicians prompted by an electronic alert to consider DVT prophylaxis for at‐risk patients employed nearly double the rate of pharmacologic prophylaxis and reduced the incidence of DVT or pulmonary embolism (PE) by 41%. This study suggests that a change introduced to the clinical workflow can improve evidence‐based VTE prophylaxis and also can reduce the incidence of VTE in acutely ill hospitalized patients.

We believe that if hospitalists use the current evidence and experience assembled in the VTE QI RR, they could develop and lead a systematic approach to improving utilization of VTE prophylaxis. Although there is no gold standard method for integrating VTE risk assessment into clinical workflow, the VTE QI RR presents key lessons both from the literature and real world experiences. The crucial take‐home message is that hospitalists can facilitate implementation of VTE risk assessments if they stress simplicity (ie, the sick, old, surgery benefit), link the risk assessment to a menu of evidence‐based prophylaxis options, and require assessment of VTE risk as part of a regular routine (on admission and at regular intervals). Although many hospitals do not yet have computerized entry of physician orders, the simple 4‐point VTE risk assessment described by Kucher et al might be applied to other hospitals.10 The 4‐point system would identify the patients at highest risk, a reasonable starting point for a QI initiative. Whatever the modelCPOE alerts of very high‐risk patients, CPOE‐forced VTE risk assessments, nursing assessments, or paper‐based order setsregular VTE risk assessment can be incorporated into the daily routine of hospital care.

The QI workbook sequences the steps of a multidisciplinary improvement team and prompts users to set specific goals, collect practical metrics, and conduct plan‐do‐study‐act (PDSA) cycles of learning and action (Figure 4). Hospitalists and other team members can use the information in the workbook to estimate the prevalence of use of the appropriate VTE prophylaxis and the incidence of hospital‐acquired VTE at their medical centers, develop a suitable VTE risk assessment model, and plan interventions. Starting with all patients admitted to one nurse on one unit, then expanding to an entire nursing unit, an improvement team could implement rapid PDSA cycles to iron out the wrinkles of a risk assessment protocol. After demonstrating a measurable benefit for the patients at highest risk, the team would then be expected to capture more patients at risk for VTE by modifying the risk assessment protocol to identify moderate‐risk patients (hospitalized patients with one risk factor), as in the MEDENOX, ARTEMIS, and PREVENT clinical trials. Within the first several months, the QI intervention could be expanded to more nursing units. An improvement report profiling a clinically important increase in the rate of appropriate VTE prophylaxis would advocate for additional local resources and projects.

Figure 4
Table of contents of the VTE QI workbook, by Greg Maynard.

As questions arise in assembling an improvement team, setting useful aims and metrics, choosing interventions, implementing and studying change, or collecting performance data, hospitalists can review answers to questions already posted and post their own questions in the Ask the Expert area. For example, one user asked whether there was a standard risk assessment tool for identifying patients at high risk of VTE. Another asked about the use of unfractionated heparin as a low‐cost alternative to low‐molecular‐weight heparin. Both these questions were answered within 24 hours by the content editor of the VTE QI RR and, for one question, also by 2 pharmacists and an international expert in VTE.

As other hospitalists begin de novo efforts of their own, success stories and strategies posted in the online forums of the VTE QI RR will be an evolving resource for basic know‐how and innovation.

Suggestions from a community of resource room users will be solicited, evaluated, and incorporated into the QI RR in order to improve its educational value and utility. The curricula could also be adapted or refined by others with an interest in systems‐based care or practice‐based learning, such as directors of residency training programs.

CONCLUSIONS

The QI RRs bring QI theory and practice to the hospitalist, when and wherever it is wanted, minimizing time away from patient care. The workbook links theory to practice and can be used to launch, sustain, and document a local VTE‐specific QI initiative. A range of experience is accommodated. Content is provided in a way that enables the user to immediately apply and adapt it to a local contextusers can access and download the subset of tools that best meet their needs. For practicing hospitalists, this QI resource offers an opportunity to bridge the training gap in systems‐based hospital care and should increase the quality and quantity of and support for opportunities to lead successful QI projects.

The Accreditation Council of Graduate Medical Education (ACGME) now requires education in health care systems, a requirement not previously mandated for traditional medical residency programs.17 Because the resource rooms should increase the number of hospitalists competently leading local efforts that achieve measurable gains in hospital outcomes, a wider potential constituency also includes residency program directors, internal medicine residents, physician assistants and nurse‐practitioners, nurses, hospital quality officers, and hospital medicine practice leaders.

Further research is needed to determine the clinical impact of the VTE QI workbook on outcomes for hospitalized patients. The effectiveness of such an educational method should be evaluated, at least in part, by documenting changes in clinically important process and outcome measures, in this case those specific to hospital‐acquired VTE. Investigation also will need to generate an impact assessment to see if the curricula are effective in meeting the strategic educational goals of the Society of Hospital Medicine. Further investigation will examine whether this resource can help residency training programs achieve ACGME goals for practice‐based learning and systems‐based care.

The goal of this article is to explain how the first in a series of online resource rooms provides trainees and hospitalists with quality improvement tools that can be applied locally to improve inpatient care.1 During the emergence and explosive growth of hospital medicine, the SHM recognized the need to revise training relating to inpatient care and hospital process design to meet the evolving expectation of hospitalists that their performance will be measured, to actively set quality parameters, and to lead multidisciplinary teams to improve hospital performance.2 Armed with the appropriate skill set, hospitalists would be uniquely situated to lead and manage improvements in processes in the hospitals in which they work.

The content of the first Society of Hospital Medicine (SHM) Quality Improvement Resource Room (QI RR) supports hospitalists leading a multidisciplinary team dedicated to improving inpatient outcomes by preventing hospital‐acquired venous thromboembolism (VTE), a common cause of morbidity and mortality in hospitalized patients.3 The SHM developed this educational resource in the context of numerous reports on the incidence of medical errors in US hospitals and calls for action to improve the quality of health care.'47 Hospital report cards on quality measures are now public record, and hospitals will require uniformity in practice among physicians. Hospitalists are increasingly expected to lead initiatives that will implement national standards in key practices such as VTE prophylaxis2.

The QI RRs of the SHM are a collection of electronic tools accessible through the SHM Web site. They are designed to enhance the readiness of hospitalists and members of the multidisciplinary inpatient team to redesign care at the institutional level. Although all performance improvement is ultimately occurs locally, many QI methods and tools transcend hospital geography and disease topic. Leveraging a Web‐based platform, the SHM QI RRs present hospitalists with a general approach to QI, enriched by customizable workbooks that can be downloaded to best meet user needs. This resource is an innovation in practice‐based learning, quality improvement, and systems‐based practice.

METHODS

Development of the first QI RR followed a series of steps described in Curriculum Development for Medical Education8 (for process and timeline, see Table 1). Inadequate VTE prophylaxis was identified as an ongoing widespread problem of health care underutilization despite randomized clinical trials supporting the efficacy of prophylaxis.9, 10 Mirroring the AHRQ's assessment of underutilization of VTE prophylaxis as the single most important safety priority,6 the first QI RR focused on VTE, with plans to cover additional clinical conditions over time. As experts in the care of inpatients, hospitalists should be able to take custody of predictable complications of serious illness, identify and lower barriers to prevention, critically review prophylaxis options, utilize hospital‐specific data, and devise strategies to bridge the gap between knowledge and practice. Already leaders of multidisciplinary care teams, hospitalists are primed to lead multidisciplinary improvement teams as well.

Process and Timelines
Phase 1 (January 2005April 2005): Executing the educational strategy
One‐hour conference calls
Curricular, clinical, technical, and creative aspects of production
Additional communication between members of working group between calls
Development of questionnaire for SHM membership, board, education, and hospital quality patient safety (HQPS) committees
Content freeze: fourth month of development
Implementation of revisions prior to April 2005 SHM Annual Meeting
Phase 2 (April 2005August 2005): revision based on feedback
Analysis of formative evaluation from Phase 1
Launch of the VTE QI RR August 2005
Secondary phases and venues for implementation
Workshops at hospital medicine educational events
SHM Quality course
Formal recognition of the learning, experience, or proficiency acquired by users
The working editorial team for the first resource room
Dedicated project manager (SHM staff)
Senior adviser for planning and development (SHM staff)
Senior adviser for education (SHM staff)
Content expert
Education editor
Hospital quality editor
Managing editor

Available data on the demographics of hospitalists and feedback from the SHM membership, leadership, and committees indicated that most learners would have minimal previous exposure to QI concepts and only a few years of management experience. Any previous quality improvement initiatives would tend to have been isolated, experimental, or smaller in scale. The resource rooms are designed to facilitate quality improvement learning among hospitalists that is practice‐based and immediately relevant to patient care. Measurable improvement in particular care processes or outcomes should correlate with actual learning.

The educational strategy of the SHM was predicated on ensuring that a quality and patient safety curriculum would retain clinical applicability in the hospital setting. This approach, grounded in adult learning principles and common to medical education, teaches general principles by framing the learning experience as problem centered.11 Several domains were identified as universally important to any quality improvement effort: raising awareness of a local performance gap, applying the best current evidence to practice, tapping the experience of others leading QI efforts, and using measurements derived from rapid‐cycle tests of change. Such a template delineates the components of successful QI planning, implementation, and evaluation and provides users with a familiar RR format applicable to improving any care process, not just VTE.

The Internet was chosen as the mechanism for delivering training on the basis of previous surveys of the SHM membership in which members expressed a preference for electronic and Web‐based forms of educational content delivery. Drawing from the example of other organizations teaching quality improvement, including the Institute for Healthcare Improvement and Intermountain Health Care, the SHM valued the ubiquity of a Web‐based educational resource. To facilitate on‐the‐job training, the first SHM QI RR provides a comprehensive tool kit to guide hospitalists through the process of advocating, developing, implementing, and evaluating a QI initiative for VTE.

Prior to launching the resource room, formative input was collected from SHM leaders, a panel of education and QI experts, and attendees of the society's annual meetings. Such input followed each significant step in the development of the RR curricula. For example, visitors at a kiosk at the 2005 SHM annual meeting completed surveys as they navigated through the VTE QI RR. This focused feedback shaped prelaunch development. The ultimate performance evaluation and feedback for the QI RR curricula will be gauged by user reports of measurable improvement in specific hospital process or outcomes measures. The VTE QI RR was launched in August 2005 and promoted at the SHM Web site.

RESULTS

The content and layout of the VTE QI RR are depicted in Figure 1. The self‐directed learner may navigate through the entire resource room or just select areas for study. Those likely to visit only a single area are individuals looking for guidance to support discrete roles on the improvement team: champion, clinical leader, facilitator of the QI process, or educator of staff or patient audiences (see Figure 2).

Figure 1
QI Resource Room Landing Page.
Figure 2
Suggested uses of content areas in the VTE QI Resource Room.

Why Should You Act?

The visual center of the QI RR layout presents sobering statisticsalthough pulmonary embolism from deep vein thrombosis is the most common cause of preventable hospital death, most hospitalized medical patients at risk do not receive appropriate prophylaxisand then encourages hospitalist‐led action to reduce hospital‐acquired VTE. The role of the hospitalist is extracted from the competencies articulated in the Venous Thromboembolism, Quality Improvement, and Hospitalist as Teacher chapters of The Core Competencies in Hospital Medicine.2

Awareness

In the Awareness area of the VTE QI RR, materials to raise clinician, hospital staff, and patient awareness are suggested and made available. Through the SHM's lead sponsorship of the national DVT Awareness Month campaign, suggested Steps to Action depict exactly how a hospital medicine service can use the campaign's materials to raise institutional support for tackling this preventable problem.

Evidence

The Evidence section aggregates a list of the most pertinent VTE prophylaxis literature to help ground any QI effort firmly in the evidence base. Through an agreement with the American College of Physicians (ACP), VTE prophylaxis articles reviewed in the ACP Journal Club are presented here.12 Although the listed literature focuses on prophylaxis, plans are in place to include references on diagnosis and treatment.

Experience

Resource room visitors interested in tapping into the experience of hospitalists and other leaders of QI efforts can navigate directly to this area. Interactive resources here include downloadable and adaptable protocols for VTE prophylaxis and, most importantly, improvement stories profiling actual QI successes. The Experience section features comments from an author of a seminal trial that studied computer alerts for high‐risk patients not receiving prophylaxis.10 The educational goal of this section of the QI RR is to provide opportunities to learn from successful QI projects, from the composition of the improvement team to the relevant metrics, implementation plan, and next steps.

Ask the Expert

The most interactive part of the resource room, the Ask the Expert forum, provides a hybrid of experience and evidence. A visitor who posts a clinical or improvement question to this discussion community receives a multidisciplinary response. For each question posted, a hospitalist moderator collects and aggregates responses from a panel of VTE experts, QI experts, hospitalist teachers, and pharmacists. The online exchange permitted by this forum promotes wider debate and learning. The questions and responses are archived and thus are available for subsequent users to read.

Improve

This area features the focal point of the entire resource room, the VTE QI workbook, which was written and designed to provide action‐oriented learning in quality improvement. The workbook is a downloadable project outline to guide and document efforts aimed at reducing rates of hospital‐acquired VTE. Hospitalists who complete the workbook should have acquired familiarity with and a working proficiency in leading system‐level efforts to drive better patient care. Users new to the theory and practice of QI can also review key concepts from a slide presentation in this part of the resource room.

Educate

This content area profiles the hospital medicine core competencies that relate to VTE and QI while also offering teaching materials and advice for teachers of VTE or QI. Teaching resources for clinician educators include online CME and an up‐to‐date slide lecture about VTE prophylaxis. The lecture presentation can be downloaded and customized to serve the needs of the speaker and the audience, whether students, residents, or other hospital staff. Clinician educators can also share or review teaching pearls used by hospitalist colleagues who serve as ward attendings.

DISCUSSION

A case example, shown in Figure 3, demonstrates how content accessible through the SHM VTE QI RR may be used to catalyze a local quality improvement effort.

Figure 3
Case example: the need for quality improvement.

Hospitals will be measured on rates of VTE prophylaxis on medical and surgical services. Failure to standardize prophylaxis among different physician groups may adversely affect overall performance, with implications for both patient care and accreditation. The lack of a agreed‐on gold standard of what constitutes appropriate prophylaxis for a given patient does not absolve an institution of the duty to implement its own standards. The challenge of achieving local consensus on appropriate prophylaxis should not outweigh the urgency to address preventable in‐hospital deaths. In caring for increasing numbers of general medical and surgical patients, hospitalists are likely to be asked to develop and implement a protocol for VTE prophylaxis that can be used hospitalwide. In many instances hospitalists will accept this charge in the aftermath of previous hospital failures in which admission order sets or VTE assessment protocols were launched but never widely implemented. As the National Quality Forum or JCAHO regulations for uniformity among hospitals shift VTE prophylaxis from being voluntary to compulsory, hospitalists will need to develop improvement strategies that have greater reliability.

Hospitalists with no formal training in either vascular medicine or quality improvement may not be able to immediately cite the most current data about VTE prophylaxis rates and regimens and may not have the time to enroll in a training course on quality improvement. How would hospitalists determine baseline rates of appropriate VTE prophylaxis? How can medical education be used to build consensus and recruit support from other physicians? What should be the scope of the QI initiative, and what patient population should be targeted for intervention?

The goal of the SHM QI RR is to provide the tools and the framework to help hospitalists develop, implement, and manage a VTE prophylaxis quality improvement initiative. Suggested Steps to Action in the Awareness section depict exactly how a hospital medicine service can use the campaign's materials to raise institutional support for tackling this preventable problem. Hospital quality officers can direct the hospital's public relations department to the Awareness section for DVT Awareness Month materials, including public service announcements in audio, visual, and print formats. The hold music at the hospital can be temporarily replaced, television kiosks can be set up to run video loops, and banners can be printed and hung in central locations, all to get out the message simultaneously to patients and medical staff.

The Evidence section of the VTE QI RR references a key benchmark study, the DVT‐Free Prospective Registry.9 This study reported that at 183 sites in North America and Europe, more than twice as many medical patients as surgical patients failed to receive prophylaxis. The Evidence section includes the 7th American College of Chest Physicians Consensus Conference on Antithrombotic and Thrombolytic Therapy and also highlights 3 randomized placebo‐controlled clinical trials (MEDENOX 1999, ARTEMIS 2003, and PREVENT 2004) that have reported significant reduction of risk of VTE (50%‐60%) from pharmacologic prophylaxis in moderate‐risk medical inpatients.1315 Review of the data helps to determine which patient population to study first, which prophylaxis options a hospital could deploy appropriately, and the expected magnitude of the effect. Because the literature has already been narrowed and is kept current, hospitalists can save time in answering a range of questions, from the most commonly agreed‐on factors to stratify risk to which populations require alternative interventions.

The Experience section references the first clinical trial demonstrating improved patient outcomes from a quality improvement initiative aimed at improving utilization of VTE prophylaxis.10 At the large teaching hospital where the electronic alerts were studied, a preexisting wealth of educational information on the hospital Web site, in the form of multiple seminars and lectures on VTE prophylaxis by opinion leaders and international experts, had little impact on practice. For this reason, the investigators implemented a trial of how to change physician behavior by introducing a point‐of‐care intervention, the computer alerts. Clinicians prompted by an electronic alert to consider DVT prophylaxis for at‐risk patients employed nearly double the rate of pharmacologic prophylaxis and reduced the incidence of DVT or pulmonary embolism (PE) by 41%. This study suggests that a change introduced to the clinical workflow can improve evidence‐based VTE prophylaxis and also can reduce the incidence of VTE in acutely ill hospitalized patients.

We believe that if hospitalists use the current evidence and experience assembled in the VTE QI RR, they could develop and lead a systematic approach to improving utilization of VTE prophylaxis. Although there is no gold standard method for integrating VTE risk assessment into clinical workflow, the VTE QI RR presents key lessons both from the literature and real world experiences. The crucial take‐home message is that hospitalists can facilitate implementation of VTE risk assessments if they stress simplicity (ie, the sick, old, surgery benefit), link the risk assessment to a menu of evidence‐based prophylaxis options, and require assessment of VTE risk as part of a regular routine (on admission and at regular intervals). Although many hospitals do not yet have computerized entry of physician orders, the simple 4‐point VTE risk assessment described by Kucher et al might be applied to other hospitals.10 The 4‐point system would identify the patients at highest risk, a reasonable starting point for a QI initiative. Whatever the modelCPOE alerts of very high‐risk patients, CPOE‐forced VTE risk assessments, nursing assessments, or paper‐based order setsregular VTE risk assessment can be incorporated into the daily routine of hospital care.

The QI workbook sequences the steps of a multidisciplinary improvement team and prompts users to set specific goals, collect practical metrics, and conduct plan‐do‐study‐act (PDSA) cycles of learning and action (Figure 4). Hospitalists and other team members can use the information in the workbook to estimate the prevalence of use of the appropriate VTE prophylaxis and the incidence of hospital‐acquired VTE at their medical centers, develop a suitable VTE risk assessment model, and plan interventions. Starting with all patients admitted to one nurse on one unit, then expanding to an entire nursing unit, an improvement team could implement rapid PDSA cycles to iron out the wrinkles of a risk assessment protocol. After demonstrating a measurable benefit for the patients at highest risk, the team would then be expected to capture more patients at risk for VTE by modifying the risk assessment protocol to identify moderate‐risk patients (hospitalized patients with one risk factor), as in the MEDENOX, ARTEMIS, and PREVENT clinical trials. Within the first several months, the QI intervention could be expanded to more nursing units. An improvement report profiling a clinically important increase in the rate of appropriate VTE prophylaxis would advocate for additional local resources and projects.

Figure 4
Table of contents of the VTE QI workbook, by Greg Maynard.

As questions arise in assembling an improvement team, setting useful aims and metrics, choosing interventions, implementing and studying change, or collecting performance data, hospitalists can review answers to questions already posted and post their own questions in the Ask the Expert area. For example, one user asked whether there was a standard risk assessment tool for identifying patients at high risk of VTE. Another asked about the use of unfractionated heparin as a low‐cost alternative to low‐molecular‐weight heparin. Both these questions were answered within 24 hours by the content editor of the VTE QI RR and, for one question, also by 2 pharmacists and an international expert in VTE.

As other hospitalists begin de novo efforts of their own, success stories and strategies posted in the online forums of the VTE QI RR will be an evolving resource for basic know‐how and innovation.

Suggestions from a community of resource room users will be solicited, evaluated, and incorporated into the QI RR in order to improve its educational value and utility. The curricula could also be adapted or refined by others with an interest in systems‐based care or practice‐based learning, such as directors of residency training programs.

CONCLUSIONS

The QI RRs bring QI theory and practice to the hospitalist, when and wherever it is wanted, minimizing time away from patient care. The workbook links theory to practice and can be used to launch, sustain, and document a local VTE‐specific QI initiative. A range of experience is accommodated. Content is provided in a way that enables the user to immediately apply and adapt it to a local contextusers can access and download the subset of tools that best meet their needs. For practicing hospitalists, this QI resource offers an opportunity to bridge the training gap in systems‐based hospital care and should increase the quality and quantity of and support for opportunities to lead successful QI projects.

The Accreditation Council of Graduate Medical Education (ACGME) now requires education in health care systems, a requirement not previously mandated for traditional medical residency programs.17 Because the resource rooms should increase the number of hospitalists competently leading local efforts that achieve measurable gains in hospital outcomes, a wider potential constituency also includes residency program directors, internal medicine residents, physician assistants and nurse‐practitioners, nurses, hospital quality officers, and hospital medicine practice leaders.

Further research is needed to determine the clinical impact of the VTE QI workbook on outcomes for hospitalized patients. The effectiveness of such an educational method should be evaluated, at least in part, by documenting changes in clinically important process and outcome measures, in this case those specific to hospital‐acquired VTE. Investigation also will need to generate an impact assessment to see if the curricula are effective in meeting the strategic educational goals of the Society of Hospital Medicine. Further investigation will examine whether this resource can help residency training programs achieve ACGME goals for practice‐based learning and systems‐based care.

References
  1. Society of Hospital Medicine Available at: http://www.hospitalmedicine.org/AM/Template.cfm?Section=Quality_Improvement_Resource_Rooms1(suppl 1).
  2. Anderson FA,Wheeler HB,Goldberg RJ,Hosmer DW,Forcier A,Patwardham NA.Physician practices in the prevention of venous thromboembolism.Arch Intern Med.1991;151:933938.
  3. Kohn LT,Corrigan JM,Donaldson MS, eds.To Err Is Human.Washington, DC:National Academy Press;2000.
  4. Institute of Medicinehttp://www.iom.edu/CMS/3718.aspx
  5. Shojania KG,Duncan BW,McDonald KM,Wachter RM, eds.Making health care safer: a critical analysis of patient safety practices.Agency for Healthcare Research and Quality, Publication 01‐E058;2001.
  6. Joint Commission on the Accreditation of Health Care Organizations. Public policy initiatives. Available at: http://www.jcaho.org/about+us/public+policy+initiatives/pay_for_performance.htm
  7. Kern DE.Curriculum Development for Medical Education: A Six‐Step Approach.Baltimore, Md:Johns Hopkins University Press;1998.
  8. Goldhaber SZ,Tapson VF;DVT FREE Steering Committee.A prospective registry of 5,451 patients with ultrasound‐confirmed deep vein thrombosis.Am J Cardiol.2004;93:259.
  9. Kucher N,Koo S,Quiroz R, et al.Electronic alerts to prevent venous thromboembolism among hospitalized patients.N Engl J Med.2005;352:969.
  10. Barnes LB,Christensen CR,Hersent AJ.Teaching the Case Method.3rd ed.Cambridge, Mass :Harvard Business School.
  11. American College of Physicians. Available at: http://www.acpjc.org/?hp
  12. Samama MM,Cohen AT,Darmon JY, et al.MEDENOX trial.N Engl J Med.1999;341:793800.
  13. Cohen A,Gallus AS,Lassen MR.Fondaparinux versus placebo for the prevention of VTE in acutely ill medical patients (ARTEMIS).J Thromb Haemost.2003;1(suppl 1):2046.
  14. Leizorovicz A,Cohen AT,Turpie AG,Olsson CG,Vaitkus PT,Goldhaber SZ.PREVENT Medical Thromboprophylaxis Study Group.Circulation.2004;110:874879.
  15. Avorn J,Winkelmayer W.Comparing the costs, risks and benefits of competing strategies for the primary prevention of VTE.Circulation.2004;110:IV25IV32.
  16. Accreditation Council for Graduate Medical Education. Available at: http://www.acgme.org/acWebsite/programDir/pd_index.asp.
References
  1. Society of Hospital Medicine Available at: http://www.hospitalmedicine.org/AM/Template.cfm?Section=Quality_Improvement_Resource_Rooms1(suppl 1).
  2. Anderson FA,Wheeler HB,Goldberg RJ,Hosmer DW,Forcier A,Patwardham NA.Physician practices in the prevention of venous thromboembolism.Arch Intern Med.1991;151:933938.
  3. Kohn LT,Corrigan JM,Donaldson MS, eds.To Err Is Human.Washington, DC:National Academy Press;2000.
  4. Institute of Medicinehttp://www.iom.edu/CMS/3718.aspx
  5. Shojania KG,Duncan BW,McDonald KM,Wachter RM, eds.Making health care safer: a critical analysis of patient safety practices.Agency for Healthcare Research and Quality, Publication 01‐E058;2001.
  6. Joint Commission on the Accreditation of Health Care Organizations. Public policy initiatives. Available at: http://www.jcaho.org/about+us/public+policy+initiatives/pay_for_performance.htm
  7. Kern DE.Curriculum Development for Medical Education: A Six‐Step Approach.Baltimore, Md:Johns Hopkins University Press;1998.
  8. Goldhaber SZ,Tapson VF;DVT FREE Steering Committee.A prospective registry of 5,451 patients with ultrasound‐confirmed deep vein thrombosis.Am J Cardiol.2004;93:259.
  9. Kucher N,Koo S,Quiroz R, et al.Electronic alerts to prevent venous thromboembolism among hospitalized patients.N Engl J Med.2005;352:969.
  10. Barnes LB,Christensen CR,Hersent AJ.Teaching the Case Method.3rd ed.Cambridge, Mass :Harvard Business School.
  11. American College of Physicians. Available at: http://www.acpjc.org/?hp
  12. Samama MM,Cohen AT,Darmon JY, et al.MEDENOX trial.N Engl J Med.1999;341:793800.
  13. Cohen A,Gallus AS,Lassen MR.Fondaparinux versus placebo for the prevention of VTE in acutely ill medical patients (ARTEMIS).J Thromb Haemost.2003;1(suppl 1):2046.
  14. Leizorovicz A,Cohen AT,Turpie AG,Olsson CG,Vaitkus PT,Goldhaber SZ.PREVENT Medical Thromboprophylaxis Study Group.Circulation.2004;110:874879.
  15. Avorn J,Winkelmayer W.Comparing the costs, risks and benefits of competing strategies for the primary prevention of VTE.Circulation.2004;110:IV25IV32.
  16. Accreditation Council for Graduate Medical Education. Available at: http://www.acgme.org/acWebsite/programDir/pd_index.asp.
Issue
Journal of Hospital Medicine - 1(2)
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Journal of Hospital Medicine - 1(2)
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124-132
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124-132
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Curriculum development: The venous thromboembolism quality improvement resource room
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Curriculum development: The venous thromboembolism quality improvement resource room
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curriculum development, quality improvement, web‐based education, hospitalist
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curriculum development, quality improvement, web‐based education, hospitalist
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