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PICC Placement and Related Complications
Peripherally inserted central venous catheters (PICCs) are used for a variety of indications, including administration of long‐term intravenous (IV) antibiotics, home IV medications, chemotherapy, and parenteral nutrition.[1, 2, 3] Additionally, PICCs have also been recognized as an alternative to large‐bore central venous catheters such as subclavian or internal jugular central venous catheters. PICCs have been associated with fewer bloodstream infections in patients with cancer than tunneled catheters.[4] Compared to central venous catheters, they demonstrate reduced complication rates,[5] decreased cost,[6] and increased safety for longer durations of use.[1, 2, 3, 7, 8, 9]
Despite the numerous benefits of PICCs, Prandoni et al. estimate an all‐cause complication rate of 12% to 17% with the use of PICCs.[10] Associated complications include infection,[11] pain, bleeding, and mechanical dysfunction, all of which contribute to patient discomfort and additional healthcare costs.[12] Bloodstream infections, for example, had previously been thought to occur at a substantially lower rate in PICCs than central venous catheters.[13] However, a recent systematic review suggests the rate of PICC‐associated bloodstream infections in the inpatient setting is actually comparable to that of central venous catheters.[14] Perhaps the most serious PICC‐associated complication is catheter‐related venous thrombosis. A recent systematic review and meta‐analysis found evidence to suggest the rate of catheter‐related venous thrombosis was highest in patients with cancer or critical illness15; additionally, rates of thrombosis associated with PICCs were higher than those associated with subclavian or internal jugular central venous catheters.[15, 16] Fletcher et al. showed an 8.1% incidence of symptomatic PICC‐related upper extremity deep vein thrombosis (DVT) in the neurosurgical intensive care unit, with 15% of patients subsequently developing a pulmonary embolism.[17] A recent prospective, randomized controlled trial by Itkin et al. similarly demonstrated symptomatic DVT rates of approximately 4%.[18] However, in this study, when PICCs were routinely screened for thrombosis (with or without associated symptoms), approximately 72% demonstrated thrombosis,[18] suggesting that many PICC‐associated thromboses may be clinically undetected. This may have far‐reaching clinical significance, as pulmonary embolism complicates upper extremity DVT in 9% of cases and can result in a mortality rate as high as 25%.[10, 19]
Some strategies to reduce the rate of catheter‐related complications include identification of characteristics that put patients at risk. Many potential risk factors have been investigated, including catheter size,[12, 20, 21, 22, 23, 24] choice of vein,[24] location of catheter tip,[25] and history of malignancy or prior DVT.[12] However, to date, no definitive consensus has been reached. Special attention has been paid to the investigation of underlying risk factors and treatment for catheter‐related DVT, given its significant morbidity and mortality. Results have been equivocal, though, and in some instances, complicated by a diagnosis of underlying malignancy.[26, 27, 28]
As PICCs become more widely utilized, assessments of factors that place patients at greater risk of PICC‐related complications are needed.[21] The purpose of this study was to establish the incidence of complications associated with PICCs placed in the inpatient setting and examine risk factors predisposing patients to these complications.
MATERIALS AND METHODS
Study Design
A case control analysis of adult inpatients who underwent PICC placement between January 2009 and January 2010 was conducted at Scott & White Healthcare (now Baylor Scott & White Healthcare) to determine the incidence and risk factors for PICC‐associated complications.
Study Site
The study took place at Scott & White Memorial Hospital in Temple, Texas, a 636‐bed multispecialty teaching hospital and level 1 trauma center. It is part of a healthcare system that includes 12 hospitals and more than 60 regional clinics, all of which share an electronic medical record to enable full integration.
Human Subjects Approval
This study received approval from the institutional review board at Scott & White Healthcare.
PICC Placement Technique
Inpatient PICC placement was performed by the PICC consult service. The consult service was comprised of 3 separate provider teams: (1) internal medicine, including select hospitalists and internal medicine residents; (2) radiology, including interventional radiologists and radiology residents; and (3) nursing, including registered nurses with advanced training in PICC placement. Following placement of a consult, the PICC consult service assessed the patient, obtained consent, and subsequently placed the catheter. Members of the PICC consult service followed a system‐wide protocol wherein target veins were identified by ultrasound prior to attempting catheter placement, and actual placement of the PICC was ultrasound guided. Images obtained during the procedure were permanently documented in the medical record. At the time of this study, no formal protocol existed wherein target veins were mapped for caliber. Operators relied on their professional judgment to determine if vein caliber appeared sufficient to accommodate catheter placement.
All PICCs were placed using industry standard sterile precautions. A universally accepted modified Seldinger technique was used to obtain venous access.[29] A guidewire was then positioned in the desired vessel to facilitate proper venous placement of the catheter. During the course of the study period, catheters used were either single‐ (4 Fr) or double lumen (5 Fr).
Catheters were placed at the bedside by hospitalists or registered nurse teams; the location of the catheter tip at the cavoatrial junction was confirmed by chest radiography. Catheter insertions by radiologists were performed in the interventional radiology suite, and confirmation of location of the catheter tip was obtained with fluoroscopy.
PICC Maintenance
Following placement, nurses managed the PICC site according to nursing policy. Per policy, the site was assessed each shift. Documentation of assessment was recorded in nursing notes. Routine dressing changes were performed every 7 days, and as needed, to maintain a sterile site. Date and time of dressing changes were documented in nursing notes and on the PICC dressing. Catheter hubs and injection ports were disinfected with an antiseptic preparation for 15 seconds and allowed to air dry for 30 seconds prior to accessing the catheter. Catheters were flushed with 10 mL of normal saline before and after use. Any abnormality noted during PICC assessment was relayed to the primary provider. If the catheter did not flush readily or demonstrate appropriate blood return, nursing staff obtained an order for alteplase to be administered in an effort to salvage the line. PICCs were discontinued at the discretion of the healthcare provider.
Participants
Records of all patients 18 years of age and older who underwent PICC placement between January 2009 and January 2010 were reviewed (N=1444) for study inclusion. There were no exclusion criteria.
Data Collection
Patients who experienced complications were identified by electronic medical record review. One‐to‐one matching was performed for age and gender‐matched controls randomly selected from inpatients who underwent PICC placement during the same time period without complications. A total of 170 cases with PICC‐related complications were identified. One hundred seventy exact age‐ and gender‐matched controls, who based upon documentation available in the electronic medical record did not experience complications, were then randomly selected. Prior to data collection, the research team reviewed and discussed the data collection form and agreed upon a standardized protocol for data collection. Data collection was completed by authors J.M. and J.H. on the standardized data collection form. Although a formal analysis of inter‐rater agreement was not performed, J.M. and J.H. discussed any items where questions arose and arrived at a consensus decision regarding completion of the data point.
End points of the chart review were completion of medical therapy for which the PICC was indicated (eg, IV antibiotics or total parenteral nutrition [TPN]) or documentation of a complication that led to 1 of the following: discontinuation of the PICC or adjustment of either catheter placement or medical therapy. All complications were identified via International Classification of Diseases, 9th Revision codes and systematic chart review.
Complications resulting in discontinuation of the PICC, adjustment of catheter placement, or change in medical therapy were identified by review of nursing or physician documentation, and were categorized as follows: mechanical complications (defined as loss of the ability of the catheter to flush or draw properly, inadvertent catheter dislodgement, or retained portion of the catheter following catheter removal), catheter‐associated bloodstream infection (development of a positive blood culture attributable to the central catheter with no other clearly identifiable source of bacteremia present), cellulitis (defined as cellulitis in the extremity where the catheter was placed), bleeding from the site of catheter, fever (for which no other cause could be identified), and catheter‐associated thrombosis (identified by Doppler ultrasonography in patients exhibiting symptoms such as pain, swelling, redness, or warmth in the extremity in which the PICC was placed).[30]
Demographic data were collected, including insurance status, age, ethnicity, and gender. Clinical data included body mass index (BMI), presence of malnutrition (defined by a serum albumin of less than 3 g/dL),[31] previous or active cancer, previous DVT, use of anticoagulants (eg, warfarin, heparin, or low‐molecular‐weight heparin) or antiplatelet agent (eg, aspirin or clopidogrel) at the time of placement, and indication for PICC placement. A patient's history of previous or active cancer and previous DVT were identified by clinical documentation. Indications for PICC placement included: treating infectious processes (ie, infusion of antimicrobials), providing TPN, chemotherapy administration, and IV access. Catheter‐specific data were also collected and included venous access obtained (cephalic, basilic, brachial), catheter size (single lumen [4 Fr] or double lumen [5 Fr]), type of complication, and time to complication. The procedure note accompanying PICC placement was reviewed for data regarding time of day inserted (with after hours defined as documentation of placement occurring after 5 pm), and procedure operator to identify type of team (internal medicine, radiology, nursing) responsible for placement.
Data Analysis
Demographic characteristics and potential risk factors for patients in both the case and control groups of the study were summarized using descriptive statistics: mean ( standard deviation [SD]) for continuous variables and frequency (percent) for categorical variables. Univariate and multivariable conditional logistic regression analyses of variables that were potential risk factors of PICC‐related complications were utilized. A stepwise selection method was used for multivariable conditional logistic regression models. Alpha=0.2 was used for the significance to enter the model, and =0.05 was used for significance level to remain in the model. Attribution of PICC‐related complications was evaluated in terms of odds ratios (OR) and 95% confidence interval (CI). A P value of <0.05 indicated statistical significance. No prospective power analysis was performed. However, for a retrospective power analysis for 1:1 matching with 170 cases and 170 matched controls, assuming 20% of controls were affected and an of 0.05, one would achieve 80% power to detect an odds ratio of 2. SAS 9.2 (SAS Institute Inc., Cary, NC) was used for data analysis.
RESULTS
In 2009, 1444 PICCs were placed, and 170 cases in which patients experienced complications associated with PICC placement were identified, resulting in a complication rate of 11.77% (95% CI: 10.11%‐13.44%). The most common complications experienced by our patient population included catheter‐associated thrombosis (3%, n = 46), mechanical complications (4%, n=67), inadvertent catheter dislodgement (2%, n=36), mechanical dysfunction (2%, n=30), retained portion of the catheter following catheter removal (<1%, n=1), catheter‐associated bloodstream infections (2%, n=24), and cellulitis at the catheter insertion site (1%, n=15). Other documented complications included unexplained fever and bleeding (Table 1).
| Complication | N (%) |
|---|---|
| |
| Thrombosis | 46 (3) |
| Infection | 24 (2) |
| Cellulitis | 15 (1) |
| Mechanical complications* | 67 (4) |
| Unexplained fever | 15 (1) |
| Bleeding | 3 (0) |
| No complication | 1,274 (88) |
The mean age of the total cohort (N=340), comprised of case (N=170) and control (N=170) groups, was 58 years (SD 17), and 55% (n=94) were females. There were no significant differences in complications between groups based on ethnicity (P=0.66). In the case group, 46% (n=78) of PICCs were placed by the radiology team, 41% (n=69) were placed by the internal medicine team, and 14% (n=23) were placed by nursing. In the control group, 44% (n=74) of PICCs were placed by radiology, 36% (n=62) by internal medicine, and 20% (n=34) by nursing. Based on univariate conditional analysis, provider team was not significantly associated with complications (P=0.29).
Predictors of All‐Cause Complications
Based upon univariate conditional logistic regression analyses of complications related to PICC placement (N=340), the following variables demonstrated a statistically significant increased risk for complications: malnutrition (OR: 1.88 [95% CI: 1.023.44], P=0.04) and after‐hours placement (OR: 8.67 [95% CI: 2.62‐28.63], P=0.0004) (Table 2). Anticoagulation was associated with a decreased risk of complications (OR: 0.27 [95% CI: 0.16‐0.45], P=0.04). Based upon multivariable logistic regression analysis, after‐hours placement (OR: 9.52 [95% CI: 2.68‐33.78], P=0.0005) and BMI >30 (OR: 1.98 [95% CI: 1.09‐3.61], P=0.02) were significantly associated with an increased risk of PICC‐associated complications. Conversely, anticoagulation/antiplatelet use was associated with a decreased risk of complications (OR: 0.24 [95% CI: 0.14‐0.43], P<0.0001).
| Variable | Case, N (%) | Control, N (%) | Univariate | Multivariable | ||
|---|---|---|---|---|---|---|
| OR (95% CI) | P Value | AOR (95% CI) | P Value | |||
| ||||||
| Age, y, meanSD | 5817 | 5817 | ||||
| BMI, meanSD | 29.29.5 | 27.97.9 | 1.02 (0.991.05) | 0.12 | ||
| 30 | 108 (64) | 116 (68%) | 1.00 | 1.00 | ||
| >30 | 62 (36) | 54 (32%) | 1.29 (0.792.11) | 0.32 | 1.98 (1.093.61) | 0.02 |
| Length of stay, d, meanSD | 1822 | 1416 | 1.01 (1.001.03) | 0.06 | ||
| Length of stay group, d | 0.11a | |||||
| <7 | 41 (24) | 52 (31) | 1.00 | |||
| 729 | 101 (59) | 103 (61) | 1.19 (0.721.98) | 0.49 | ||
| 30 | 28 (16) | 15 (9) | 2.21 (1.074.58) | 0.03 | ||
| Gender | ||||||
| Female | 94 (55) | 94 (55) | ||||
| Male | 76 (45) | 76 (45) | ||||
| Ethnicity | 0.66a | |||||
| Caucasian | 131 (77) | 125 (74) | 1.00 | |||
| African American | 26 (15) | 28 (16) | 0.88 (0.481.60) | 0.67 | ||
| Hispanic/Asian | 13 (8) | 17 (10) | 0.70 (0.311.58) | 0.38 | ||
| Provider team | 0.29a | |||||
| Radiology | 78 (46) | 74 (44) | 1.00 | |||
| Internal medicine | 69 (41) | 62 (36) | 1.05 (0.681.64) | 0.82 | ||
| Nursing | 23 (14) | 34 (20) | 0.65 (0.351.19) | 0.16 | ||
| Insuranceb | 0.22a | |||||
| Private insurance | 46 (27) | 42 (25) | 1.00 | |||
| Uninsured | 17 (10) | 24 (14) | 0.73 (0.351.55) | 0.41 | ||
| Medicare | 57 (34) | 62 (37) | 0.73 (0.381.40) | 0.34 | ||
| Medicaid | 39 (23) | 25 (15) | 1.51 (0.743.06) | 0.26 | ||
| Tricare/Veterans Administration | 11 (6) | 16 (9) | 0.59 (0.241.45) | 0.25 | ||
| History of DVT | 27 (16) | 26 (15) | 1.05 (0.581.91) | 0.88 | ||
| Malnutritionb | 149 (88) | 134 (79) | 1.88 (1.023.44) | 0.04 | ||
| Cancer | 25 (15) | 36 (21) | 0.58 (0.311.09) | 0.09 | ||
| Fluoroscopy | 129 (76) | 139 (82) | 0.71 (0.421.19) | 0.19 | ||
| Anticoagulation use | 50 (29) | 100 (59) | 0.27 (0.160.45) | <0.0001 | 0.24 (0.140.43) | <0.0001 |
| Multilumenc | 99 (58) | 111 (66) | 0.70 (0.441.11) | 0.13 | ||
| Veinb | 0.39a | |||||
| Basilic | 98 (58) | 86 (51) | 1.00 | |||
| Cephalic | 11 (6) | 8 (5) | 1.37 (0.483.89) | 0.55 | ||
| Brachial | 61 (36) | 74 (44) | 0.70 (0.451.09) | 0.12 | ||
| Internal mammary | 0 (0) | 1 (1) | <0.001 (<0.001>999) | 0.99 | ||
| Time of dayb | ||||||
| Morning/afternoon | 144 (85) | 166 (98) | 1.00 | 1.00 | ||
| After hours | 26 (15) | 3 (2) | 8.67 (2.6228.63) | 0.0004 | 9.52 (2.6833.78) | 0.0005 |
| Indication for PICC | 0.02a | |||||
| Infection | 88 (52) | 71 (42) | 1.00 | |||
| Pneumonia | 21 (12) | 14 (8) | 1.07 (0.502.29) | 0.87 | ||
| Chemotherapy | 5 (3) | 2 (1) | 1.84 (0.349.93) | 0.48 | ||
| IV access | 36 (21) | 66 (39) | 0.44 (0.250.75) | 0.003 | ||
| Total parenteral nutrition | 20 (12) | 17 (10) | 0.96 (0.442.14) | 0.93 | ||
Predictors of Nonmechanical Complications
To study risk factors related to nonmechanical complications, a secondary analysis (N=206) was performed in which all patients who experienced mechanical complications (N=67) and matched controls (N=67) were excluded. Based upon multivariable logistic regression analysis, after‐hours placement (OR: 6.93 [95% CI: 1.35‐35.56], P=0.02) and malnutrition (OR: 2.83 [95% CI: 1.037.81], P=0.04) were significantly associated with increased risk of nonmechanical complications. The use of anticoagulation/antiplatelet agents was associated with decreased risk of nonmechanical complications (OR: 0.17 [95% CI: 0.07‐0.40], P<0.0001). Variables not significantly associated with nonmechanical complications included BMI>30, previous history of DVT, history of cancer, catheter size, and venous access choice (Table 3).
| Variable | Case, N (%) | Control, N (%) | Univariate | Multivariable | ||
|---|---|---|---|---|---|---|
| OR (95% CI) | P Value | AOR (95% CI) | P Value | |||
| ||||||
| Age, y, meanSD | 5816 | 5816 | ||||
| BMI, meanSD | 29.79.8 | 28.57.9 | 1.03 (0.991.07) | 0.22 | ||
| 30 | 64 (62) | 68 (66) | 1.00 | |||
| >30 | 39 (38) | 35 (34) | 1.27 (0.642.49) | 0.49 | ||
| Length of stay, d, meanSD | 2026 | 1418 | 1.02 (1.001.03) | 0.08 | ||
| Length of stay group, d | 0.03a | |||||
| <7 | 22 (21) | 28 (27) | 1.00 | |||
| 729 | 60 (58) | 68 (66) | 0.95 (0.491.82) | 0.87 | ||
| 30 | 21 (20) | 7 (7) | 3.24 (1.238.54) | 0.02 | ||
| Gender | ||||||
| Female | 63 (61) | 63 (61) | ||||
| Male | 40 (39) | 40 (39) | ||||
| Ethnicity | 0.95a | |||||
| Caucasian | 75 (73) | 75 (73) | 1.00 | |||
| African American | 19 (18) | 18 (17) | 1.06 (0.512.21) | 0.87 | ||
| Hispanic/Asian | 9 (9) | 10 (10) | 0.88 (0.322.44) | 0.81 | ||
| Provider team | 0.81a | |||||
| Radiology | 43 (42) | 44 (43) | 1.00 | |||
| Internal medicine | 45 (44) | 41 (40) | 1.11 (0.621.96) | 0.73 | ||
| Nursing | 15 (15) | 18 (17) | 0.86 (0.391.90) | 0.71 | ||
| Insuranceb | 0.22a | |||||
| Private insurance | 29 (28) | 27 (26) | 1.00 | |||
| Uninsured | 13 (13) | 12 (12) | 1.18 (0.433.26) | 0.74 | ||
| Medicare | 32 (31) | 40 (39) | 0.52 (0.211.29) | 0.16 | ||
| Medicaid | 21 (20) | 12 (12) | 1.81 (0.694.74) | 0.23 | ||
| Tricare/Veterans Administration | 8 (8) | 11 (11) | 0.58 (0.191.79) | 0.34 | ||
| History of DVT | 15 (15) | 15 (15) | 1.00 (0.462.16) | 1.00 | ||
| Malnutritionb | 93 (90) | 79 (77) | 2.86 (1.216.76) | 0.02 | 2.83 (1.037.81) | 0.04 |
| Cancer | 17 (17) | 22 (21) | 0.67 (0.301.48) | 0.32 | ||
| Fluoroscopy | 78 (76) | 85 (83) | 0.65 (0.321.31) | 0.23 | ||
| Anticoagulation use | 29 (28) | 60 (58) | 0.21 (0.100.44) | <0.0001 | 0.17 (0.070.40) | <0.0001 |
| Multilumenc | 64 (62) | 67 (66) | 0.83 (0.461.51) | 0.55 | ||
| Veinb | 0.32a | |||||
| Basilic | 54 (52) | 49 (48) | 1.00 | |||
| Cephalic | 8 (8) | 3 (3) | 2.45 (0.649.32) | 0.19 | ||
| Brachial | 41 (40) | 49 (48) | 0.72 (0.421.24) | 0.24 | ||
| Internal mammary | 0 (0) | 1 (1) | <0.001 (<0.001>999) | 0.99 | ||
| Time of dayb | ||||||
| Morning/afternoon | 87 (84) | 100 (98) | 1.00 | 1.00 | ||
| After hours | 16 (16) | 2 (2) | 8.00 (1.8434.79) | 0.006 | 6.93 (1.3535.56) | 0.02 |
| Indication for PICC | 0.13 | |||||
| Infectiond | 52 (50) | 45 (44) | 1.00 | |||
| Pneumonia | 14 (14) | 7 (7) | 1.46 (0.514.18) | 0.48 | ||
| Chemotherapy | 5 (5) | 0 (0) | >999 (<0.001>999) | 0.99 | ||
| IV access | 22 (21) | 43 (42) | 0.48 (0.240.96) | 0.04 | ||
| Total parenteral nutrition | 10 (10) | 8 (8) | 1.08 (0.323.62) | 0.90 | ||
Predictors of Thrombotic Complications
Of 1444 patients who underwent PICC placement, 3% (n=46) were subsequently diagnosed with a catheter‐associated thrombosis, representing 27% of all observed complications. In an attempt to better identify factors predisposing patients to thrombotic complications, an additional subgroup analysis (N=92) was performed on those patients who experienced catheter‐associated thrombosis (N=46) and matched controls (N=46). Variables examined in the analysis included BMI, length of stay (LOS), history of DVT, history of cancer, utilization of anticoagulation/antiplatelet agents, malnutrition, and catheter size.
Based on conditional univariate analyses, the following variables were significantly associated with increased risk of catheter‐associated thrombosis: LOS (as a continuous variable) (OR: 1.04 [95% CI: 1.001.09], P=0.05), malnutrition (OR: 4 [95% CI: 1.1314.18], P=0.03), and after‐hours placement (OR: 8.00 [95% CI: 1.0063.96], P=0.05) (Table 4). Use of anticoagulation/antiplatelet agents (OR: 0.29 [95% CI: 0.11‐0.80], P=0.02) was associated with decreased risk of thrombosis. History of previous DVT and history of cancer were nonsignificant. In the multivariable logistic regression model, malnutrition (OR: 10.16 [95% CI: 1.76‐58.71], P=0.01) remained associated with increased risk of catheter‐associated thrombosis, whereas use of anticoagulation/antiplatelet agents (OR: 0.11 [95% CI: 0.02‐0.51], P=0.005) was associated with decreased risk of catheter‐associated thrombosis (Table 4).
| Variable | Case, N (%) | Control, N (%) | Univariate | Multivariable | ||
|---|---|---|---|---|---|---|
| OR (95% CI) | P Value | AOR (95% CI) | P Value | |||
| ||||||
| Age, y, meanSD | 5818 | 5818 | ||||
| BMI, meanSD | 27.77.1 | 27.77.8 | 1.00 (0.931.08) | 0.98 | ||
| 30 | 34 (74) | 33 (72) | ||||
| >30 | 12 (26) | 13 (28) | 0.83 (0.252.73) | 0.76 | ||
| Length of stay, d, meanSD | 1712 | 119 | 1.04 (1.001.09) | 0.05 | ||
| Length of stay group, d | 0.15 | |||||
| <7 | 8 (17) | 14 (30) | 1.00 | |||
| 729 | 29 (63) | 30 (65) | 1.13 (0.413.07) | 0.82 | ||
| 30 | 9 (20) | 2 (4) | 4.65 (0.9822.13) | 0.05 | ||
| Gender | ||||||
| Female | 26 (57) | 26 (57) | ||||
| Male | 20 (43) | 20 (43) | ||||
| Ethnicity | 0.44a | |||||
| Caucasian | 31 (67) | 36 (78) | 1.00 | |||
| African American | 11 (24) | 6 (13) | 2.02 (0.695.93) | 0.20 | ||
| Hispanic/Asian | 4 (9) | 4 (9) | 1.12 (0.225.68) | 0.89 | ||
| Provider team | 0.26a | |||||
| Radiology | 23 (50) | 19 (41) | 1.00 | |||
| Internal medicine | 20 (43) | 18 (39) | 1.00 (0.432.31) | 1.00 | ||
| Nursing | 3(7) | 9 (20) | 0.33 (0.091.27) | 0.11 | ||
| Insuranceb | 0.38a | |||||
| Private insurance | 13 (28) | 11 (24) | 1.00 | |||
| Uninsured | 8 (17) | 4 (9) | 2.01 (0.3810.58) | 0.41 | ||
| Medicare | 14 (30) | 21 (47) | 0.39 (0.101.47) | 0.16 | ||
| Medicaid | 8 (17) | 7 (16) | 1.23 (0.285.36) | 0.78 | ||
| Tricare/Veterans Administration | 3 (7) | 2 (4) | 1.01 (0.128.27) | 1.00 | ||
| History of DVT | 7 (15) | 8 (17) | 0.88 (0.322.41) | 0.80 | ||
| Malnutritionb | 43 (93) | 33 (73) | 4.00 (1.1314.18) | 0.03 | 10.16 (1.7658.71) | 0.01 |
| Cancer | 10 (22) | 13 (28) | 0.67 (0.241.87) | 0.44 | ||
| Fluoroscopy | 33 (72) | 39 (85) | 0.46 (0.161.31) | 0.14 | ||
| Anticoagulation use | 16 (35) | 28 (61) | 0.29 (0.110.80) | 0.02 | 0.11 (0.020.51) | 0.005 |
| Multilumenc | 22 (48) | 28 (62) | 0.53 (0.231.26) | 0.15 | ||
| Veinb | 0.93a | |||||
| Basilic | 24 (52) | 21 (47) | 1.00 | |||
| Cephalic | 1 (2) | 1 (2) | 0.86 (0.0514.39) | 0.92 | ||
| Brachial | 21 (46) | 22 (49) | 0.75 (0.311.79) | 0.51 | ||
| Internal mammary | 0 (0) | 1 (2) | <0.001 (<0.001>999) | 0.99 | ||
| Time of dayb | ||||||
| Morning/afternoon | 38 (83) | 44 (98) | 1.00 | |||
| After hours | 8 (17) | 1 (2) | 8.00 (1.0063.96) | 0.05 | ||
| Indication for PICC | 0.80a | |||||
| Infectiond | 20 (43) | 17 (37) | 1.00 | |||
| Pneumonia | 5 (11) | 6 (13) | 0.60 (0.142.56) | 0.49 | ||
| Chemotherapy | 3 (7) | 0 (0) | >999 (<0.001>999) | 0.99 | ||
| IV access | 14 (30) | 20 (43) | 0.58 (0.231.44) | 0.24 | ||
| Total parenteral nutrition | 4 (9) | 3 (7) | 1.22 (0.197.70) | 0.83 | ||
DISCUSSION
The goal of this study was to identify factors related to PICC placement that place the general population of patients at risk. The type and rate of complications associated with PICCs in this study were similar to those previously reported in the literature including catheter‐related infection and thrombosis.[10, 32] Two unique risk factors, not well recognized previously,[10, 27, 28, 33] were observed in this study: malnutrition and after‐hours placement. Malnutrition, defined as serum albumin <3 g/dL was associated with an increase in PICC‐related complications (such as catheter‐associated bloodstream infections and cellulitis) and catheter‐related thrombosis. Malnutrition itself has long been associated with a decreased resistance to infection[34]; in addition, low serum albumin may also be a marker of the presence of other severe comorbidities, which may contribute to increased risk of thrombosis. It has been noted in previous studies that critical illness increases risk of thrombosis.[15] Despite an exhaustive search of the literature, we have been unable to find additional studies examining the extent to which malnutrition may impact PICC‐associated complications.
After‐hours placement was also associated with increased nonmechanical complications, as well as catheter‐related thrombosis. In an effort to improve both patient and consulting provider satisfaction and provide more expedient service, PICCs were often placed after hours (between 5 pm and 8 am) by both interventional radiology (n=14) and internal medicine (n=15) teams.
LOS has been associated with PICC placement complications in other studies.[12] In both primary and secondary analyses, hospital stays >30 days were associated with a higher risk of complications than hospitalizations <7 days. In light of the clinical significance of catheter‐related thrombosis, a subgroup analysis of patients with an LOS >30 days was conducted. The conditional univariate regression analysis showed an increased risk with greater LOS, malnutrition, and after‐hours placement. Use of anticoagulant or antiplatelet agents were associated with decreased risk of thrombosis (Table 4). The association between LOS and PICC‐related thrombosis is consistent with findings from Evans et al. involving 1728 patients in a similar center.[12] In these circumstances, increased LOS may be a surrogate marker for increased severity of illness, in that those patients who are more ill require lengthier hospitalizations. In a systematic review and meta‐analysis, Chopra et al. observed that increased severity of illness correlated with higher rates of catheter‐associated thrombosis, which is supportive of these findings.[15]
In the multivariate logistic regression analysis, BMI >30 was associated with a statistically significant increased risk for PICC‐associated complications after adjusting for anticoagulation and time of placement (Table 2). In the secondary analysis, where patients with mechanical complications were removed, BMI >30 was no longer associated with an increased risk for PICC‐associated complications (Table 3). This suggests that patients with a BMI >30 had an increased risk of mechanical complications, but were not necessarily at increased risk of developing other complications, such as catheter‐related thrombosis, infection, or bleeding. This finding is congruent with studies by Evans et al.,[12] who found no association between BMI and catheter‐associated thrombosis. Our association between BMI and complications is unique; to date, there are few additional studies that examine the extent to which BMI impacts the rate and type of complications associated with PICCs. At this time, the mechanism of the association between mechanical complications (such as inadvertent catheter removal or mechanical malfunction) and BMI is uncertain and warrants further investigation.
Use of Anticoagulant Agents
Anticoagulant (ie, any agent used for DVT prophylaxis or therapeutic anticoagulation) or antiplatelet agent use at the time of PICC placement and during the patient's hospitalization was associated with a decreased risk of thrombosis in our analysis. However, it should be noted that no specific anticoagulant agent was studied, and that antiplatelet agents were included in this analysis, unlike that of Evans et al.[12] Although current literature in oncologic populations, as well as the evidence‐based clinical practice guidelines, recommend against routine use of venous thromboprophylaxis in patients with central venous catheters,[33, 35, 36, 37] we believe this deserves further study, particularly in light of conflicting data in this area.[38, 39] Evans et al.[12] noted that although use of anticoagulants initially appeared to be associated with greater incidence of upper extremity venous thrombosis, when previous diagnosis of DVT was removed from the analysis the association was no longer significant.
In our analyses, no associations between catheter size, choice of venous access, history of previous deep venous thrombosis, or history of malignancy and risk for complications were found. Our findings differed from previous studies, where a relationship between increasing catheter bore size and site of access have been associated with increased PICC‐related thrombosis or other complications.[12, 20, 40, 41] There were also no significant differences in risk for complications between provider teams (eg, internal medicine, radiology, nursing) for PICCs placed during the morning or afternoon, which is consistent with findings by Funk et al.[1] Yet, after‐hours placement of PICCs was associated with greater complications than daytime placement. Although the exploration of factors associated with after‐hours placement was beyond the scope of this study, the findings from this study caused the authors, primarily comprised of members of the internal medicine inpatient medicine division, to reexamine the division's protocol on PICC placement. A consensus decision was made to discontinue after‐hours placement of PICCs by internal medicine teams in an effort to promote patient safety until further data could be collected. As a result, internal medicine teams no longer place PICCs after regular working hours at our institution.
Limitations
Limitations include the categorization of antiplatelet and anticoagulant agents together. We did not distinguish between high‐ and low‐dose aspirin, nor did we distinguish between therapeutic dosing of heparin and low‐molecular‐weight heparin versus DVT prophylaxis dosing. Additionally, for patients who were on warfarin or heparin drip, we did not evaluate for therapeutic range of international normalized ratio or partial thromboplastin time, as this was beyond the present scope of this study. In addition, malnutrition defined by albumin alone may have been somewhat narrow, as conditions aside from malnutrition can impact albumin levels. In future evaluations, this relationship may be clarified by including other determinants of clinical malnutrition including BMI <18 or the measurement of prealbumin. For determination of after‐hours placement of PICCs, we relied upon time of procedure dictation, assuming that all dictations immediately followed catheter placement. If there was a lapse in time between catheter placement and dictation, the category may have been recorded in error. Another limitation of after‐hours categorization was that we were unable to determine whether the PICC was placed on a weekend or holiday.
CONCLUSIONS AND FUTURE DIRECTIONS
Our results suggest that more stringent screening of patients undergoing PICC placement may reduce the risk of complications, with special attention to characteristics such as BMI >30, increased LOS, and protein‐calorie malnutrition (albumin <3). Furthermore, placement of PICC lines in emergent or after‐hours settings should be carefully considered and weighed against relative risks of central venous catheter placement. Further examination of the role anticoagulant and antiplatelet agents may have in the prevention of catheter‐related thrombosis should be undertaken. We hope that the identification of these risk factors will decrease the rate of complications and ultimately enhance patient safety and satisfaction.
Acknowledgments
The authors sincerely thank Glen Cryer, Publications Manager, Baylor Scott & White Health, for his assistance with this article.
Disclosures: Nothing to report.
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- , , , et al. Risk of venous thromboembolism associated with peripherally inserted central catheters: a systematic review and meta‐analysis. Lancet. 2013;382:311–325.
- , , , et al. Prevention of VTE in nonsurgical patients: antithrombotic therapy and prevention of thrombosis, 9th ed: American College of Chest Physicians evidence‐based practice guidelines. Chest. 2012;141(2 suppl):e195S–e226S.
- , , . The clinical significance of peripherally inserted central venous catheter‐related deep vein thrombosis. Neurocrit Care. 2011;15:454–460.
- , , , , , . Peripherally inserted central catheter thrombosis‐reverse tapered versus nontapered catheters: a randomized controlled study. J Vasc Interv Radiol. 2014;25:85–91.
- , , , et al. Upper extremity venous thrombosis diagnosed by duplex scanning. Am J Surg. 1990;160:202–206.
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- . Preventing DVT in peripherally inserted central catheters. Chest. 2013;143:589–590.
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- , , , , . Efficacy of multifaceted interventions in reducing complications of peripherally inserted central catheter in adult oncology patients. Support Care Cancer. 2010;18:1293–1298.
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- . The Seldinger method for PICC insertion. J Intraven Nurs. 1989;12:238–243.
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- . Protein‐energy undernutrition. Merk Manual of Diagnosis and Therapy. 18th ed. Available at: http://www.merckmanuals.com/professional/nutritional_disorders/undernutrition/protein‐energy_undernutrition.html. Accessed May 26, 2013.
- , , , , . Risk of venous thromboembolism in hospitalized patients with peripherally inserted central catheters. J Hosp Med. 2009;4:417–422.
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- , , , et al. 2008 SOR guidelines for the prevention and treatment of thrombosis associated with central venous catheters in patients with cancer: report from the working group. Ann Oncol. 2009;20:1459–1471.
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- , , , et al. Prevention of central venous catheter associated thrombosis using minidose warfarin in patients with haematological malignancies. Br J Haematol. 1998;10:483–486.
- , , , et al. Very low doses of warfarin can prevent thrombosis in central venous catheters. A randomized prospective trial. Ann Intern Med. 1990;112:423–428.
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Peripherally inserted central venous catheters (PICCs) are used for a variety of indications, including administration of long‐term intravenous (IV) antibiotics, home IV medications, chemotherapy, and parenteral nutrition.[1, 2, 3] Additionally, PICCs have also been recognized as an alternative to large‐bore central venous catheters such as subclavian or internal jugular central venous catheters. PICCs have been associated with fewer bloodstream infections in patients with cancer than tunneled catheters.[4] Compared to central venous catheters, they demonstrate reduced complication rates,[5] decreased cost,[6] and increased safety for longer durations of use.[1, 2, 3, 7, 8, 9]
Despite the numerous benefits of PICCs, Prandoni et al. estimate an all‐cause complication rate of 12% to 17% with the use of PICCs.[10] Associated complications include infection,[11] pain, bleeding, and mechanical dysfunction, all of which contribute to patient discomfort and additional healthcare costs.[12] Bloodstream infections, for example, had previously been thought to occur at a substantially lower rate in PICCs than central venous catheters.[13] However, a recent systematic review suggests the rate of PICC‐associated bloodstream infections in the inpatient setting is actually comparable to that of central venous catheters.[14] Perhaps the most serious PICC‐associated complication is catheter‐related venous thrombosis. A recent systematic review and meta‐analysis found evidence to suggest the rate of catheter‐related venous thrombosis was highest in patients with cancer or critical illness15; additionally, rates of thrombosis associated with PICCs were higher than those associated with subclavian or internal jugular central venous catheters.[15, 16] Fletcher et al. showed an 8.1% incidence of symptomatic PICC‐related upper extremity deep vein thrombosis (DVT) in the neurosurgical intensive care unit, with 15% of patients subsequently developing a pulmonary embolism.[17] A recent prospective, randomized controlled trial by Itkin et al. similarly demonstrated symptomatic DVT rates of approximately 4%.[18] However, in this study, when PICCs were routinely screened for thrombosis (with or without associated symptoms), approximately 72% demonstrated thrombosis,[18] suggesting that many PICC‐associated thromboses may be clinically undetected. This may have far‐reaching clinical significance, as pulmonary embolism complicates upper extremity DVT in 9% of cases and can result in a mortality rate as high as 25%.[10, 19]
Some strategies to reduce the rate of catheter‐related complications include identification of characteristics that put patients at risk. Many potential risk factors have been investigated, including catheter size,[12, 20, 21, 22, 23, 24] choice of vein,[24] location of catheter tip,[25] and history of malignancy or prior DVT.[12] However, to date, no definitive consensus has been reached. Special attention has been paid to the investigation of underlying risk factors and treatment for catheter‐related DVT, given its significant morbidity and mortality. Results have been equivocal, though, and in some instances, complicated by a diagnosis of underlying malignancy.[26, 27, 28]
As PICCs become more widely utilized, assessments of factors that place patients at greater risk of PICC‐related complications are needed.[21] The purpose of this study was to establish the incidence of complications associated with PICCs placed in the inpatient setting and examine risk factors predisposing patients to these complications.
MATERIALS AND METHODS
Study Design
A case control analysis of adult inpatients who underwent PICC placement between January 2009 and January 2010 was conducted at Scott & White Healthcare (now Baylor Scott & White Healthcare) to determine the incidence and risk factors for PICC‐associated complications.
Study Site
The study took place at Scott & White Memorial Hospital in Temple, Texas, a 636‐bed multispecialty teaching hospital and level 1 trauma center. It is part of a healthcare system that includes 12 hospitals and more than 60 regional clinics, all of which share an electronic medical record to enable full integration.
Human Subjects Approval
This study received approval from the institutional review board at Scott & White Healthcare.
PICC Placement Technique
Inpatient PICC placement was performed by the PICC consult service. The consult service was comprised of 3 separate provider teams: (1) internal medicine, including select hospitalists and internal medicine residents; (2) radiology, including interventional radiologists and radiology residents; and (3) nursing, including registered nurses with advanced training in PICC placement. Following placement of a consult, the PICC consult service assessed the patient, obtained consent, and subsequently placed the catheter. Members of the PICC consult service followed a system‐wide protocol wherein target veins were identified by ultrasound prior to attempting catheter placement, and actual placement of the PICC was ultrasound guided. Images obtained during the procedure were permanently documented in the medical record. At the time of this study, no formal protocol existed wherein target veins were mapped for caliber. Operators relied on their professional judgment to determine if vein caliber appeared sufficient to accommodate catheter placement.
All PICCs were placed using industry standard sterile precautions. A universally accepted modified Seldinger technique was used to obtain venous access.[29] A guidewire was then positioned in the desired vessel to facilitate proper venous placement of the catheter. During the course of the study period, catheters used were either single‐ (4 Fr) or double lumen (5 Fr).
Catheters were placed at the bedside by hospitalists or registered nurse teams; the location of the catheter tip at the cavoatrial junction was confirmed by chest radiography. Catheter insertions by radiologists were performed in the interventional radiology suite, and confirmation of location of the catheter tip was obtained with fluoroscopy.
PICC Maintenance
Following placement, nurses managed the PICC site according to nursing policy. Per policy, the site was assessed each shift. Documentation of assessment was recorded in nursing notes. Routine dressing changes were performed every 7 days, and as needed, to maintain a sterile site. Date and time of dressing changes were documented in nursing notes and on the PICC dressing. Catheter hubs and injection ports were disinfected with an antiseptic preparation for 15 seconds and allowed to air dry for 30 seconds prior to accessing the catheter. Catheters were flushed with 10 mL of normal saline before and after use. Any abnormality noted during PICC assessment was relayed to the primary provider. If the catheter did not flush readily or demonstrate appropriate blood return, nursing staff obtained an order for alteplase to be administered in an effort to salvage the line. PICCs were discontinued at the discretion of the healthcare provider.
Participants
Records of all patients 18 years of age and older who underwent PICC placement between January 2009 and January 2010 were reviewed (N=1444) for study inclusion. There were no exclusion criteria.
Data Collection
Patients who experienced complications were identified by electronic medical record review. One‐to‐one matching was performed for age and gender‐matched controls randomly selected from inpatients who underwent PICC placement during the same time period without complications. A total of 170 cases with PICC‐related complications were identified. One hundred seventy exact age‐ and gender‐matched controls, who based upon documentation available in the electronic medical record did not experience complications, were then randomly selected. Prior to data collection, the research team reviewed and discussed the data collection form and agreed upon a standardized protocol for data collection. Data collection was completed by authors J.M. and J.H. on the standardized data collection form. Although a formal analysis of inter‐rater agreement was not performed, J.M. and J.H. discussed any items where questions arose and arrived at a consensus decision regarding completion of the data point.
End points of the chart review were completion of medical therapy for which the PICC was indicated (eg, IV antibiotics or total parenteral nutrition [TPN]) or documentation of a complication that led to 1 of the following: discontinuation of the PICC or adjustment of either catheter placement or medical therapy. All complications were identified via International Classification of Diseases, 9th Revision codes and systematic chart review.
Complications resulting in discontinuation of the PICC, adjustment of catheter placement, or change in medical therapy were identified by review of nursing or physician documentation, and were categorized as follows: mechanical complications (defined as loss of the ability of the catheter to flush or draw properly, inadvertent catheter dislodgement, or retained portion of the catheter following catheter removal), catheter‐associated bloodstream infection (development of a positive blood culture attributable to the central catheter with no other clearly identifiable source of bacteremia present), cellulitis (defined as cellulitis in the extremity where the catheter was placed), bleeding from the site of catheter, fever (for which no other cause could be identified), and catheter‐associated thrombosis (identified by Doppler ultrasonography in patients exhibiting symptoms such as pain, swelling, redness, or warmth in the extremity in which the PICC was placed).[30]
Demographic data were collected, including insurance status, age, ethnicity, and gender. Clinical data included body mass index (BMI), presence of malnutrition (defined by a serum albumin of less than 3 g/dL),[31] previous or active cancer, previous DVT, use of anticoagulants (eg, warfarin, heparin, or low‐molecular‐weight heparin) or antiplatelet agent (eg, aspirin or clopidogrel) at the time of placement, and indication for PICC placement. A patient's history of previous or active cancer and previous DVT were identified by clinical documentation. Indications for PICC placement included: treating infectious processes (ie, infusion of antimicrobials), providing TPN, chemotherapy administration, and IV access. Catheter‐specific data were also collected and included venous access obtained (cephalic, basilic, brachial), catheter size (single lumen [4 Fr] or double lumen [5 Fr]), type of complication, and time to complication. The procedure note accompanying PICC placement was reviewed for data regarding time of day inserted (with after hours defined as documentation of placement occurring after 5 pm), and procedure operator to identify type of team (internal medicine, radiology, nursing) responsible for placement.
Data Analysis
Demographic characteristics and potential risk factors for patients in both the case and control groups of the study were summarized using descriptive statistics: mean ( standard deviation [SD]) for continuous variables and frequency (percent) for categorical variables. Univariate and multivariable conditional logistic regression analyses of variables that were potential risk factors of PICC‐related complications were utilized. A stepwise selection method was used for multivariable conditional logistic regression models. Alpha=0.2 was used for the significance to enter the model, and =0.05 was used for significance level to remain in the model. Attribution of PICC‐related complications was evaluated in terms of odds ratios (OR) and 95% confidence interval (CI). A P value of <0.05 indicated statistical significance. No prospective power analysis was performed. However, for a retrospective power analysis for 1:1 matching with 170 cases and 170 matched controls, assuming 20% of controls were affected and an of 0.05, one would achieve 80% power to detect an odds ratio of 2. SAS 9.2 (SAS Institute Inc., Cary, NC) was used for data analysis.
RESULTS
In 2009, 1444 PICCs were placed, and 170 cases in which patients experienced complications associated with PICC placement were identified, resulting in a complication rate of 11.77% (95% CI: 10.11%‐13.44%). The most common complications experienced by our patient population included catheter‐associated thrombosis (3%, n = 46), mechanical complications (4%, n=67), inadvertent catheter dislodgement (2%, n=36), mechanical dysfunction (2%, n=30), retained portion of the catheter following catheter removal (<1%, n=1), catheter‐associated bloodstream infections (2%, n=24), and cellulitis at the catheter insertion site (1%, n=15). Other documented complications included unexplained fever and bleeding (Table 1).
| Complication | N (%) |
|---|---|
| |
| Thrombosis | 46 (3) |
| Infection | 24 (2) |
| Cellulitis | 15 (1) |
| Mechanical complications* | 67 (4) |
| Unexplained fever | 15 (1) |
| Bleeding | 3 (0) |
| No complication | 1,274 (88) |
The mean age of the total cohort (N=340), comprised of case (N=170) and control (N=170) groups, was 58 years (SD 17), and 55% (n=94) were females. There were no significant differences in complications between groups based on ethnicity (P=0.66). In the case group, 46% (n=78) of PICCs were placed by the radiology team, 41% (n=69) were placed by the internal medicine team, and 14% (n=23) were placed by nursing. In the control group, 44% (n=74) of PICCs were placed by radiology, 36% (n=62) by internal medicine, and 20% (n=34) by nursing. Based on univariate conditional analysis, provider team was not significantly associated with complications (P=0.29).
Predictors of All‐Cause Complications
Based upon univariate conditional logistic regression analyses of complications related to PICC placement (N=340), the following variables demonstrated a statistically significant increased risk for complications: malnutrition (OR: 1.88 [95% CI: 1.023.44], P=0.04) and after‐hours placement (OR: 8.67 [95% CI: 2.62‐28.63], P=0.0004) (Table 2). Anticoagulation was associated with a decreased risk of complications (OR: 0.27 [95% CI: 0.16‐0.45], P=0.04). Based upon multivariable logistic regression analysis, after‐hours placement (OR: 9.52 [95% CI: 2.68‐33.78], P=0.0005) and BMI >30 (OR: 1.98 [95% CI: 1.09‐3.61], P=0.02) were significantly associated with an increased risk of PICC‐associated complications. Conversely, anticoagulation/antiplatelet use was associated with a decreased risk of complications (OR: 0.24 [95% CI: 0.14‐0.43], P<0.0001).
| Variable | Case, N (%) | Control, N (%) | Univariate | Multivariable | ||
|---|---|---|---|---|---|---|
| OR (95% CI) | P Value | AOR (95% CI) | P Value | |||
| ||||||
| Age, y, meanSD | 5817 | 5817 | ||||
| BMI, meanSD | 29.29.5 | 27.97.9 | 1.02 (0.991.05) | 0.12 | ||
| 30 | 108 (64) | 116 (68%) | 1.00 | 1.00 | ||
| >30 | 62 (36) | 54 (32%) | 1.29 (0.792.11) | 0.32 | 1.98 (1.093.61) | 0.02 |
| Length of stay, d, meanSD | 1822 | 1416 | 1.01 (1.001.03) | 0.06 | ||
| Length of stay group, d | 0.11a | |||||
| <7 | 41 (24) | 52 (31) | 1.00 | |||
| 729 | 101 (59) | 103 (61) | 1.19 (0.721.98) | 0.49 | ||
| 30 | 28 (16) | 15 (9) | 2.21 (1.074.58) | 0.03 | ||
| Gender | ||||||
| Female | 94 (55) | 94 (55) | ||||
| Male | 76 (45) | 76 (45) | ||||
| Ethnicity | 0.66a | |||||
| Caucasian | 131 (77) | 125 (74) | 1.00 | |||
| African American | 26 (15) | 28 (16) | 0.88 (0.481.60) | 0.67 | ||
| Hispanic/Asian | 13 (8) | 17 (10) | 0.70 (0.311.58) | 0.38 | ||
| Provider team | 0.29a | |||||
| Radiology | 78 (46) | 74 (44) | 1.00 | |||
| Internal medicine | 69 (41) | 62 (36) | 1.05 (0.681.64) | 0.82 | ||
| Nursing | 23 (14) | 34 (20) | 0.65 (0.351.19) | 0.16 | ||
| Insuranceb | 0.22a | |||||
| Private insurance | 46 (27) | 42 (25) | 1.00 | |||
| Uninsured | 17 (10) | 24 (14) | 0.73 (0.351.55) | 0.41 | ||
| Medicare | 57 (34) | 62 (37) | 0.73 (0.381.40) | 0.34 | ||
| Medicaid | 39 (23) | 25 (15) | 1.51 (0.743.06) | 0.26 | ||
| Tricare/Veterans Administration | 11 (6) | 16 (9) | 0.59 (0.241.45) | 0.25 | ||
| History of DVT | 27 (16) | 26 (15) | 1.05 (0.581.91) | 0.88 | ||
| Malnutritionb | 149 (88) | 134 (79) | 1.88 (1.023.44) | 0.04 | ||
| Cancer | 25 (15) | 36 (21) | 0.58 (0.311.09) | 0.09 | ||
| Fluoroscopy | 129 (76) | 139 (82) | 0.71 (0.421.19) | 0.19 | ||
| Anticoagulation use | 50 (29) | 100 (59) | 0.27 (0.160.45) | <0.0001 | 0.24 (0.140.43) | <0.0001 |
| Multilumenc | 99 (58) | 111 (66) | 0.70 (0.441.11) | 0.13 | ||
| Veinb | 0.39a | |||||
| Basilic | 98 (58) | 86 (51) | 1.00 | |||
| Cephalic | 11 (6) | 8 (5) | 1.37 (0.483.89) | 0.55 | ||
| Brachial | 61 (36) | 74 (44) | 0.70 (0.451.09) | 0.12 | ||
| Internal mammary | 0 (0) | 1 (1) | <0.001 (<0.001>999) | 0.99 | ||
| Time of dayb | ||||||
| Morning/afternoon | 144 (85) | 166 (98) | 1.00 | 1.00 | ||
| After hours | 26 (15) | 3 (2) | 8.67 (2.6228.63) | 0.0004 | 9.52 (2.6833.78) | 0.0005 |
| Indication for PICC | 0.02a | |||||
| Infection | 88 (52) | 71 (42) | 1.00 | |||
| Pneumonia | 21 (12) | 14 (8) | 1.07 (0.502.29) | 0.87 | ||
| Chemotherapy | 5 (3) | 2 (1) | 1.84 (0.349.93) | 0.48 | ||
| IV access | 36 (21) | 66 (39) | 0.44 (0.250.75) | 0.003 | ||
| Total parenteral nutrition | 20 (12) | 17 (10) | 0.96 (0.442.14) | 0.93 | ||
Predictors of Nonmechanical Complications
To study risk factors related to nonmechanical complications, a secondary analysis (N=206) was performed in which all patients who experienced mechanical complications (N=67) and matched controls (N=67) were excluded. Based upon multivariable logistic regression analysis, after‐hours placement (OR: 6.93 [95% CI: 1.35‐35.56], P=0.02) and malnutrition (OR: 2.83 [95% CI: 1.037.81], P=0.04) were significantly associated with increased risk of nonmechanical complications. The use of anticoagulation/antiplatelet agents was associated with decreased risk of nonmechanical complications (OR: 0.17 [95% CI: 0.07‐0.40], P<0.0001). Variables not significantly associated with nonmechanical complications included BMI>30, previous history of DVT, history of cancer, catheter size, and venous access choice (Table 3).
| Variable | Case, N (%) | Control, N (%) | Univariate | Multivariable | ||
|---|---|---|---|---|---|---|
| OR (95% CI) | P Value | AOR (95% CI) | P Value | |||
| ||||||
| Age, y, meanSD | 5816 | 5816 | ||||
| BMI, meanSD | 29.79.8 | 28.57.9 | 1.03 (0.991.07) | 0.22 | ||
| 30 | 64 (62) | 68 (66) | 1.00 | |||
| >30 | 39 (38) | 35 (34) | 1.27 (0.642.49) | 0.49 | ||
| Length of stay, d, meanSD | 2026 | 1418 | 1.02 (1.001.03) | 0.08 | ||
| Length of stay group, d | 0.03a | |||||
| <7 | 22 (21) | 28 (27) | 1.00 | |||
| 729 | 60 (58) | 68 (66) | 0.95 (0.491.82) | 0.87 | ||
| 30 | 21 (20) | 7 (7) | 3.24 (1.238.54) | 0.02 | ||
| Gender | ||||||
| Female | 63 (61) | 63 (61) | ||||
| Male | 40 (39) | 40 (39) | ||||
| Ethnicity | 0.95a | |||||
| Caucasian | 75 (73) | 75 (73) | 1.00 | |||
| African American | 19 (18) | 18 (17) | 1.06 (0.512.21) | 0.87 | ||
| Hispanic/Asian | 9 (9) | 10 (10) | 0.88 (0.322.44) | 0.81 | ||
| Provider team | 0.81a | |||||
| Radiology | 43 (42) | 44 (43) | 1.00 | |||
| Internal medicine | 45 (44) | 41 (40) | 1.11 (0.621.96) | 0.73 | ||
| Nursing | 15 (15) | 18 (17) | 0.86 (0.391.90) | 0.71 | ||
| Insuranceb | 0.22a | |||||
| Private insurance | 29 (28) | 27 (26) | 1.00 | |||
| Uninsured | 13 (13) | 12 (12) | 1.18 (0.433.26) | 0.74 | ||
| Medicare | 32 (31) | 40 (39) | 0.52 (0.211.29) | 0.16 | ||
| Medicaid | 21 (20) | 12 (12) | 1.81 (0.694.74) | 0.23 | ||
| Tricare/Veterans Administration | 8 (8) | 11 (11) | 0.58 (0.191.79) | 0.34 | ||
| History of DVT | 15 (15) | 15 (15) | 1.00 (0.462.16) | 1.00 | ||
| Malnutritionb | 93 (90) | 79 (77) | 2.86 (1.216.76) | 0.02 | 2.83 (1.037.81) | 0.04 |
| Cancer | 17 (17) | 22 (21) | 0.67 (0.301.48) | 0.32 | ||
| Fluoroscopy | 78 (76) | 85 (83) | 0.65 (0.321.31) | 0.23 | ||
| Anticoagulation use | 29 (28) | 60 (58) | 0.21 (0.100.44) | <0.0001 | 0.17 (0.070.40) | <0.0001 |
| Multilumenc | 64 (62) | 67 (66) | 0.83 (0.461.51) | 0.55 | ||
| Veinb | 0.32a | |||||
| Basilic | 54 (52) | 49 (48) | 1.00 | |||
| Cephalic | 8 (8) | 3 (3) | 2.45 (0.649.32) | 0.19 | ||
| Brachial | 41 (40) | 49 (48) | 0.72 (0.421.24) | 0.24 | ||
| Internal mammary | 0 (0) | 1 (1) | <0.001 (<0.001>999) | 0.99 | ||
| Time of dayb | ||||||
| Morning/afternoon | 87 (84) | 100 (98) | 1.00 | 1.00 | ||
| After hours | 16 (16) | 2 (2) | 8.00 (1.8434.79) | 0.006 | 6.93 (1.3535.56) | 0.02 |
| Indication for PICC | 0.13 | |||||
| Infectiond | 52 (50) | 45 (44) | 1.00 | |||
| Pneumonia | 14 (14) | 7 (7) | 1.46 (0.514.18) | 0.48 | ||
| Chemotherapy | 5 (5) | 0 (0) | >999 (<0.001>999) | 0.99 | ||
| IV access | 22 (21) | 43 (42) | 0.48 (0.240.96) | 0.04 | ||
| Total parenteral nutrition | 10 (10) | 8 (8) | 1.08 (0.323.62) | 0.90 | ||
Predictors of Thrombotic Complications
Of 1444 patients who underwent PICC placement, 3% (n=46) were subsequently diagnosed with a catheter‐associated thrombosis, representing 27% of all observed complications. In an attempt to better identify factors predisposing patients to thrombotic complications, an additional subgroup analysis (N=92) was performed on those patients who experienced catheter‐associated thrombosis (N=46) and matched controls (N=46). Variables examined in the analysis included BMI, length of stay (LOS), history of DVT, history of cancer, utilization of anticoagulation/antiplatelet agents, malnutrition, and catheter size.
Based on conditional univariate analyses, the following variables were significantly associated with increased risk of catheter‐associated thrombosis: LOS (as a continuous variable) (OR: 1.04 [95% CI: 1.001.09], P=0.05), malnutrition (OR: 4 [95% CI: 1.1314.18], P=0.03), and after‐hours placement (OR: 8.00 [95% CI: 1.0063.96], P=0.05) (Table 4). Use of anticoagulation/antiplatelet agents (OR: 0.29 [95% CI: 0.11‐0.80], P=0.02) was associated with decreased risk of thrombosis. History of previous DVT and history of cancer were nonsignificant. In the multivariable logistic regression model, malnutrition (OR: 10.16 [95% CI: 1.76‐58.71], P=0.01) remained associated with increased risk of catheter‐associated thrombosis, whereas use of anticoagulation/antiplatelet agents (OR: 0.11 [95% CI: 0.02‐0.51], P=0.005) was associated with decreased risk of catheter‐associated thrombosis (Table 4).
| Variable | Case, N (%) | Control, N (%) | Univariate | Multivariable | ||
|---|---|---|---|---|---|---|
| OR (95% CI) | P Value | AOR (95% CI) | P Value | |||
| ||||||
| Age, y, meanSD | 5818 | 5818 | ||||
| BMI, meanSD | 27.77.1 | 27.77.8 | 1.00 (0.931.08) | 0.98 | ||
| 30 | 34 (74) | 33 (72) | ||||
| >30 | 12 (26) | 13 (28) | 0.83 (0.252.73) | 0.76 | ||
| Length of stay, d, meanSD | 1712 | 119 | 1.04 (1.001.09) | 0.05 | ||
| Length of stay group, d | 0.15 | |||||
| <7 | 8 (17) | 14 (30) | 1.00 | |||
| 729 | 29 (63) | 30 (65) | 1.13 (0.413.07) | 0.82 | ||
| 30 | 9 (20) | 2 (4) | 4.65 (0.9822.13) | 0.05 | ||
| Gender | ||||||
| Female | 26 (57) | 26 (57) | ||||
| Male | 20 (43) | 20 (43) | ||||
| Ethnicity | 0.44a | |||||
| Caucasian | 31 (67) | 36 (78) | 1.00 | |||
| African American | 11 (24) | 6 (13) | 2.02 (0.695.93) | 0.20 | ||
| Hispanic/Asian | 4 (9) | 4 (9) | 1.12 (0.225.68) | 0.89 | ||
| Provider team | 0.26a | |||||
| Radiology | 23 (50) | 19 (41) | 1.00 | |||
| Internal medicine | 20 (43) | 18 (39) | 1.00 (0.432.31) | 1.00 | ||
| Nursing | 3(7) | 9 (20) | 0.33 (0.091.27) | 0.11 | ||
| Insuranceb | 0.38a | |||||
| Private insurance | 13 (28) | 11 (24) | 1.00 | |||
| Uninsured | 8 (17) | 4 (9) | 2.01 (0.3810.58) | 0.41 | ||
| Medicare | 14 (30) | 21 (47) | 0.39 (0.101.47) | 0.16 | ||
| Medicaid | 8 (17) | 7 (16) | 1.23 (0.285.36) | 0.78 | ||
| Tricare/Veterans Administration | 3 (7) | 2 (4) | 1.01 (0.128.27) | 1.00 | ||
| History of DVT | 7 (15) | 8 (17) | 0.88 (0.322.41) | 0.80 | ||
| Malnutritionb | 43 (93) | 33 (73) | 4.00 (1.1314.18) | 0.03 | 10.16 (1.7658.71) | 0.01 |
| Cancer | 10 (22) | 13 (28) | 0.67 (0.241.87) | 0.44 | ||
| Fluoroscopy | 33 (72) | 39 (85) | 0.46 (0.161.31) | 0.14 | ||
| Anticoagulation use | 16 (35) | 28 (61) | 0.29 (0.110.80) | 0.02 | 0.11 (0.020.51) | 0.005 |
| Multilumenc | 22 (48) | 28 (62) | 0.53 (0.231.26) | 0.15 | ||
| Veinb | 0.93a | |||||
| Basilic | 24 (52) | 21 (47) | 1.00 | |||
| Cephalic | 1 (2) | 1 (2) | 0.86 (0.0514.39) | 0.92 | ||
| Brachial | 21 (46) | 22 (49) | 0.75 (0.311.79) | 0.51 | ||
| Internal mammary | 0 (0) | 1 (2) | <0.001 (<0.001>999) | 0.99 | ||
| Time of dayb | ||||||
| Morning/afternoon | 38 (83) | 44 (98) | 1.00 | |||
| After hours | 8 (17) | 1 (2) | 8.00 (1.0063.96) | 0.05 | ||
| Indication for PICC | 0.80a | |||||
| Infectiond | 20 (43) | 17 (37) | 1.00 | |||
| Pneumonia | 5 (11) | 6 (13) | 0.60 (0.142.56) | 0.49 | ||
| Chemotherapy | 3 (7) | 0 (0) | >999 (<0.001>999) | 0.99 | ||
| IV access | 14 (30) | 20 (43) | 0.58 (0.231.44) | 0.24 | ||
| Total parenteral nutrition | 4 (9) | 3 (7) | 1.22 (0.197.70) | 0.83 | ||
DISCUSSION
The goal of this study was to identify factors related to PICC placement that place the general population of patients at risk. The type and rate of complications associated with PICCs in this study were similar to those previously reported in the literature including catheter‐related infection and thrombosis.[10, 32] Two unique risk factors, not well recognized previously,[10, 27, 28, 33] were observed in this study: malnutrition and after‐hours placement. Malnutrition, defined as serum albumin <3 g/dL was associated with an increase in PICC‐related complications (such as catheter‐associated bloodstream infections and cellulitis) and catheter‐related thrombosis. Malnutrition itself has long been associated with a decreased resistance to infection[34]; in addition, low serum albumin may also be a marker of the presence of other severe comorbidities, which may contribute to increased risk of thrombosis. It has been noted in previous studies that critical illness increases risk of thrombosis.[15] Despite an exhaustive search of the literature, we have been unable to find additional studies examining the extent to which malnutrition may impact PICC‐associated complications.
After‐hours placement was also associated with increased nonmechanical complications, as well as catheter‐related thrombosis. In an effort to improve both patient and consulting provider satisfaction and provide more expedient service, PICCs were often placed after hours (between 5 pm and 8 am) by both interventional radiology (n=14) and internal medicine (n=15) teams.
LOS has been associated with PICC placement complications in other studies.[12] In both primary and secondary analyses, hospital stays >30 days were associated with a higher risk of complications than hospitalizations <7 days. In light of the clinical significance of catheter‐related thrombosis, a subgroup analysis of patients with an LOS >30 days was conducted. The conditional univariate regression analysis showed an increased risk with greater LOS, malnutrition, and after‐hours placement. Use of anticoagulant or antiplatelet agents were associated with decreased risk of thrombosis (Table 4). The association between LOS and PICC‐related thrombosis is consistent with findings from Evans et al. involving 1728 patients in a similar center.[12] In these circumstances, increased LOS may be a surrogate marker for increased severity of illness, in that those patients who are more ill require lengthier hospitalizations. In a systematic review and meta‐analysis, Chopra et al. observed that increased severity of illness correlated with higher rates of catheter‐associated thrombosis, which is supportive of these findings.[15]
In the multivariate logistic regression analysis, BMI >30 was associated with a statistically significant increased risk for PICC‐associated complications after adjusting for anticoagulation and time of placement (Table 2). In the secondary analysis, where patients with mechanical complications were removed, BMI >30 was no longer associated with an increased risk for PICC‐associated complications (Table 3). This suggests that patients with a BMI >30 had an increased risk of mechanical complications, but were not necessarily at increased risk of developing other complications, such as catheter‐related thrombosis, infection, or bleeding. This finding is congruent with studies by Evans et al.,[12] who found no association between BMI and catheter‐associated thrombosis. Our association between BMI and complications is unique; to date, there are few additional studies that examine the extent to which BMI impacts the rate and type of complications associated with PICCs. At this time, the mechanism of the association between mechanical complications (such as inadvertent catheter removal or mechanical malfunction) and BMI is uncertain and warrants further investigation.
Use of Anticoagulant Agents
Anticoagulant (ie, any agent used for DVT prophylaxis or therapeutic anticoagulation) or antiplatelet agent use at the time of PICC placement and during the patient's hospitalization was associated with a decreased risk of thrombosis in our analysis. However, it should be noted that no specific anticoagulant agent was studied, and that antiplatelet agents were included in this analysis, unlike that of Evans et al.[12] Although current literature in oncologic populations, as well as the evidence‐based clinical practice guidelines, recommend against routine use of venous thromboprophylaxis in patients with central venous catheters,[33, 35, 36, 37] we believe this deserves further study, particularly in light of conflicting data in this area.[38, 39] Evans et al.[12] noted that although use of anticoagulants initially appeared to be associated with greater incidence of upper extremity venous thrombosis, when previous diagnosis of DVT was removed from the analysis the association was no longer significant.
In our analyses, no associations between catheter size, choice of venous access, history of previous deep venous thrombosis, or history of malignancy and risk for complications were found. Our findings differed from previous studies, where a relationship between increasing catheter bore size and site of access have been associated with increased PICC‐related thrombosis or other complications.[12, 20, 40, 41] There were also no significant differences in risk for complications between provider teams (eg, internal medicine, radiology, nursing) for PICCs placed during the morning or afternoon, which is consistent with findings by Funk et al.[1] Yet, after‐hours placement of PICCs was associated with greater complications than daytime placement. Although the exploration of factors associated with after‐hours placement was beyond the scope of this study, the findings from this study caused the authors, primarily comprised of members of the internal medicine inpatient medicine division, to reexamine the division's protocol on PICC placement. A consensus decision was made to discontinue after‐hours placement of PICCs by internal medicine teams in an effort to promote patient safety until further data could be collected. As a result, internal medicine teams no longer place PICCs after regular working hours at our institution.
Limitations
Limitations include the categorization of antiplatelet and anticoagulant agents together. We did not distinguish between high‐ and low‐dose aspirin, nor did we distinguish between therapeutic dosing of heparin and low‐molecular‐weight heparin versus DVT prophylaxis dosing. Additionally, for patients who were on warfarin or heparin drip, we did not evaluate for therapeutic range of international normalized ratio or partial thromboplastin time, as this was beyond the present scope of this study. In addition, malnutrition defined by albumin alone may have been somewhat narrow, as conditions aside from malnutrition can impact albumin levels. In future evaluations, this relationship may be clarified by including other determinants of clinical malnutrition including BMI <18 or the measurement of prealbumin. For determination of after‐hours placement of PICCs, we relied upon time of procedure dictation, assuming that all dictations immediately followed catheter placement. If there was a lapse in time between catheter placement and dictation, the category may have been recorded in error. Another limitation of after‐hours categorization was that we were unable to determine whether the PICC was placed on a weekend or holiday.
CONCLUSIONS AND FUTURE DIRECTIONS
Our results suggest that more stringent screening of patients undergoing PICC placement may reduce the risk of complications, with special attention to characteristics such as BMI >30, increased LOS, and protein‐calorie malnutrition (albumin <3). Furthermore, placement of PICC lines in emergent or after‐hours settings should be carefully considered and weighed against relative risks of central venous catheter placement. Further examination of the role anticoagulant and antiplatelet agents may have in the prevention of catheter‐related thrombosis should be undertaken. We hope that the identification of these risk factors will decrease the rate of complications and ultimately enhance patient safety and satisfaction.
Acknowledgments
The authors sincerely thank Glen Cryer, Publications Manager, Baylor Scott & White Health, for his assistance with this article.
Disclosures: Nothing to report.
Peripherally inserted central venous catheters (PICCs) are used for a variety of indications, including administration of long‐term intravenous (IV) antibiotics, home IV medications, chemotherapy, and parenteral nutrition.[1, 2, 3] Additionally, PICCs have also been recognized as an alternative to large‐bore central venous catheters such as subclavian or internal jugular central venous catheters. PICCs have been associated with fewer bloodstream infections in patients with cancer than tunneled catheters.[4] Compared to central venous catheters, they demonstrate reduced complication rates,[5] decreased cost,[6] and increased safety for longer durations of use.[1, 2, 3, 7, 8, 9]
Despite the numerous benefits of PICCs, Prandoni et al. estimate an all‐cause complication rate of 12% to 17% with the use of PICCs.[10] Associated complications include infection,[11] pain, bleeding, and mechanical dysfunction, all of which contribute to patient discomfort and additional healthcare costs.[12] Bloodstream infections, for example, had previously been thought to occur at a substantially lower rate in PICCs than central venous catheters.[13] However, a recent systematic review suggests the rate of PICC‐associated bloodstream infections in the inpatient setting is actually comparable to that of central venous catheters.[14] Perhaps the most serious PICC‐associated complication is catheter‐related venous thrombosis. A recent systematic review and meta‐analysis found evidence to suggest the rate of catheter‐related venous thrombosis was highest in patients with cancer or critical illness15; additionally, rates of thrombosis associated with PICCs were higher than those associated with subclavian or internal jugular central venous catheters.[15, 16] Fletcher et al. showed an 8.1% incidence of symptomatic PICC‐related upper extremity deep vein thrombosis (DVT) in the neurosurgical intensive care unit, with 15% of patients subsequently developing a pulmonary embolism.[17] A recent prospective, randomized controlled trial by Itkin et al. similarly demonstrated symptomatic DVT rates of approximately 4%.[18] However, in this study, when PICCs were routinely screened for thrombosis (with or without associated symptoms), approximately 72% demonstrated thrombosis,[18] suggesting that many PICC‐associated thromboses may be clinically undetected. This may have far‐reaching clinical significance, as pulmonary embolism complicates upper extremity DVT in 9% of cases and can result in a mortality rate as high as 25%.[10, 19]
Some strategies to reduce the rate of catheter‐related complications include identification of characteristics that put patients at risk. Many potential risk factors have been investigated, including catheter size,[12, 20, 21, 22, 23, 24] choice of vein,[24] location of catheter tip,[25] and history of malignancy or prior DVT.[12] However, to date, no definitive consensus has been reached. Special attention has been paid to the investigation of underlying risk factors and treatment for catheter‐related DVT, given its significant morbidity and mortality. Results have been equivocal, though, and in some instances, complicated by a diagnosis of underlying malignancy.[26, 27, 28]
As PICCs become more widely utilized, assessments of factors that place patients at greater risk of PICC‐related complications are needed.[21] The purpose of this study was to establish the incidence of complications associated with PICCs placed in the inpatient setting and examine risk factors predisposing patients to these complications.
MATERIALS AND METHODS
Study Design
A case control analysis of adult inpatients who underwent PICC placement between January 2009 and January 2010 was conducted at Scott & White Healthcare (now Baylor Scott & White Healthcare) to determine the incidence and risk factors for PICC‐associated complications.
Study Site
The study took place at Scott & White Memorial Hospital in Temple, Texas, a 636‐bed multispecialty teaching hospital and level 1 trauma center. It is part of a healthcare system that includes 12 hospitals and more than 60 regional clinics, all of which share an electronic medical record to enable full integration.
Human Subjects Approval
This study received approval from the institutional review board at Scott & White Healthcare.
PICC Placement Technique
Inpatient PICC placement was performed by the PICC consult service. The consult service was comprised of 3 separate provider teams: (1) internal medicine, including select hospitalists and internal medicine residents; (2) radiology, including interventional radiologists and radiology residents; and (3) nursing, including registered nurses with advanced training in PICC placement. Following placement of a consult, the PICC consult service assessed the patient, obtained consent, and subsequently placed the catheter. Members of the PICC consult service followed a system‐wide protocol wherein target veins were identified by ultrasound prior to attempting catheter placement, and actual placement of the PICC was ultrasound guided. Images obtained during the procedure were permanently documented in the medical record. At the time of this study, no formal protocol existed wherein target veins were mapped for caliber. Operators relied on their professional judgment to determine if vein caliber appeared sufficient to accommodate catheter placement.
All PICCs were placed using industry standard sterile precautions. A universally accepted modified Seldinger technique was used to obtain venous access.[29] A guidewire was then positioned in the desired vessel to facilitate proper venous placement of the catheter. During the course of the study period, catheters used were either single‐ (4 Fr) or double lumen (5 Fr).
Catheters were placed at the bedside by hospitalists or registered nurse teams; the location of the catheter tip at the cavoatrial junction was confirmed by chest radiography. Catheter insertions by radiologists were performed in the interventional radiology suite, and confirmation of location of the catheter tip was obtained with fluoroscopy.
PICC Maintenance
Following placement, nurses managed the PICC site according to nursing policy. Per policy, the site was assessed each shift. Documentation of assessment was recorded in nursing notes. Routine dressing changes were performed every 7 days, and as needed, to maintain a sterile site. Date and time of dressing changes were documented in nursing notes and on the PICC dressing. Catheter hubs and injection ports were disinfected with an antiseptic preparation for 15 seconds and allowed to air dry for 30 seconds prior to accessing the catheter. Catheters were flushed with 10 mL of normal saline before and after use. Any abnormality noted during PICC assessment was relayed to the primary provider. If the catheter did not flush readily or demonstrate appropriate blood return, nursing staff obtained an order for alteplase to be administered in an effort to salvage the line. PICCs were discontinued at the discretion of the healthcare provider.
Participants
Records of all patients 18 years of age and older who underwent PICC placement between January 2009 and January 2010 were reviewed (N=1444) for study inclusion. There were no exclusion criteria.
Data Collection
Patients who experienced complications were identified by electronic medical record review. One‐to‐one matching was performed for age and gender‐matched controls randomly selected from inpatients who underwent PICC placement during the same time period without complications. A total of 170 cases with PICC‐related complications were identified. One hundred seventy exact age‐ and gender‐matched controls, who based upon documentation available in the electronic medical record did not experience complications, were then randomly selected. Prior to data collection, the research team reviewed and discussed the data collection form and agreed upon a standardized protocol for data collection. Data collection was completed by authors J.M. and J.H. on the standardized data collection form. Although a formal analysis of inter‐rater agreement was not performed, J.M. and J.H. discussed any items where questions arose and arrived at a consensus decision regarding completion of the data point.
End points of the chart review were completion of medical therapy for which the PICC was indicated (eg, IV antibiotics or total parenteral nutrition [TPN]) or documentation of a complication that led to 1 of the following: discontinuation of the PICC or adjustment of either catheter placement or medical therapy. All complications were identified via International Classification of Diseases, 9th Revision codes and systematic chart review.
Complications resulting in discontinuation of the PICC, adjustment of catheter placement, or change in medical therapy were identified by review of nursing or physician documentation, and were categorized as follows: mechanical complications (defined as loss of the ability of the catheter to flush or draw properly, inadvertent catheter dislodgement, or retained portion of the catheter following catheter removal), catheter‐associated bloodstream infection (development of a positive blood culture attributable to the central catheter with no other clearly identifiable source of bacteremia present), cellulitis (defined as cellulitis in the extremity where the catheter was placed), bleeding from the site of catheter, fever (for which no other cause could be identified), and catheter‐associated thrombosis (identified by Doppler ultrasonography in patients exhibiting symptoms such as pain, swelling, redness, or warmth in the extremity in which the PICC was placed).[30]
Demographic data were collected, including insurance status, age, ethnicity, and gender. Clinical data included body mass index (BMI), presence of malnutrition (defined by a serum albumin of less than 3 g/dL),[31] previous or active cancer, previous DVT, use of anticoagulants (eg, warfarin, heparin, or low‐molecular‐weight heparin) or antiplatelet agent (eg, aspirin or clopidogrel) at the time of placement, and indication for PICC placement. A patient's history of previous or active cancer and previous DVT were identified by clinical documentation. Indications for PICC placement included: treating infectious processes (ie, infusion of antimicrobials), providing TPN, chemotherapy administration, and IV access. Catheter‐specific data were also collected and included venous access obtained (cephalic, basilic, brachial), catheter size (single lumen [4 Fr] or double lumen [5 Fr]), type of complication, and time to complication. The procedure note accompanying PICC placement was reviewed for data regarding time of day inserted (with after hours defined as documentation of placement occurring after 5 pm), and procedure operator to identify type of team (internal medicine, radiology, nursing) responsible for placement.
Data Analysis
Demographic characteristics and potential risk factors for patients in both the case and control groups of the study were summarized using descriptive statistics: mean ( standard deviation [SD]) for continuous variables and frequency (percent) for categorical variables. Univariate and multivariable conditional logistic regression analyses of variables that were potential risk factors of PICC‐related complications were utilized. A stepwise selection method was used for multivariable conditional logistic regression models. Alpha=0.2 was used for the significance to enter the model, and =0.05 was used for significance level to remain in the model. Attribution of PICC‐related complications was evaluated in terms of odds ratios (OR) and 95% confidence interval (CI). A P value of <0.05 indicated statistical significance. No prospective power analysis was performed. However, for a retrospective power analysis for 1:1 matching with 170 cases and 170 matched controls, assuming 20% of controls were affected and an of 0.05, one would achieve 80% power to detect an odds ratio of 2. SAS 9.2 (SAS Institute Inc., Cary, NC) was used for data analysis.
RESULTS
In 2009, 1444 PICCs were placed, and 170 cases in which patients experienced complications associated with PICC placement were identified, resulting in a complication rate of 11.77% (95% CI: 10.11%‐13.44%). The most common complications experienced by our patient population included catheter‐associated thrombosis (3%, n = 46), mechanical complications (4%, n=67), inadvertent catheter dislodgement (2%, n=36), mechanical dysfunction (2%, n=30), retained portion of the catheter following catheter removal (<1%, n=1), catheter‐associated bloodstream infections (2%, n=24), and cellulitis at the catheter insertion site (1%, n=15). Other documented complications included unexplained fever and bleeding (Table 1).
| Complication | N (%) |
|---|---|
| |
| Thrombosis | 46 (3) |
| Infection | 24 (2) |
| Cellulitis | 15 (1) |
| Mechanical complications* | 67 (4) |
| Unexplained fever | 15 (1) |
| Bleeding | 3 (0) |
| No complication | 1,274 (88) |
The mean age of the total cohort (N=340), comprised of case (N=170) and control (N=170) groups, was 58 years (SD 17), and 55% (n=94) were females. There were no significant differences in complications between groups based on ethnicity (P=0.66). In the case group, 46% (n=78) of PICCs were placed by the radiology team, 41% (n=69) were placed by the internal medicine team, and 14% (n=23) were placed by nursing. In the control group, 44% (n=74) of PICCs were placed by radiology, 36% (n=62) by internal medicine, and 20% (n=34) by nursing. Based on univariate conditional analysis, provider team was not significantly associated with complications (P=0.29).
Predictors of All‐Cause Complications
Based upon univariate conditional logistic regression analyses of complications related to PICC placement (N=340), the following variables demonstrated a statistically significant increased risk for complications: malnutrition (OR: 1.88 [95% CI: 1.023.44], P=0.04) and after‐hours placement (OR: 8.67 [95% CI: 2.62‐28.63], P=0.0004) (Table 2). Anticoagulation was associated with a decreased risk of complications (OR: 0.27 [95% CI: 0.16‐0.45], P=0.04). Based upon multivariable logistic regression analysis, after‐hours placement (OR: 9.52 [95% CI: 2.68‐33.78], P=0.0005) and BMI >30 (OR: 1.98 [95% CI: 1.09‐3.61], P=0.02) were significantly associated with an increased risk of PICC‐associated complications. Conversely, anticoagulation/antiplatelet use was associated with a decreased risk of complications (OR: 0.24 [95% CI: 0.14‐0.43], P<0.0001).
| Variable | Case, N (%) | Control, N (%) | Univariate | Multivariable | ||
|---|---|---|---|---|---|---|
| OR (95% CI) | P Value | AOR (95% CI) | P Value | |||
| ||||||
| Age, y, meanSD | 5817 | 5817 | ||||
| BMI, meanSD | 29.29.5 | 27.97.9 | 1.02 (0.991.05) | 0.12 | ||
| 30 | 108 (64) | 116 (68%) | 1.00 | 1.00 | ||
| >30 | 62 (36) | 54 (32%) | 1.29 (0.792.11) | 0.32 | 1.98 (1.093.61) | 0.02 |
| Length of stay, d, meanSD | 1822 | 1416 | 1.01 (1.001.03) | 0.06 | ||
| Length of stay group, d | 0.11a | |||||
| <7 | 41 (24) | 52 (31) | 1.00 | |||
| 729 | 101 (59) | 103 (61) | 1.19 (0.721.98) | 0.49 | ||
| 30 | 28 (16) | 15 (9) | 2.21 (1.074.58) | 0.03 | ||
| Gender | ||||||
| Female | 94 (55) | 94 (55) | ||||
| Male | 76 (45) | 76 (45) | ||||
| Ethnicity | 0.66a | |||||
| Caucasian | 131 (77) | 125 (74) | 1.00 | |||
| African American | 26 (15) | 28 (16) | 0.88 (0.481.60) | 0.67 | ||
| Hispanic/Asian | 13 (8) | 17 (10) | 0.70 (0.311.58) | 0.38 | ||
| Provider team | 0.29a | |||||
| Radiology | 78 (46) | 74 (44) | 1.00 | |||
| Internal medicine | 69 (41) | 62 (36) | 1.05 (0.681.64) | 0.82 | ||
| Nursing | 23 (14) | 34 (20) | 0.65 (0.351.19) | 0.16 | ||
| Insuranceb | 0.22a | |||||
| Private insurance | 46 (27) | 42 (25) | 1.00 | |||
| Uninsured | 17 (10) | 24 (14) | 0.73 (0.351.55) | 0.41 | ||
| Medicare | 57 (34) | 62 (37) | 0.73 (0.381.40) | 0.34 | ||
| Medicaid | 39 (23) | 25 (15) | 1.51 (0.743.06) | 0.26 | ||
| Tricare/Veterans Administration | 11 (6) | 16 (9) | 0.59 (0.241.45) | 0.25 | ||
| History of DVT | 27 (16) | 26 (15) | 1.05 (0.581.91) | 0.88 | ||
| Malnutritionb | 149 (88) | 134 (79) | 1.88 (1.023.44) | 0.04 | ||
| Cancer | 25 (15) | 36 (21) | 0.58 (0.311.09) | 0.09 | ||
| Fluoroscopy | 129 (76) | 139 (82) | 0.71 (0.421.19) | 0.19 | ||
| Anticoagulation use | 50 (29) | 100 (59) | 0.27 (0.160.45) | <0.0001 | 0.24 (0.140.43) | <0.0001 |
| Multilumenc | 99 (58) | 111 (66) | 0.70 (0.441.11) | 0.13 | ||
| Veinb | 0.39a | |||||
| Basilic | 98 (58) | 86 (51) | 1.00 | |||
| Cephalic | 11 (6) | 8 (5) | 1.37 (0.483.89) | 0.55 | ||
| Brachial | 61 (36) | 74 (44) | 0.70 (0.451.09) | 0.12 | ||
| Internal mammary | 0 (0) | 1 (1) | <0.001 (<0.001>999) | 0.99 | ||
| Time of dayb | ||||||
| Morning/afternoon | 144 (85) | 166 (98) | 1.00 | 1.00 | ||
| After hours | 26 (15) | 3 (2) | 8.67 (2.6228.63) | 0.0004 | 9.52 (2.6833.78) | 0.0005 |
| Indication for PICC | 0.02a | |||||
| Infection | 88 (52) | 71 (42) | 1.00 | |||
| Pneumonia | 21 (12) | 14 (8) | 1.07 (0.502.29) | 0.87 | ||
| Chemotherapy | 5 (3) | 2 (1) | 1.84 (0.349.93) | 0.48 | ||
| IV access | 36 (21) | 66 (39) | 0.44 (0.250.75) | 0.003 | ||
| Total parenteral nutrition | 20 (12) | 17 (10) | 0.96 (0.442.14) | 0.93 | ||
Predictors of Nonmechanical Complications
To study risk factors related to nonmechanical complications, a secondary analysis (N=206) was performed in which all patients who experienced mechanical complications (N=67) and matched controls (N=67) were excluded. Based upon multivariable logistic regression analysis, after‐hours placement (OR: 6.93 [95% CI: 1.35‐35.56], P=0.02) and malnutrition (OR: 2.83 [95% CI: 1.037.81], P=0.04) were significantly associated with increased risk of nonmechanical complications. The use of anticoagulation/antiplatelet agents was associated with decreased risk of nonmechanical complications (OR: 0.17 [95% CI: 0.07‐0.40], P<0.0001). Variables not significantly associated with nonmechanical complications included BMI>30, previous history of DVT, history of cancer, catheter size, and venous access choice (Table 3).
| Variable | Case, N (%) | Control, N (%) | Univariate | Multivariable | ||
|---|---|---|---|---|---|---|
| OR (95% CI) | P Value | AOR (95% CI) | P Value | |||
| ||||||
| Age, y, meanSD | 5816 | 5816 | ||||
| BMI, meanSD | 29.79.8 | 28.57.9 | 1.03 (0.991.07) | 0.22 | ||
| 30 | 64 (62) | 68 (66) | 1.00 | |||
| >30 | 39 (38) | 35 (34) | 1.27 (0.642.49) | 0.49 | ||
| Length of stay, d, meanSD | 2026 | 1418 | 1.02 (1.001.03) | 0.08 | ||
| Length of stay group, d | 0.03a | |||||
| <7 | 22 (21) | 28 (27) | 1.00 | |||
| 729 | 60 (58) | 68 (66) | 0.95 (0.491.82) | 0.87 | ||
| 30 | 21 (20) | 7 (7) | 3.24 (1.238.54) | 0.02 | ||
| Gender | ||||||
| Female | 63 (61) | 63 (61) | ||||
| Male | 40 (39) | 40 (39) | ||||
| Ethnicity | 0.95a | |||||
| Caucasian | 75 (73) | 75 (73) | 1.00 | |||
| African American | 19 (18) | 18 (17) | 1.06 (0.512.21) | 0.87 | ||
| Hispanic/Asian | 9 (9) | 10 (10) | 0.88 (0.322.44) | 0.81 | ||
| Provider team | 0.81a | |||||
| Radiology | 43 (42) | 44 (43) | 1.00 | |||
| Internal medicine | 45 (44) | 41 (40) | 1.11 (0.621.96) | 0.73 | ||
| Nursing | 15 (15) | 18 (17) | 0.86 (0.391.90) | 0.71 | ||
| Insuranceb | 0.22a | |||||
| Private insurance | 29 (28) | 27 (26) | 1.00 | |||
| Uninsured | 13 (13) | 12 (12) | 1.18 (0.433.26) | 0.74 | ||
| Medicare | 32 (31) | 40 (39) | 0.52 (0.211.29) | 0.16 | ||
| Medicaid | 21 (20) | 12 (12) | 1.81 (0.694.74) | 0.23 | ||
| Tricare/Veterans Administration | 8 (8) | 11 (11) | 0.58 (0.191.79) | 0.34 | ||
| History of DVT | 15 (15) | 15 (15) | 1.00 (0.462.16) | 1.00 | ||
| Malnutritionb | 93 (90) | 79 (77) | 2.86 (1.216.76) | 0.02 | 2.83 (1.037.81) | 0.04 |
| Cancer | 17 (17) | 22 (21) | 0.67 (0.301.48) | 0.32 | ||
| Fluoroscopy | 78 (76) | 85 (83) | 0.65 (0.321.31) | 0.23 | ||
| Anticoagulation use | 29 (28) | 60 (58) | 0.21 (0.100.44) | <0.0001 | 0.17 (0.070.40) | <0.0001 |
| Multilumenc | 64 (62) | 67 (66) | 0.83 (0.461.51) | 0.55 | ||
| Veinb | 0.32a | |||||
| Basilic | 54 (52) | 49 (48) | 1.00 | |||
| Cephalic | 8 (8) | 3 (3) | 2.45 (0.649.32) | 0.19 | ||
| Brachial | 41 (40) | 49 (48) | 0.72 (0.421.24) | 0.24 | ||
| Internal mammary | 0 (0) | 1 (1) | <0.001 (<0.001>999) | 0.99 | ||
| Time of dayb | ||||||
| Morning/afternoon | 87 (84) | 100 (98) | 1.00 | 1.00 | ||
| After hours | 16 (16) | 2 (2) | 8.00 (1.8434.79) | 0.006 | 6.93 (1.3535.56) | 0.02 |
| Indication for PICC | 0.13 | |||||
| Infectiond | 52 (50) | 45 (44) | 1.00 | |||
| Pneumonia | 14 (14) | 7 (7) | 1.46 (0.514.18) | 0.48 | ||
| Chemotherapy | 5 (5) | 0 (0) | >999 (<0.001>999) | 0.99 | ||
| IV access | 22 (21) | 43 (42) | 0.48 (0.240.96) | 0.04 | ||
| Total parenteral nutrition | 10 (10) | 8 (8) | 1.08 (0.323.62) | 0.90 | ||
Predictors of Thrombotic Complications
Of 1444 patients who underwent PICC placement, 3% (n=46) were subsequently diagnosed with a catheter‐associated thrombosis, representing 27% of all observed complications. In an attempt to better identify factors predisposing patients to thrombotic complications, an additional subgroup analysis (N=92) was performed on those patients who experienced catheter‐associated thrombosis (N=46) and matched controls (N=46). Variables examined in the analysis included BMI, length of stay (LOS), history of DVT, history of cancer, utilization of anticoagulation/antiplatelet agents, malnutrition, and catheter size.
Based on conditional univariate analyses, the following variables were significantly associated with increased risk of catheter‐associated thrombosis: LOS (as a continuous variable) (OR: 1.04 [95% CI: 1.001.09], P=0.05), malnutrition (OR: 4 [95% CI: 1.1314.18], P=0.03), and after‐hours placement (OR: 8.00 [95% CI: 1.0063.96], P=0.05) (Table 4). Use of anticoagulation/antiplatelet agents (OR: 0.29 [95% CI: 0.11‐0.80], P=0.02) was associated with decreased risk of thrombosis. History of previous DVT and history of cancer were nonsignificant. In the multivariable logistic regression model, malnutrition (OR: 10.16 [95% CI: 1.76‐58.71], P=0.01) remained associated with increased risk of catheter‐associated thrombosis, whereas use of anticoagulation/antiplatelet agents (OR: 0.11 [95% CI: 0.02‐0.51], P=0.005) was associated with decreased risk of catheter‐associated thrombosis (Table 4).
| Variable | Case, N (%) | Control, N (%) | Univariate | Multivariable | ||
|---|---|---|---|---|---|---|
| OR (95% CI) | P Value | AOR (95% CI) | P Value | |||
| ||||||
| Age, y, meanSD | 5818 | 5818 | ||||
| BMI, meanSD | 27.77.1 | 27.77.8 | 1.00 (0.931.08) | 0.98 | ||
| 30 | 34 (74) | 33 (72) | ||||
| >30 | 12 (26) | 13 (28) | 0.83 (0.252.73) | 0.76 | ||
| Length of stay, d, meanSD | 1712 | 119 | 1.04 (1.001.09) | 0.05 | ||
| Length of stay group, d | 0.15 | |||||
| <7 | 8 (17) | 14 (30) | 1.00 | |||
| 729 | 29 (63) | 30 (65) | 1.13 (0.413.07) | 0.82 | ||
| 30 | 9 (20) | 2 (4) | 4.65 (0.9822.13) | 0.05 | ||
| Gender | ||||||
| Female | 26 (57) | 26 (57) | ||||
| Male | 20 (43) | 20 (43) | ||||
| Ethnicity | 0.44a | |||||
| Caucasian | 31 (67) | 36 (78) | 1.00 | |||
| African American | 11 (24) | 6 (13) | 2.02 (0.695.93) | 0.20 | ||
| Hispanic/Asian | 4 (9) | 4 (9) | 1.12 (0.225.68) | 0.89 | ||
| Provider team | 0.26a | |||||
| Radiology | 23 (50) | 19 (41) | 1.00 | |||
| Internal medicine | 20 (43) | 18 (39) | 1.00 (0.432.31) | 1.00 | ||
| Nursing | 3(7) | 9 (20) | 0.33 (0.091.27) | 0.11 | ||
| Insuranceb | 0.38a | |||||
| Private insurance | 13 (28) | 11 (24) | 1.00 | |||
| Uninsured | 8 (17) | 4 (9) | 2.01 (0.3810.58) | 0.41 | ||
| Medicare | 14 (30) | 21 (47) | 0.39 (0.101.47) | 0.16 | ||
| Medicaid | 8 (17) | 7 (16) | 1.23 (0.285.36) | 0.78 | ||
| Tricare/Veterans Administration | 3 (7) | 2 (4) | 1.01 (0.128.27) | 1.00 | ||
| History of DVT | 7 (15) | 8 (17) | 0.88 (0.322.41) | 0.80 | ||
| Malnutritionb | 43 (93) | 33 (73) | 4.00 (1.1314.18) | 0.03 | 10.16 (1.7658.71) | 0.01 |
| Cancer | 10 (22) | 13 (28) | 0.67 (0.241.87) | 0.44 | ||
| Fluoroscopy | 33 (72) | 39 (85) | 0.46 (0.161.31) | 0.14 | ||
| Anticoagulation use | 16 (35) | 28 (61) | 0.29 (0.110.80) | 0.02 | 0.11 (0.020.51) | 0.005 |
| Multilumenc | 22 (48) | 28 (62) | 0.53 (0.231.26) | 0.15 | ||
| Veinb | 0.93a | |||||
| Basilic | 24 (52) | 21 (47) | 1.00 | |||
| Cephalic | 1 (2) | 1 (2) | 0.86 (0.0514.39) | 0.92 | ||
| Brachial | 21 (46) | 22 (49) | 0.75 (0.311.79) | 0.51 | ||
| Internal mammary | 0 (0) | 1 (2) | <0.001 (<0.001>999) | 0.99 | ||
| Time of dayb | ||||||
| Morning/afternoon | 38 (83) | 44 (98) | 1.00 | |||
| After hours | 8 (17) | 1 (2) | 8.00 (1.0063.96) | 0.05 | ||
| Indication for PICC | 0.80a | |||||
| Infectiond | 20 (43) | 17 (37) | 1.00 | |||
| Pneumonia | 5 (11) | 6 (13) | 0.60 (0.142.56) | 0.49 | ||
| Chemotherapy | 3 (7) | 0 (0) | >999 (<0.001>999) | 0.99 | ||
| IV access | 14 (30) | 20 (43) | 0.58 (0.231.44) | 0.24 | ||
| Total parenteral nutrition | 4 (9) | 3 (7) | 1.22 (0.197.70) | 0.83 | ||
DISCUSSION
The goal of this study was to identify factors related to PICC placement that place the general population of patients at risk. The type and rate of complications associated with PICCs in this study were similar to those previously reported in the literature including catheter‐related infection and thrombosis.[10, 32] Two unique risk factors, not well recognized previously,[10, 27, 28, 33] were observed in this study: malnutrition and after‐hours placement. Malnutrition, defined as serum albumin <3 g/dL was associated with an increase in PICC‐related complications (such as catheter‐associated bloodstream infections and cellulitis) and catheter‐related thrombosis. Malnutrition itself has long been associated with a decreased resistance to infection[34]; in addition, low serum albumin may also be a marker of the presence of other severe comorbidities, which may contribute to increased risk of thrombosis. It has been noted in previous studies that critical illness increases risk of thrombosis.[15] Despite an exhaustive search of the literature, we have been unable to find additional studies examining the extent to which malnutrition may impact PICC‐associated complications.
After‐hours placement was also associated with increased nonmechanical complications, as well as catheter‐related thrombosis. In an effort to improve both patient and consulting provider satisfaction and provide more expedient service, PICCs were often placed after hours (between 5 pm and 8 am) by both interventional radiology (n=14) and internal medicine (n=15) teams.
LOS has been associated with PICC placement complications in other studies.[12] In both primary and secondary analyses, hospital stays >30 days were associated with a higher risk of complications than hospitalizations <7 days. In light of the clinical significance of catheter‐related thrombosis, a subgroup analysis of patients with an LOS >30 days was conducted. The conditional univariate regression analysis showed an increased risk with greater LOS, malnutrition, and after‐hours placement. Use of anticoagulant or antiplatelet agents were associated with decreased risk of thrombosis (Table 4). The association between LOS and PICC‐related thrombosis is consistent with findings from Evans et al. involving 1728 patients in a similar center.[12] In these circumstances, increased LOS may be a surrogate marker for increased severity of illness, in that those patients who are more ill require lengthier hospitalizations. In a systematic review and meta‐analysis, Chopra et al. observed that increased severity of illness correlated with higher rates of catheter‐associated thrombosis, which is supportive of these findings.[15]
In the multivariate logistic regression analysis, BMI >30 was associated with a statistically significant increased risk for PICC‐associated complications after adjusting for anticoagulation and time of placement (Table 2). In the secondary analysis, where patients with mechanical complications were removed, BMI >30 was no longer associated with an increased risk for PICC‐associated complications (Table 3). This suggests that patients with a BMI >30 had an increased risk of mechanical complications, but were not necessarily at increased risk of developing other complications, such as catheter‐related thrombosis, infection, or bleeding. This finding is congruent with studies by Evans et al.,[12] who found no association between BMI and catheter‐associated thrombosis. Our association between BMI and complications is unique; to date, there are few additional studies that examine the extent to which BMI impacts the rate and type of complications associated with PICCs. At this time, the mechanism of the association between mechanical complications (such as inadvertent catheter removal or mechanical malfunction) and BMI is uncertain and warrants further investigation.
Use of Anticoagulant Agents
Anticoagulant (ie, any agent used for DVT prophylaxis or therapeutic anticoagulation) or antiplatelet agent use at the time of PICC placement and during the patient's hospitalization was associated with a decreased risk of thrombosis in our analysis. However, it should be noted that no specific anticoagulant agent was studied, and that antiplatelet agents were included in this analysis, unlike that of Evans et al.[12] Although current literature in oncologic populations, as well as the evidence‐based clinical practice guidelines, recommend against routine use of venous thromboprophylaxis in patients with central venous catheters,[33, 35, 36, 37] we believe this deserves further study, particularly in light of conflicting data in this area.[38, 39] Evans et al.[12] noted that although use of anticoagulants initially appeared to be associated with greater incidence of upper extremity venous thrombosis, when previous diagnosis of DVT was removed from the analysis the association was no longer significant.
In our analyses, no associations between catheter size, choice of venous access, history of previous deep venous thrombosis, or history of malignancy and risk for complications were found. Our findings differed from previous studies, where a relationship between increasing catheter bore size and site of access have been associated with increased PICC‐related thrombosis or other complications.[12, 20, 40, 41] There were also no significant differences in risk for complications between provider teams (eg, internal medicine, radiology, nursing) for PICCs placed during the morning or afternoon, which is consistent with findings by Funk et al.[1] Yet, after‐hours placement of PICCs was associated with greater complications than daytime placement. Although the exploration of factors associated with after‐hours placement was beyond the scope of this study, the findings from this study caused the authors, primarily comprised of members of the internal medicine inpatient medicine division, to reexamine the division's protocol on PICC placement. A consensus decision was made to discontinue after‐hours placement of PICCs by internal medicine teams in an effort to promote patient safety until further data could be collected. As a result, internal medicine teams no longer place PICCs after regular working hours at our institution.
Limitations
Limitations include the categorization of antiplatelet and anticoagulant agents together. We did not distinguish between high‐ and low‐dose aspirin, nor did we distinguish between therapeutic dosing of heparin and low‐molecular‐weight heparin versus DVT prophylaxis dosing. Additionally, for patients who were on warfarin or heparin drip, we did not evaluate for therapeutic range of international normalized ratio or partial thromboplastin time, as this was beyond the present scope of this study. In addition, malnutrition defined by albumin alone may have been somewhat narrow, as conditions aside from malnutrition can impact albumin levels. In future evaluations, this relationship may be clarified by including other determinants of clinical malnutrition including BMI <18 or the measurement of prealbumin. For determination of after‐hours placement of PICCs, we relied upon time of procedure dictation, assuming that all dictations immediately followed catheter placement. If there was a lapse in time between catheter placement and dictation, the category may have been recorded in error. Another limitation of after‐hours categorization was that we were unable to determine whether the PICC was placed on a weekend or holiday.
CONCLUSIONS AND FUTURE DIRECTIONS
Our results suggest that more stringent screening of patients undergoing PICC placement may reduce the risk of complications, with special attention to characteristics such as BMI >30, increased LOS, and protein‐calorie malnutrition (albumin <3). Furthermore, placement of PICC lines in emergent or after‐hours settings should be carefully considered and weighed against relative risks of central venous catheter placement. Further examination of the role anticoagulant and antiplatelet agents may have in the prevention of catheter‐related thrombosis should be undertaken. We hope that the identification of these risk factors will decrease the rate of complications and ultimately enhance patient safety and satisfaction.
Acknowledgments
The authors sincerely thank Glen Cryer, Publications Manager, Baylor Scott & White Health, for his assistance with this article.
Disclosures: Nothing to report.
- , , . Two‐year trends of peripherally inserted central catheter‐line complications at a tertiary‐care hospital: role of nursing expertise. Infect Control Hosp Epidemiol. 2001;22:377–379.
- , , . Peripherally inserted central catheters in the intensive care unit. J Intensive Care Med. 1996;11:49–54.
- , , , . Peripherally inserted central catheters in an acute‐care hospital. Arch Intern Med. 1994;154:1833–1837.
- , , , et al. Catheter‐associated bloodstream infection incidence and risk factors in adults with cancer: a prospective cohort study. J Hosp Infect. 2011;78:26–30.
- , . Peripherally inserted central catheters: a report of 2506 catheter days. JPEN J Parenter Enteral Nutr. 1995;19:133–136.
- , , , . Peripherally inserted central catheters in general medicine. Mayo Clin Proc. 1997;72:225–233.
- , , . Bedside placement of peripherally inserted central catheters: a cost‐effectiveness analysis. Radiology. 1998;206:423–428.
- , , , . Frequent asymptomatic pulmonary embolism in patients with deep venous thrombosis. JAMA. 1994;271:223–225.
- , . Experience with PICC at a university medical center. J Intraven Nurs. 1997;20:141–147.
- , , , et al. Upper‐extremity deep vein thrombosis. Risk factors, diagnosis, and complications. Arch Intern Med. 1997;157:57–62.
- , , , . Previous PICC placement may be associated with catheter‐related infections in hemodialysis patients. Cardiovasc Intervent Radiol. 2011;34:120–123.
- , , , et al. Risk of symptomatic DVT associated with peripherally inserted central catheters. Chest. 2010;138:803–810.
- . Be picky about PICCs. ACP Hospitalist, American College of Physicians website. Available at: http://www.acphospitalist.org/archives/2013/09/coverstory.htm. Accessed January 4, 2014.
- , , , , . The risk of bloodstream infection associated with peripherally inserted central catheters compared with central venous catheters in adults: a systematic review and meta‐analysis. Infect Control Hosp Epidemiol. 2013;34:908–918.
- , , , et al. Risk of venous thromboembolism associated with peripherally inserted central catheters: a systematic review and meta‐analysis. Lancet. 2013;382:311–325.
- , , , et al. Prevention of VTE in nonsurgical patients: antithrombotic therapy and prevention of thrombosis, 9th ed: American College of Chest Physicians evidence‐based practice guidelines. Chest. 2012;141(2 suppl):e195S–e226S.
- , , . The clinical significance of peripherally inserted central venous catheter‐related deep vein thrombosis. Neurocrit Care. 2011;15:454–460.
- , , , , , . Peripherally inserted central catheter thrombosis‐reverse tapered versus nontapered catheters: a randomized controlled study. J Vasc Interv Radiol. 2014;25:85–91.
- , , , et al. Upper extremity venous thrombosis diagnosed by duplex scanning. Am J Surg. 1990;160:202–206.
- , , , et al. Reduction of peripherally inserted central catheter‐associated DVT. Chest. 2013;143:627–633.
- . Preventing DVT in peripherally inserted central catheters. Chest. 2013;143:589–590.
- , , , et al. Incidence of upper limb venous thrombosis associated with peripherally inserted central catheters (PICC). Br J Radiol. 2005;78:596–600.
- , , , , , . Computer identification of symptomatic deep venous thrombosis associated with peripherally inserted central catheters. AMIA Annu Symp Proc. 2007:226–230.
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- , , , , . To clot or not to clot? That is the question in central venous catheters. Clin Radiol. 2004;59:349–355.
- , , , et al. The Incidence of PICC line–associated thrombosis with and without the use of prophylactic anticoagulants. JPEN J Parenter Enteral Nutr. 2008;32:443–447.
- , , , , . Efficacy of multifaceted interventions in reducing complications of peripherally inserted central catheter in adult oncology patients. Support Care Cancer. 2010;18:1293–1298.
- , , , , . Peripherally inserted central venous catheter‐associated thrombosis: retrospective analysis of clinical risk factors in adult patients. South Med J. 2006;99:1073–1077.
- . The Seldinger method for PICC insertion. J Intraven Nurs. 1989;12:238–243.
- , , , et al. Venous thrombosis associated with the placement of peripherally inserted central catheters. J Vasc Interv Radiol. 2000;11:1309–1314.
- . Protein‐energy undernutrition. Merk Manual of Diagnosis and Therapy. 18th ed. Available at: http://www.merckmanuals.com/professional/nutritional_disorders/undernutrition/protein‐energy_undernutrition.html. Accessed May 26, 2013.
- , , , , . Risk of venous thromboembolism in hospitalized patients with peripherally inserted central catheters. J Hosp Med. 2009;4:417–422.
- , , , . Risk factors for upper extremity venous thrombosis associated with peripherally inserted central venous catheters. J Vasc Access. 2012;13:231–238.
- . Historical concepts of interactions, synergism and antagonism between nutrition and infection. J Nutr. 2003;133:316S–321S.
- , , , et al. 2008 SOR guidelines for the prevention and treatment of thrombosis associated with central venous catheters in patients with cancer: report from the working group. Ann Oncol. 2009;20:1459–1471.
- , , , , . Thromboprophylaxis in cancer patients with central venous catheters. A systematic review and meta‐analysis. Thromb Haemost. 2008;99:38–43.
- , , , . Thromboprophylaxis for catheter‐related thrombosis in patients with cancer: a systematic review of the randomized, controlled trials. J Thromb Haemost. 2007;5:2552–2554.
- , , , et al. Prevention of central venous catheter associated thrombosis using minidose warfarin in patients with haematological malignancies. Br J Haematol. 1998;10:483–486.
- , , , et al. Very low doses of warfarin can prevent thrombosis in central venous catheters. A randomized prospective trial. Ann Intern Med. 1990;112:423–428.
- , , , , , . The peripherally inserted central catheter (PICC): a prospective study of its natural history after cubital fossa insertion. Anesth Intensive Care. 2002;30:21–24.
- . Thrombotic complications in intravenous access. J Intraven Nurs. 1998;21:96–100.
- , , . Two‐year trends of peripherally inserted central catheter‐line complications at a tertiary‐care hospital: role of nursing expertise. Infect Control Hosp Epidemiol. 2001;22:377–379.
- , , . Peripherally inserted central catheters in the intensive care unit. J Intensive Care Med. 1996;11:49–54.
- , , , . Peripherally inserted central catheters in an acute‐care hospital. Arch Intern Med. 1994;154:1833–1837.
- , , , et al. Catheter‐associated bloodstream infection incidence and risk factors in adults with cancer: a prospective cohort study. J Hosp Infect. 2011;78:26–30.
- , . Peripherally inserted central catheters: a report of 2506 catheter days. JPEN J Parenter Enteral Nutr. 1995;19:133–136.
- , , , . Peripherally inserted central catheters in general medicine. Mayo Clin Proc. 1997;72:225–233.
- , , . Bedside placement of peripherally inserted central catheters: a cost‐effectiveness analysis. Radiology. 1998;206:423–428.
- , , , . Frequent asymptomatic pulmonary embolism in patients with deep venous thrombosis. JAMA. 1994;271:223–225.
- , . Experience with PICC at a university medical center. J Intraven Nurs. 1997;20:141–147.
- , , , et al. Upper‐extremity deep vein thrombosis. Risk factors, diagnosis, and complications. Arch Intern Med. 1997;157:57–62.
- , , , . Previous PICC placement may be associated with catheter‐related infections in hemodialysis patients. Cardiovasc Intervent Radiol. 2011;34:120–123.
- , , , et al. Risk of symptomatic DVT associated with peripherally inserted central catheters. Chest. 2010;138:803–810.
- . Be picky about PICCs. ACP Hospitalist, American College of Physicians website. Available at: http://www.acphospitalist.org/archives/2013/09/coverstory.htm. Accessed January 4, 2014.
- , , , , . The risk of bloodstream infection associated with peripherally inserted central catheters compared with central venous catheters in adults: a systematic review and meta‐analysis. Infect Control Hosp Epidemiol. 2013;34:908–918.
- , , , et al. Risk of venous thromboembolism associated with peripherally inserted central catheters: a systematic review and meta‐analysis. Lancet. 2013;382:311–325.
- , , , et al. Prevention of VTE in nonsurgical patients: antithrombotic therapy and prevention of thrombosis, 9th ed: American College of Chest Physicians evidence‐based practice guidelines. Chest. 2012;141(2 suppl):e195S–e226S.
- , , . The clinical significance of peripherally inserted central venous catheter‐related deep vein thrombosis. Neurocrit Care. 2011;15:454–460.
- , , , , , . Peripherally inserted central catheter thrombosis‐reverse tapered versus nontapered catheters: a randomized controlled study. J Vasc Interv Radiol. 2014;25:85–91.
- , , , et al. Upper extremity venous thrombosis diagnosed by duplex scanning. Am J Surg. 1990;160:202–206.
- , , , et al. Reduction of peripherally inserted central catheter‐associated DVT. Chest. 2013;143:627–633.
- . Preventing DVT in peripherally inserted central catheters. Chest. 2013;143:589–590.
- , , , et al. Incidence of upper limb venous thrombosis associated with peripherally inserted central catheters (PICC). Br J Radiol. 2005;78:596–600.
- , , , , , . Computer identification of symptomatic deep venous thrombosis associated with peripherally inserted central catheters. AMIA Annu Symp Proc. 2007:226–230.
- , . Venous thrombosis related to peripherally inserted central catheters. J Vasc Interv Radiol. 2000;11:837–884.
- , , , , . To clot or not to clot? That is the question in central venous catheters. Clin Radiol. 2004;59:349–355.
- , , , et al. The Incidence of PICC line–associated thrombosis with and without the use of prophylactic anticoagulants. JPEN J Parenter Enteral Nutr. 2008;32:443–447.
- , , , , . Efficacy of multifaceted interventions in reducing complications of peripherally inserted central catheter in adult oncology patients. Support Care Cancer. 2010;18:1293–1298.
- , , , , . Peripherally inserted central venous catheter‐associated thrombosis: retrospective analysis of clinical risk factors in adult patients. South Med J. 2006;99:1073–1077.
- . The Seldinger method for PICC insertion. J Intraven Nurs. 1989;12:238–243.
- , , , et al. Venous thrombosis associated with the placement of peripherally inserted central catheters. J Vasc Interv Radiol. 2000;11:1309–1314.
- . Protein‐energy undernutrition. Merk Manual of Diagnosis and Therapy. 18th ed. Available at: http://www.merckmanuals.com/professional/nutritional_disorders/undernutrition/protein‐energy_undernutrition.html. Accessed May 26, 2013.
- , , , , . Risk of venous thromboembolism in hospitalized patients with peripherally inserted central catheters. J Hosp Med. 2009;4:417–422.
- , , , . Risk factors for upper extremity venous thrombosis associated with peripherally inserted central venous catheters. J Vasc Access. 2012;13:231–238.
- . Historical concepts of interactions, synergism and antagonism between nutrition and infection. J Nutr. 2003;133:316S–321S.
- , , , et al. 2008 SOR guidelines for the prevention and treatment of thrombosis associated with central venous catheters in patients with cancer: report from the working group. Ann Oncol. 2009;20:1459–1471.
- , , , , . Thromboprophylaxis in cancer patients with central venous catheters. A systematic review and meta‐analysis. Thromb Haemost. 2008;99:38–43.
- , , , . Thromboprophylaxis for catheter‐related thrombosis in patients with cancer: a systematic review of the randomized, controlled trials. J Thromb Haemost. 2007;5:2552–2554.
- , , , et al. Prevention of central venous catheter associated thrombosis using minidose warfarin in patients with haematological malignancies. Br J Haematol. 1998;10:483–486.
- , , , et al. Very low doses of warfarin can prevent thrombosis in central venous catheters. A randomized prospective trial. Ann Intern Med. 1990;112:423–428.
- , , , , , . The peripherally inserted central catheter (PICC): a prospective study of its natural history after cubital fossa insertion. Anesth Intensive Care. 2002;30:21–24.
- . Thrombotic complications in intravenous access. J Intraven Nurs. 1998;21:96–100.
© 2014 Society of Hospital Medicine
How to interpret surveys in medical research: A practical approach
Surveys are common in medical research. Although survey research may be subject to inherent self-report bias, surveys have a great impact on policies and practices in medicine, often forming the basis for recommendations or new guidelines.1,2 To interpret and use survey research results, clinicians should be familiar with key elements involved in the creation and validation of surveys.
The purpose of this article is to provide readers with a basic framework for evaluating surveys to allow them to be more informed as consumers of survey research.
IMPORTANT TOOLS IN MEDICAL RESEARCH
Surveys are important tools for answering questions on topics that are difficult to assess using other methods.3 They allow us to gather data systematically from subjects by asking questions, in order to make inferences about a larger population.3,4 Clinicians use surveys to explore the opinions, beliefs, and perceptions of a group, or to investigate physician practice patterns and adherence to clinical guidelines. They may also use surveys to better understand why patients are not engaging in recommended behavioral or lifestyle changes.
Survey methods include interviews (in person, by phone) and questionnaires (paper-and-pencil, e-mailed, online).4
A well-constructed, validated survey can provide powerful data that may influence clinical practice, guide future research development, or drive the development and provision of needed programs and services. Surveys have the potential to transform the ways in which we think about and practice medicine.
READER BEWARE
While survey research in health care appears to have grown exponentially, the quality of reported survey research has not necessarily increased over time.
For consumers of survey research, the adage “reader beware” is apt. Although a considerable number of studies have examined the effects of survey methodology on the validity, reliability, and generalizability of the results,4 medical journals differ in their requirements for reporting survey methods.
In an analysis of 117 articles, Bennett et al3 found that more than 80% did not fully describe the survey development process or pretesting methods. They also found limited guidance and lack of consensus about the best way to report survey research. Of 95 surveys requiring scoring, 66% did not report scoring practices.
Duffett et al5 noted that of 127 critical care medicine surveys, only 36% had been pretested or pilot-tested, and half of all surveys reviewed did not include participant demographics or included only minimal information.
Because journal reporting practices differ, physicians may be unaware of the steps involved in survey construction and validation. Knowledge of these steps is helpful not only in constructing surveys but also in assessing published articles that used survey research.
LIMITATIONS OF SURVEY RESEARCH
Indirect measures of attitudes and behaviors
Surveys that rely on participants’ self-reports of behaviors, attitudes, beliefs, or actions are indirect measures and are susceptible to self-report and social-desirability biases. Participants may overestimate their own expertise or knowledge in self-report surveys. They may wish to reduce embarrassment6 or answer in ways that would make them “look better,”7 resulting in social-desirability bias. These issues need to be mentioned in the limitations section in papers reporting survey research.
Questions and response choices
The data derived from surveys are only as good as the questions that are asked.8 Stone9 noted that questions should be intelligible, unambiguous, and unbiased. If respondents do not comprehend questions as researchers intended, if questionnaire response choices are inadequate, or if questions trigger unintended emotional responses,10–14 researchers may unwittingly introduce error, which will affect the validity of results. Even seemingly objective questions, such as those related to clinical algorithm use, practice patterns, or equipment available to hospital staff, may be interpreted differently by different respondents.
In their eagerness to launch a survey, clinician researchers may not realize that it must be carefully constructed. A focus on question development and validation is critical, as the questions determine the quality of the data derived from the survey.8 Even the position of the question or answer in the survey can affect how participants respond,15 as they may be guided to a response choice by preceding questions.16
WHAT DO YOU NEED TO KNOW ABOUT ASSESSING SURVEY RESEARCH?
What follows are questions and a basic framework that can be used to evaluate published survey research. Recommendations are based on the work of survey scientists,4,7,10,14,15,17,18 survey researchers in medicine and the social sciences, and national standards for test and questionnaire construction and validation (Table 1).4,19,20
Who created the survey? How did they do it?
How the survey was created should be sufficiently described to allow readers to judge the adequacy of instrument development.3–5 It is generally recommended that feedback from multiple sources be solicited during survey creation. Both questionnaire-design experts and subject-matter experts are considered critical in the process.4
What question was the survey designed to answer?
Is the objective of the study articulated in the paper? 3,20 To judge survey research, readers need to know if the survey appears to adequately address the research question or questions and the objectives of the study in terms of methods used.4
Was evidence on validity gathered?
Instrument pretesting and field testing are considered best practices by the American Association for Public Opinion Research, a professional organization for US survey scientists.4
Pretesting can include cognitive interviewing, the use of questionnaire appraisal tools, and hybrid methods, all of which are aimed at addressing validity issues.21 Pretesting with a group of participants similar to the target population allows for assessment of item ambiguity, instrument ease of use, adequacy of response categories (response choices), and time to completion.4,12
Cognitive interviewing is designed to explore respondents’ comprehension of questions, response processes, and decision processes governing how they answer questions.4,7,10,11 In cognitive interviewing, respondents are generally interviewed one on one. Techniques vary, but typically include “think alouds” (in which a respondent is asked to verbalize thoughts while responding to questions) and “verbal probing” (in which the respondent answers a question, then is asked follow-up questions as the interviewer probes for information related to the response choice or question itself).7 These techniques can provide evidence that researchers are actually measuring what they set out to measure and not an unrelated construct.4,19
Field testing of a survey under realistic conditions can help to uncover problems in administration, such as issues in standardization of key procedures, and to ensure that the survey was administered as the researchers intended.21,22 Field testing is vital before phone or in-person interviews to ensure standardization of any critical procedures. Pilot testing in a sample similar to the intended population allows for further refinement, with deletion of problem items, before the survey is launched.15
Because even “objective” questions can be somewhat subjective, all research surveys should go through some type of pretesting.4,21 Based on the results of pretesting and field testing, surveys should then be revised before launch.4,21 If an article on a self-report survey makes no mention of survey validation steps, readers may well question the validity of the results.
Are the survey questions and response choices understandable?
Is the meaning of each question unambiguous? Is the reading level appropriate for the sample population (a critical consideration in patient surveys)? Do any of the items actually ask two different questions?13 An example would be: “Was the representative courteous and prompt?” as it is possible to be courteous, but not prompt, and vice versa. If so, respondents may be confused or frustrated in attempting to answer it. If a rating scale is used throughout the questionnaire, are the anchors appropriate? For example, a question may be written in such a way that respondents want to answer “yes/no” or “agree/disagree,” but the scale used may include response options such as “poor,” “marginal,” “good,” and “excellent.” Items with Likert-response formats are commonly used in self-report surveys and allow participants to respond to a statement by choosing from a range of responses (eg, strongly disagree to strongly agree), often spaced horizontally under a line.
It is recommended that surveys also include options for answers beyond the response choices provided,20 such as comment boxes or fill-in-the-blank items. Surveys with a closed-response format may constrain the quality of data collected because investigators may not foresee all possible answers. Surveys need to be available for review either within the article itself, in an appendix, or as supplementary material that is available elsewhere.
Does the sample appear to be appropriate?
Articles that report the results of surveys should describe the target population, the sample design, and, in a demographic table, respondents and nonrespondents. To judge appropriateness, several questions can be asked regarding sampling:
Target population. Is the population of interest (ie, the target population) described, including regional demographics, if applicable? The relationship between the sample and the target population is important, as a nonrepresentative sample may result in misleading conclusions about the population of interest.
Sampling frame. Who had an opportunity to participate in the survey? At its simplest, the sampling frame establishes who (or what, in the case of institutions) should be included within the sample. This is typically a list of elements (Groves et al4) that acts to “frame” or define the sample to be selected. Where the target population may be all academic internal medicine physicians in the United States, the sampling frame may be all male and female US physicians who are members of particular internal medicine professional organizations, identified by their directory email addresses.
Sample design. How was the sample actually selected?4 For example, did investigators use a convenience sample of colleagues at other institutions or use a stratified random sample, ensuring adequate representation of respondents with certain characteristics?
Description of respondents. How is the sample of respondents described? Are demographic features reported, including statistics on regional or national representativeness?5 Does the sample of survey respondents appear to be representative of the researcher’s population of interest (ie, the target population)?3,23 If not, is this adequately described in the limitations section? Although outcomes will not be available on nonrespondents, demographic and baseline data often are available and should be reported. Are there systematic differences between respondents and nonrespondents?
Was the response rate adequate?
Was the response rate adequate, given the number of participants initially recruited? If the response rate was not adequate, did the researchers discuss this limitation?
Maximum response rate, defined as the total number of surveys returned divided by the total number of surveys sent,18 may be difficult to calculate with electronic or Web-based survey platforms. When the maximum response rate cannot be calculated, this issue needs to be addressed in the article’s limitations section.
The number of surveys has increased across fields over the past few decades, but survey response rates in general have decreased.17,21,24,25 In fields outside of clinical medicine, response rates in the 40% range are common.17 In the 1990s, the mean response rate for surveys published in medical journals (mailed surveys) was approximately 60%.26 A 2001 review of physician questionnaire studies found a similar average response rate (61%), with a 52% response rate for large-sample surveys.27 In 2002, Field et al28 examined the impact of incentives in physician survey studies and found response rates ranging from 8.5% to 80%.
Importantly, electronically delivered surveys (e-mail, Web-based) often have lower response rates than mailed surveys.24,29 Nominal financial incentives have been associated with enhanced response rates.28
A relatively low response rate does not necessarily mean you cannot trust the data. Survey scientists note that the representativeness of the sample may be more critical than response rate alone.17 Studies with small sample sizes may be more representative—and findings more valid—than those with large samples, if large samples are nonrepresentative when considering the target population.17
Do the conclusions go beyond the data?
Are the inferences overreaching, in view of the survey design? In studies with low response rates and nonrepresentative samples, researchers must be careful in interpreting the results. If the results cannot be generalized beyond the research sample, is this clear from the limitations, discussion, and conclusion sections?
In this review, we have summarized the findings of three published surveys1,2,30 and commented on how they appear to meet—or don’t quite meet—recommendations for survey development, validation, and use. The papers chosen were deemed strong examples in particular categories, such as description of survey authorship,1 instrument validation,30 sampling methodology,2 and response rate.1
It should be noted that even when surveys are conducted with the utmost rigor, survey reporting may leave out critical details. Survey methodology may not be adequately described for a variety of reasons, including researchers’ training in survey design and methodology; a lack of universally accepted journal-reporting guidelines3; and even journals’ space limitations. At times, journals may excise descriptions of survey development and validation, deeming these sections superfluous. Limitations sections can be critical to interpreting the results of survey research and evaluating the scope of conclusions.
- Jha AK, DesRoches CM, Campbell EG, et al. Use of electronic health records in US hospitals. N Engl J Med 2009; 360:1628–1638.
- Angus DC, Shorr AF, White A, Dremsizov TT, Schmitz RJ, Kelley MA; Committee on Manpower for Pulmonary and Critical Care Societies (COMPACCS). Critical care delivery in the United States: distribution of services and compliance with Leapfrog recommendations. Crit Care Med 2006; 34:1016–1024.
- Bennett C, Khangura S, Brehaut JC, et al. Reporting guidelines for survey research: an analysis of published guidance and reporting practices. PLoS Med 2010; 8:e1001069.
- Groves RM, Fowler FJ, Couper MP, Lepkowski JM, Singer E, Tourangeau R. Survey Methodology. 2nd ed. Hoboken, NJ: John Wiley and Sons, Inc; 2009.
- Duffett M, Burns KE, Adhikari NK, et al. Quality of reporting of surveys in critical care journals: a methodologic review. Crit Care Med 2012; 40:441–449.
- Mattell MS, Jacoby J. Is there an optimal number of alternatives for Likert-scale items? Effects of testing time and scale properties. J Appl Psychol 1972; 56:506–509.
- Willis GB. Cognitive Interviewing. A “How To” Guide. Research Triangle Institute. Presented at the meeting of the American Statistical Association; 1999. http://fog.its.uiowa.edu/~c07b209/interview.pdf. Accessed June 3, 2013.
- Schwarz N. Self-reports. How the questions shape the answers. Amer Psychol 1999; 54:93–105.
- Stone DH. Design a questionnaire. BMJ 1993; 307:1264–1266.
- Willis GB, Royston P, Bercini D. The use of verbal report methods in the development and testing of survey questionnaires. Appl Cogn Psychol 1991; 5:251–267.
- Desimone LM, LeFloch KC. Are we asking the right questions? Using cognitive interviews to improve surveys in education research. Educ Eval Policy Anal 2004; 26:1–22.
- Presser S, Couper MP, Lessler JT, et al. Methods for testing and evaluating survey questions. Public Opin Q 2004; 68:109–130.
- Rogers G. Accreditation Board for Engineering and Technology (ABET), Inc. Sample Protocol for Pilot Testing Survey Items. www.abet.org/WorkArea/DownloadAsset.aspx?id=1299. Accessed January 22, 2013.
- Schwarz N, Oyserman D. Asking questions about behavior: cognition, communication, and questionnaire construction. Am J Eval 2001; 22:127–160.
- Bradburn N, Sudman S, Wansink B. Asking Questions. The Definitive Guide to Questionnaire Design—For Market Research, Political Polls, and Social and Health Questionnaires. San Francisco, CA: Jossey-Bass; 2004.
- Stone AA, Broderick JE, Schwartz JE, Schwarz N. Context effects in survey ratings of health, symptoms, and satisfaction. Med Care 2008; 46:662–667.
- Cook C, Heath F, Thompson RL. A meta-analysis of response rates in Web or internet-based surveys. Educ Psychol Meas 2000; 60:821–836.
- Kaplowitz MD, Hadlock TD, Levine R. A comparison of Web and mail survey response rates. Public Opin Q 2004; 68:94–101.
- American Educational Research Association. Standards for Educational and Psychological Testing/American Educational Research Association, American Psychological Association, National Council on Measurement in Education. Washington, DC: American Educational Research Association; 1999.
- Burns KE, Duffett M, Kho ME, et al; ACCADEMY Group. A guide for the design and conduct of self-administered surveys of clinicians. CMAJ 2008; 179:245–252.
- American Association for Public Opinion Research (AAPOR). http://www.aapor.org/Home.htm. Accessed June 3, 2013.
- National Center for Education Statistics. Planning and Design of Surveys. http://nces.ed.gov/statprog/2002/std2_1.asp. Accessed January 22, 2013.
- Bordens KS, Abbott BB. Research Design and Methods. A Process Approach. 6th ed. New York, NY: McGraw-Hill; 2004.
- Sheehan K. Email survey response rates: a review. JCMC 2001. http://jcmc.indiana.edu/vol6/issue2/sheehan.html. Accessed January 22, 2013.
- Baruch Y, Holtom BC. Survey response rate levels and trends in organizational research. Hum Relat 2008; 61:1139–1160.
- Asch DA, Jedrziewski MK, Christakis NA. Response rates to mail surveys published in medical journals. J Clin Epidemiol 1997; 50:1129–1136.
- Cummings SM, Savitz LA, Konrad TR. Reported response rates to mailed physician questionnaires. Health Services Res 2001; 35:1347–1355.
- Field TS, Cadoret CA, Brown ML, et al. Surveying physicians. Do components of the “Total Design Approach” to optimizing survey response rates apply to physicians? Med Care 2002; 40:596–606.
- Converse PD, Wolfe EW, Huang X, Oswald FL. Response rates for mixed-mode surveys using mail and e-mail/Web. Am J Eval 2008; 29:99–107.
- Hirshberg E, Lacroix J, Sward K, Willson D, Morris AH. Blood glucose control in critically ill adults and children: a survey on stated practice. Chest 2008; 133:1328–1335.
Surveys are common in medical research. Although survey research may be subject to inherent self-report bias, surveys have a great impact on policies and practices in medicine, often forming the basis for recommendations or new guidelines.1,2 To interpret and use survey research results, clinicians should be familiar with key elements involved in the creation and validation of surveys.
The purpose of this article is to provide readers with a basic framework for evaluating surveys to allow them to be more informed as consumers of survey research.
IMPORTANT TOOLS IN MEDICAL RESEARCH
Surveys are important tools for answering questions on topics that are difficult to assess using other methods.3 They allow us to gather data systematically from subjects by asking questions, in order to make inferences about a larger population.3,4 Clinicians use surveys to explore the opinions, beliefs, and perceptions of a group, or to investigate physician practice patterns and adherence to clinical guidelines. They may also use surveys to better understand why patients are not engaging in recommended behavioral or lifestyle changes.
Survey methods include interviews (in person, by phone) and questionnaires (paper-and-pencil, e-mailed, online).4
A well-constructed, validated survey can provide powerful data that may influence clinical practice, guide future research development, or drive the development and provision of needed programs and services. Surveys have the potential to transform the ways in which we think about and practice medicine.
READER BEWARE
While survey research in health care appears to have grown exponentially, the quality of reported survey research has not necessarily increased over time.
For consumers of survey research, the adage “reader beware” is apt. Although a considerable number of studies have examined the effects of survey methodology on the validity, reliability, and generalizability of the results,4 medical journals differ in their requirements for reporting survey methods.
In an analysis of 117 articles, Bennett et al3 found that more than 80% did not fully describe the survey development process or pretesting methods. They also found limited guidance and lack of consensus about the best way to report survey research. Of 95 surveys requiring scoring, 66% did not report scoring practices.
Duffett et al5 noted that of 127 critical care medicine surveys, only 36% had been pretested or pilot-tested, and half of all surveys reviewed did not include participant demographics or included only minimal information.
Because journal reporting practices differ, physicians may be unaware of the steps involved in survey construction and validation. Knowledge of these steps is helpful not only in constructing surveys but also in assessing published articles that used survey research.
LIMITATIONS OF SURVEY RESEARCH
Indirect measures of attitudes and behaviors
Surveys that rely on participants’ self-reports of behaviors, attitudes, beliefs, or actions are indirect measures and are susceptible to self-report and social-desirability biases. Participants may overestimate their own expertise or knowledge in self-report surveys. They may wish to reduce embarrassment6 or answer in ways that would make them “look better,”7 resulting in social-desirability bias. These issues need to be mentioned in the limitations section in papers reporting survey research.
Questions and response choices
The data derived from surveys are only as good as the questions that are asked.8 Stone9 noted that questions should be intelligible, unambiguous, and unbiased. If respondents do not comprehend questions as researchers intended, if questionnaire response choices are inadequate, or if questions trigger unintended emotional responses,10–14 researchers may unwittingly introduce error, which will affect the validity of results. Even seemingly objective questions, such as those related to clinical algorithm use, practice patterns, or equipment available to hospital staff, may be interpreted differently by different respondents.
In their eagerness to launch a survey, clinician researchers may not realize that it must be carefully constructed. A focus on question development and validation is critical, as the questions determine the quality of the data derived from the survey.8 Even the position of the question or answer in the survey can affect how participants respond,15 as they may be guided to a response choice by preceding questions.16
WHAT DO YOU NEED TO KNOW ABOUT ASSESSING SURVEY RESEARCH?
What follows are questions and a basic framework that can be used to evaluate published survey research. Recommendations are based on the work of survey scientists,4,7,10,14,15,17,18 survey researchers in medicine and the social sciences, and national standards for test and questionnaire construction and validation (Table 1).4,19,20
Who created the survey? How did they do it?
How the survey was created should be sufficiently described to allow readers to judge the adequacy of instrument development.3–5 It is generally recommended that feedback from multiple sources be solicited during survey creation. Both questionnaire-design experts and subject-matter experts are considered critical in the process.4
What question was the survey designed to answer?
Is the objective of the study articulated in the paper? 3,20 To judge survey research, readers need to know if the survey appears to adequately address the research question or questions and the objectives of the study in terms of methods used.4
Was evidence on validity gathered?
Instrument pretesting and field testing are considered best practices by the American Association for Public Opinion Research, a professional organization for US survey scientists.4
Pretesting can include cognitive interviewing, the use of questionnaire appraisal tools, and hybrid methods, all of which are aimed at addressing validity issues.21 Pretesting with a group of participants similar to the target population allows for assessment of item ambiguity, instrument ease of use, adequacy of response categories (response choices), and time to completion.4,12
Cognitive interviewing is designed to explore respondents’ comprehension of questions, response processes, and decision processes governing how they answer questions.4,7,10,11 In cognitive interviewing, respondents are generally interviewed one on one. Techniques vary, but typically include “think alouds” (in which a respondent is asked to verbalize thoughts while responding to questions) and “verbal probing” (in which the respondent answers a question, then is asked follow-up questions as the interviewer probes for information related to the response choice or question itself).7 These techniques can provide evidence that researchers are actually measuring what they set out to measure and not an unrelated construct.4,19
Field testing of a survey under realistic conditions can help to uncover problems in administration, such as issues in standardization of key procedures, and to ensure that the survey was administered as the researchers intended.21,22 Field testing is vital before phone or in-person interviews to ensure standardization of any critical procedures. Pilot testing in a sample similar to the intended population allows for further refinement, with deletion of problem items, before the survey is launched.15
Because even “objective” questions can be somewhat subjective, all research surveys should go through some type of pretesting.4,21 Based on the results of pretesting and field testing, surveys should then be revised before launch.4,21 If an article on a self-report survey makes no mention of survey validation steps, readers may well question the validity of the results.
Are the survey questions and response choices understandable?
Is the meaning of each question unambiguous? Is the reading level appropriate for the sample population (a critical consideration in patient surveys)? Do any of the items actually ask two different questions?13 An example would be: “Was the representative courteous and prompt?” as it is possible to be courteous, but not prompt, and vice versa. If so, respondents may be confused or frustrated in attempting to answer it. If a rating scale is used throughout the questionnaire, are the anchors appropriate? For example, a question may be written in such a way that respondents want to answer “yes/no” or “agree/disagree,” but the scale used may include response options such as “poor,” “marginal,” “good,” and “excellent.” Items with Likert-response formats are commonly used in self-report surveys and allow participants to respond to a statement by choosing from a range of responses (eg, strongly disagree to strongly agree), often spaced horizontally under a line.
It is recommended that surveys also include options for answers beyond the response choices provided,20 such as comment boxes or fill-in-the-blank items. Surveys with a closed-response format may constrain the quality of data collected because investigators may not foresee all possible answers. Surveys need to be available for review either within the article itself, in an appendix, or as supplementary material that is available elsewhere.
Does the sample appear to be appropriate?
Articles that report the results of surveys should describe the target population, the sample design, and, in a demographic table, respondents and nonrespondents. To judge appropriateness, several questions can be asked regarding sampling:
Target population. Is the population of interest (ie, the target population) described, including regional demographics, if applicable? The relationship between the sample and the target population is important, as a nonrepresentative sample may result in misleading conclusions about the population of interest.
Sampling frame. Who had an opportunity to participate in the survey? At its simplest, the sampling frame establishes who (or what, in the case of institutions) should be included within the sample. This is typically a list of elements (Groves et al4) that acts to “frame” or define the sample to be selected. Where the target population may be all academic internal medicine physicians in the United States, the sampling frame may be all male and female US physicians who are members of particular internal medicine professional organizations, identified by their directory email addresses.
Sample design. How was the sample actually selected?4 For example, did investigators use a convenience sample of colleagues at other institutions or use a stratified random sample, ensuring adequate representation of respondents with certain characteristics?
Description of respondents. How is the sample of respondents described? Are demographic features reported, including statistics on regional or national representativeness?5 Does the sample of survey respondents appear to be representative of the researcher’s population of interest (ie, the target population)?3,23 If not, is this adequately described in the limitations section? Although outcomes will not be available on nonrespondents, demographic and baseline data often are available and should be reported. Are there systematic differences between respondents and nonrespondents?
Was the response rate adequate?
Was the response rate adequate, given the number of participants initially recruited? If the response rate was not adequate, did the researchers discuss this limitation?
Maximum response rate, defined as the total number of surveys returned divided by the total number of surveys sent,18 may be difficult to calculate with electronic or Web-based survey platforms. When the maximum response rate cannot be calculated, this issue needs to be addressed in the article’s limitations section.
The number of surveys has increased across fields over the past few decades, but survey response rates in general have decreased.17,21,24,25 In fields outside of clinical medicine, response rates in the 40% range are common.17 In the 1990s, the mean response rate for surveys published in medical journals (mailed surveys) was approximately 60%.26 A 2001 review of physician questionnaire studies found a similar average response rate (61%), with a 52% response rate for large-sample surveys.27 In 2002, Field et al28 examined the impact of incentives in physician survey studies and found response rates ranging from 8.5% to 80%.
Importantly, electronically delivered surveys (e-mail, Web-based) often have lower response rates than mailed surveys.24,29 Nominal financial incentives have been associated with enhanced response rates.28
A relatively low response rate does not necessarily mean you cannot trust the data. Survey scientists note that the representativeness of the sample may be more critical than response rate alone.17 Studies with small sample sizes may be more representative—and findings more valid—than those with large samples, if large samples are nonrepresentative when considering the target population.17
Do the conclusions go beyond the data?
Are the inferences overreaching, in view of the survey design? In studies with low response rates and nonrepresentative samples, researchers must be careful in interpreting the results. If the results cannot be generalized beyond the research sample, is this clear from the limitations, discussion, and conclusion sections?
In this review, we have summarized the findings of three published surveys1,2,30 and commented on how they appear to meet—or don’t quite meet—recommendations for survey development, validation, and use. The papers chosen were deemed strong examples in particular categories, such as description of survey authorship,1 instrument validation,30 sampling methodology,2 and response rate.1
It should be noted that even when surveys are conducted with the utmost rigor, survey reporting may leave out critical details. Survey methodology may not be adequately described for a variety of reasons, including researchers’ training in survey design and methodology; a lack of universally accepted journal-reporting guidelines3; and even journals’ space limitations. At times, journals may excise descriptions of survey development and validation, deeming these sections superfluous. Limitations sections can be critical to interpreting the results of survey research and evaluating the scope of conclusions.
Surveys are common in medical research. Although survey research may be subject to inherent self-report bias, surveys have a great impact on policies and practices in medicine, often forming the basis for recommendations or new guidelines.1,2 To interpret and use survey research results, clinicians should be familiar with key elements involved in the creation and validation of surveys.
The purpose of this article is to provide readers with a basic framework for evaluating surveys to allow them to be more informed as consumers of survey research.
IMPORTANT TOOLS IN MEDICAL RESEARCH
Surveys are important tools for answering questions on topics that are difficult to assess using other methods.3 They allow us to gather data systematically from subjects by asking questions, in order to make inferences about a larger population.3,4 Clinicians use surveys to explore the opinions, beliefs, and perceptions of a group, or to investigate physician practice patterns and adherence to clinical guidelines. They may also use surveys to better understand why patients are not engaging in recommended behavioral or lifestyle changes.
Survey methods include interviews (in person, by phone) and questionnaires (paper-and-pencil, e-mailed, online).4
A well-constructed, validated survey can provide powerful data that may influence clinical practice, guide future research development, or drive the development and provision of needed programs and services. Surveys have the potential to transform the ways in which we think about and practice medicine.
READER BEWARE
While survey research in health care appears to have grown exponentially, the quality of reported survey research has not necessarily increased over time.
For consumers of survey research, the adage “reader beware” is apt. Although a considerable number of studies have examined the effects of survey methodology on the validity, reliability, and generalizability of the results,4 medical journals differ in their requirements for reporting survey methods.
In an analysis of 117 articles, Bennett et al3 found that more than 80% did not fully describe the survey development process or pretesting methods. They also found limited guidance and lack of consensus about the best way to report survey research. Of 95 surveys requiring scoring, 66% did not report scoring practices.
Duffett et al5 noted that of 127 critical care medicine surveys, only 36% had been pretested or pilot-tested, and half of all surveys reviewed did not include participant demographics or included only minimal information.
Because journal reporting practices differ, physicians may be unaware of the steps involved in survey construction and validation. Knowledge of these steps is helpful not only in constructing surveys but also in assessing published articles that used survey research.
LIMITATIONS OF SURVEY RESEARCH
Indirect measures of attitudes and behaviors
Surveys that rely on participants’ self-reports of behaviors, attitudes, beliefs, or actions are indirect measures and are susceptible to self-report and social-desirability biases. Participants may overestimate their own expertise or knowledge in self-report surveys. They may wish to reduce embarrassment6 or answer in ways that would make them “look better,”7 resulting in social-desirability bias. These issues need to be mentioned in the limitations section in papers reporting survey research.
Questions and response choices
The data derived from surveys are only as good as the questions that are asked.8 Stone9 noted that questions should be intelligible, unambiguous, and unbiased. If respondents do not comprehend questions as researchers intended, if questionnaire response choices are inadequate, or if questions trigger unintended emotional responses,10–14 researchers may unwittingly introduce error, which will affect the validity of results. Even seemingly objective questions, such as those related to clinical algorithm use, practice patterns, or equipment available to hospital staff, may be interpreted differently by different respondents.
In their eagerness to launch a survey, clinician researchers may not realize that it must be carefully constructed. A focus on question development and validation is critical, as the questions determine the quality of the data derived from the survey.8 Even the position of the question or answer in the survey can affect how participants respond,15 as they may be guided to a response choice by preceding questions.16
WHAT DO YOU NEED TO KNOW ABOUT ASSESSING SURVEY RESEARCH?
What follows are questions and a basic framework that can be used to evaluate published survey research. Recommendations are based on the work of survey scientists,4,7,10,14,15,17,18 survey researchers in medicine and the social sciences, and national standards for test and questionnaire construction and validation (Table 1).4,19,20
Who created the survey? How did they do it?
How the survey was created should be sufficiently described to allow readers to judge the adequacy of instrument development.3–5 It is generally recommended that feedback from multiple sources be solicited during survey creation. Both questionnaire-design experts and subject-matter experts are considered critical in the process.4
What question was the survey designed to answer?
Is the objective of the study articulated in the paper? 3,20 To judge survey research, readers need to know if the survey appears to adequately address the research question or questions and the objectives of the study in terms of methods used.4
Was evidence on validity gathered?
Instrument pretesting and field testing are considered best practices by the American Association for Public Opinion Research, a professional organization for US survey scientists.4
Pretesting can include cognitive interviewing, the use of questionnaire appraisal tools, and hybrid methods, all of which are aimed at addressing validity issues.21 Pretesting with a group of participants similar to the target population allows for assessment of item ambiguity, instrument ease of use, adequacy of response categories (response choices), and time to completion.4,12
Cognitive interviewing is designed to explore respondents’ comprehension of questions, response processes, and decision processes governing how they answer questions.4,7,10,11 In cognitive interviewing, respondents are generally interviewed one on one. Techniques vary, but typically include “think alouds” (in which a respondent is asked to verbalize thoughts while responding to questions) and “verbal probing” (in which the respondent answers a question, then is asked follow-up questions as the interviewer probes for information related to the response choice or question itself).7 These techniques can provide evidence that researchers are actually measuring what they set out to measure and not an unrelated construct.4,19
Field testing of a survey under realistic conditions can help to uncover problems in administration, such as issues in standardization of key procedures, and to ensure that the survey was administered as the researchers intended.21,22 Field testing is vital before phone or in-person interviews to ensure standardization of any critical procedures. Pilot testing in a sample similar to the intended population allows for further refinement, with deletion of problem items, before the survey is launched.15
Because even “objective” questions can be somewhat subjective, all research surveys should go through some type of pretesting.4,21 Based on the results of pretesting and field testing, surveys should then be revised before launch.4,21 If an article on a self-report survey makes no mention of survey validation steps, readers may well question the validity of the results.
Are the survey questions and response choices understandable?
Is the meaning of each question unambiguous? Is the reading level appropriate for the sample population (a critical consideration in patient surveys)? Do any of the items actually ask two different questions?13 An example would be: “Was the representative courteous and prompt?” as it is possible to be courteous, but not prompt, and vice versa. If so, respondents may be confused or frustrated in attempting to answer it. If a rating scale is used throughout the questionnaire, are the anchors appropriate? For example, a question may be written in such a way that respondents want to answer “yes/no” or “agree/disagree,” but the scale used may include response options such as “poor,” “marginal,” “good,” and “excellent.” Items with Likert-response formats are commonly used in self-report surveys and allow participants to respond to a statement by choosing from a range of responses (eg, strongly disagree to strongly agree), often spaced horizontally under a line.
It is recommended that surveys also include options for answers beyond the response choices provided,20 such as comment boxes or fill-in-the-blank items. Surveys with a closed-response format may constrain the quality of data collected because investigators may not foresee all possible answers. Surveys need to be available for review either within the article itself, in an appendix, or as supplementary material that is available elsewhere.
Does the sample appear to be appropriate?
Articles that report the results of surveys should describe the target population, the sample design, and, in a demographic table, respondents and nonrespondents. To judge appropriateness, several questions can be asked regarding sampling:
Target population. Is the population of interest (ie, the target population) described, including regional demographics, if applicable? The relationship between the sample and the target population is important, as a nonrepresentative sample may result in misleading conclusions about the population of interest.
Sampling frame. Who had an opportunity to participate in the survey? At its simplest, the sampling frame establishes who (or what, in the case of institutions) should be included within the sample. This is typically a list of elements (Groves et al4) that acts to “frame” or define the sample to be selected. Where the target population may be all academic internal medicine physicians in the United States, the sampling frame may be all male and female US physicians who are members of particular internal medicine professional organizations, identified by their directory email addresses.
Sample design. How was the sample actually selected?4 For example, did investigators use a convenience sample of colleagues at other institutions or use a stratified random sample, ensuring adequate representation of respondents with certain characteristics?
Description of respondents. How is the sample of respondents described? Are demographic features reported, including statistics on regional or national representativeness?5 Does the sample of survey respondents appear to be representative of the researcher’s population of interest (ie, the target population)?3,23 If not, is this adequately described in the limitations section? Although outcomes will not be available on nonrespondents, demographic and baseline data often are available and should be reported. Are there systematic differences between respondents and nonrespondents?
Was the response rate adequate?
Was the response rate adequate, given the number of participants initially recruited? If the response rate was not adequate, did the researchers discuss this limitation?
Maximum response rate, defined as the total number of surveys returned divided by the total number of surveys sent,18 may be difficult to calculate with electronic or Web-based survey platforms. When the maximum response rate cannot be calculated, this issue needs to be addressed in the article’s limitations section.
The number of surveys has increased across fields over the past few decades, but survey response rates in general have decreased.17,21,24,25 In fields outside of clinical medicine, response rates in the 40% range are common.17 In the 1990s, the mean response rate for surveys published in medical journals (mailed surveys) was approximately 60%.26 A 2001 review of physician questionnaire studies found a similar average response rate (61%), with a 52% response rate for large-sample surveys.27 In 2002, Field et al28 examined the impact of incentives in physician survey studies and found response rates ranging from 8.5% to 80%.
Importantly, electronically delivered surveys (e-mail, Web-based) often have lower response rates than mailed surveys.24,29 Nominal financial incentives have been associated with enhanced response rates.28
A relatively low response rate does not necessarily mean you cannot trust the data. Survey scientists note that the representativeness of the sample may be more critical than response rate alone.17 Studies with small sample sizes may be more representative—and findings more valid—than those with large samples, if large samples are nonrepresentative when considering the target population.17
Do the conclusions go beyond the data?
Are the inferences overreaching, in view of the survey design? In studies with low response rates and nonrepresentative samples, researchers must be careful in interpreting the results. If the results cannot be generalized beyond the research sample, is this clear from the limitations, discussion, and conclusion sections?
In this review, we have summarized the findings of three published surveys1,2,30 and commented on how they appear to meet—or don’t quite meet—recommendations for survey development, validation, and use. The papers chosen were deemed strong examples in particular categories, such as description of survey authorship,1 instrument validation,30 sampling methodology,2 and response rate.1
It should be noted that even when surveys are conducted with the utmost rigor, survey reporting may leave out critical details. Survey methodology may not be adequately described for a variety of reasons, including researchers’ training in survey design and methodology; a lack of universally accepted journal-reporting guidelines3; and even journals’ space limitations. At times, journals may excise descriptions of survey development and validation, deeming these sections superfluous. Limitations sections can be critical to interpreting the results of survey research and evaluating the scope of conclusions.
- Jha AK, DesRoches CM, Campbell EG, et al. Use of electronic health records in US hospitals. N Engl J Med 2009; 360:1628–1638.
- Angus DC, Shorr AF, White A, Dremsizov TT, Schmitz RJ, Kelley MA; Committee on Manpower for Pulmonary and Critical Care Societies (COMPACCS). Critical care delivery in the United States: distribution of services and compliance with Leapfrog recommendations. Crit Care Med 2006; 34:1016–1024.
- Bennett C, Khangura S, Brehaut JC, et al. Reporting guidelines for survey research: an analysis of published guidance and reporting practices. PLoS Med 2010; 8:e1001069.
- Groves RM, Fowler FJ, Couper MP, Lepkowski JM, Singer E, Tourangeau R. Survey Methodology. 2nd ed. Hoboken, NJ: John Wiley and Sons, Inc; 2009.
- Duffett M, Burns KE, Adhikari NK, et al. Quality of reporting of surveys in critical care journals: a methodologic review. Crit Care Med 2012; 40:441–449.
- Mattell MS, Jacoby J. Is there an optimal number of alternatives for Likert-scale items? Effects of testing time and scale properties. J Appl Psychol 1972; 56:506–509.
- Willis GB. Cognitive Interviewing. A “How To” Guide. Research Triangle Institute. Presented at the meeting of the American Statistical Association; 1999. http://fog.its.uiowa.edu/~c07b209/interview.pdf. Accessed June 3, 2013.
- Schwarz N. Self-reports. How the questions shape the answers. Amer Psychol 1999; 54:93–105.
- Stone DH. Design a questionnaire. BMJ 1993; 307:1264–1266.
- Willis GB, Royston P, Bercini D. The use of verbal report methods in the development and testing of survey questionnaires. Appl Cogn Psychol 1991; 5:251–267.
- Desimone LM, LeFloch KC. Are we asking the right questions? Using cognitive interviews to improve surveys in education research. Educ Eval Policy Anal 2004; 26:1–22.
- Presser S, Couper MP, Lessler JT, et al. Methods for testing and evaluating survey questions. Public Opin Q 2004; 68:109–130.
- Rogers G. Accreditation Board for Engineering and Technology (ABET), Inc. Sample Protocol for Pilot Testing Survey Items. www.abet.org/WorkArea/DownloadAsset.aspx?id=1299. Accessed January 22, 2013.
- Schwarz N, Oyserman D. Asking questions about behavior: cognition, communication, and questionnaire construction. Am J Eval 2001; 22:127–160.
- Bradburn N, Sudman S, Wansink B. Asking Questions. The Definitive Guide to Questionnaire Design—For Market Research, Political Polls, and Social and Health Questionnaires. San Francisco, CA: Jossey-Bass; 2004.
- Stone AA, Broderick JE, Schwartz JE, Schwarz N. Context effects in survey ratings of health, symptoms, and satisfaction. Med Care 2008; 46:662–667.
- Cook C, Heath F, Thompson RL. A meta-analysis of response rates in Web or internet-based surveys. Educ Psychol Meas 2000; 60:821–836.
- Kaplowitz MD, Hadlock TD, Levine R. A comparison of Web and mail survey response rates. Public Opin Q 2004; 68:94–101.
- American Educational Research Association. Standards for Educational and Psychological Testing/American Educational Research Association, American Psychological Association, National Council on Measurement in Education. Washington, DC: American Educational Research Association; 1999.
- Burns KE, Duffett M, Kho ME, et al; ACCADEMY Group. A guide for the design and conduct of self-administered surveys of clinicians. CMAJ 2008; 179:245–252.
- American Association for Public Opinion Research (AAPOR). http://www.aapor.org/Home.htm. Accessed June 3, 2013.
- National Center for Education Statistics. Planning and Design of Surveys. http://nces.ed.gov/statprog/2002/std2_1.asp. Accessed January 22, 2013.
- Bordens KS, Abbott BB. Research Design and Methods. A Process Approach. 6th ed. New York, NY: McGraw-Hill; 2004.
- Sheehan K. Email survey response rates: a review. JCMC 2001. http://jcmc.indiana.edu/vol6/issue2/sheehan.html. Accessed January 22, 2013.
- Baruch Y, Holtom BC. Survey response rate levels and trends in organizational research. Hum Relat 2008; 61:1139–1160.
- Asch DA, Jedrziewski MK, Christakis NA. Response rates to mail surveys published in medical journals. J Clin Epidemiol 1997; 50:1129–1136.
- Cummings SM, Savitz LA, Konrad TR. Reported response rates to mailed physician questionnaires. Health Services Res 2001; 35:1347–1355.
- Field TS, Cadoret CA, Brown ML, et al. Surveying physicians. Do components of the “Total Design Approach” to optimizing survey response rates apply to physicians? Med Care 2002; 40:596–606.
- Converse PD, Wolfe EW, Huang X, Oswald FL. Response rates for mixed-mode surveys using mail and e-mail/Web. Am J Eval 2008; 29:99–107.
- Hirshberg E, Lacroix J, Sward K, Willson D, Morris AH. Blood glucose control in critically ill adults and children: a survey on stated practice. Chest 2008; 133:1328–1335.
- Jha AK, DesRoches CM, Campbell EG, et al. Use of electronic health records in US hospitals. N Engl J Med 2009; 360:1628–1638.
- Angus DC, Shorr AF, White A, Dremsizov TT, Schmitz RJ, Kelley MA; Committee on Manpower for Pulmonary and Critical Care Societies (COMPACCS). Critical care delivery in the United States: distribution of services and compliance with Leapfrog recommendations. Crit Care Med 2006; 34:1016–1024.
- Bennett C, Khangura S, Brehaut JC, et al. Reporting guidelines for survey research: an analysis of published guidance and reporting practices. PLoS Med 2010; 8:e1001069.
- Groves RM, Fowler FJ, Couper MP, Lepkowski JM, Singer E, Tourangeau R. Survey Methodology. 2nd ed. Hoboken, NJ: John Wiley and Sons, Inc; 2009.
- Duffett M, Burns KE, Adhikari NK, et al. Quality of reporting of surveys in critical care journals: a methodologic review. Crit Care Med 2012; 40:441–449.
- Mattell MS, Jacoby J. Is there an optimal number of alternatives for Likert-scale items? Effects of testing time and scale properties. J Appl Psychol 1972; 56:506–509.
- Willis GB. Cognitive Interviewing. A “How To” Guide. Research Triangle Institute. Presented at the meeting of the American Statistical Association; 1999. http://fog.its.uiowa.edu/~c07b209/interview.pdf. Accessed June 3, 2013.
- Schwarz N. Self-reports. How the questions shape the answers. Amer Psychol 1999; 54:93–105.
- Stone DH. Design a questionnaire. BMJ 1993; 307:1264–1266.
- Willis GB, Royston P, Bercini D. The use of verbal report methods in the development and testing of survey questionnaires. Appl Cogn Psychol 1991; 5:251–267.
- Desimone LM, LeFloch KC. Are we asking the right questions? Using cognitive interviews to improve surveys in education research. Educ Eval Policy Anal 2004; 26:1–22.
- Presser S, Couper MP, Lessler JT, et al. Methods for testing and evaluating survey questions. Public Opin Q 2004; 68:109–130.
- Rogers G. Accreditation Board for Engineering and Technology (ABET), Inc. Sample Protocol for Pilot Testing Survey Items. www.abet.org/WorkArea/DownloadAsset.aspx?id=1299. Accessed January 22, 2013.
- Schwarz N, Oyserman D. Asking questions about behavior: cognition, communication, and questionnaire construction. Am J Eval 2001; 22:127–160.
- Bradburn N, Sudman S, Wansink B. Asking Questions. The Definitive Guide to Questionnaire Design—For Market Research, Political Polls, and Social and Health Questionnaires. San Francisco, CA: Jossey-Bass; 2004.
- Stone AA, Broderick JE, Schwartz JE, Schwarz N. Context effects in survey ratings of health, symptoms, and satisfaction. Med Care 2008; 46:662–667.
- Cook C, Heath F, Thompson RL. A meta-analysis of response rates in Web or internet-based surveys. Educ Psychol Meas 2000; 60:821–836.
- Kaplowitz MD, Hadlock TD, Levine R. A comparison of Web and mail survey response rates. Public Opin Q 2004; 68:94–101.
- American Educational Research Association. Standards for Educational and Psychological Testing/American Educational Research Association, American Psychological Association, National Council on Measurement in Education. Washington, DC: American Educational Research Association; 1999.
- Burns KE, Duffett M, Kho ME, et al; ACCADEMY Group. A guide for the design and conduct of self-administered surveys of clinicians. CMAJ 2008; 179:245–252.
- American Association for Public Opinion Research (AAPOR). http://www.aapor.org/Home.htm. Accessed June 3, 2013.
- National Center for Education Statistics. Planning and Design of Surveys. http://nces.ed.gov/statprog/2002/std2_1.asp. Accessed January 22, 2013.
- Bordens KS, Abbott BB. Research Design and Methods. A Process Approach. 6th ed. New York, NY: McGraw-Hill; 2004.
- Sheehan K. Email survey response rates: a review. JCMC 2001. http://jcmc.indiana.edu/vol6/issue2/sheehan.html. Accessed January 22, 2013.
- Baruch Y, Holtom BC. Survey response rate levels and trends in organizational research. Hum Relat 2008; 61:1139–1160.
- Asch DA, Jedrziewski MK, Christakis NA. Response rates to mail surveys published in medical journals. J Clin Epidemiol 1997; 50:1129–1136.
- Cummings SM, Savitz LA, Konrad TR. Reported response rates to mailed physician questionnaires. Health Services Res 2001; 35:1347–1355.
- Field TS, Cadoret CA, Brown ML, et al. Surveying physicians. Do components of the “Total Design Approach” to optimizing survey response rates apply to physicians? Med Care 2002; 40:596–606.
- Converse PD, Wolfe EW, Huang X, Oswald FL. Response rates for mixed-mode surveys using mail and e-mail/Web. Am J Eval 2008; 29:99–107.
- Hirshberg E, Lacroix J, Sward K, Willson D, Morris AH. Blood glucose control in critically ill adults and children: a survey on stated practice. Chest 2008; 133:1328–1335.
KEY POINTS
- Most survey reports do not adequately describe their methods.
- Surveys that rely on participants’ self-reports of behaviors, attitudes, beliefs, or actions are indirect measures and are susceptible to self-report and social-desirability biases.
- Informed readers need to consider a survey’s authorship, objective, validation, items, response choices, sampling representativeness, response rate, generalizability, and scope of the conclusions.