Association of Self-reported Primary Care Physician Tolerance for Uncertainty With Variations in Resource Use and Patient Experience.

Importance: Inappropriate variations in clinical practice are a known cause of poor quality and safety, with variations often associated with nonclinical factors, such as individual differences in cognitive processing. The differential response of physicians to uncertainty may explain some of the variations in resource use and patient experience. Objective: To examine the association of physician tolerance for uncertainty with variations in resource use and patient experience. Design, Setting, and Participants: This survey study linked physician survey data (May to June 2019), patient experience survey data (January 2016 to December 2019), and billing data (January 2019 to December 2019) among primary care physicians (PCPs) at Massachusetts General Hospital with at least 10 visits in 2019. The statistical analysis was performed in 2021. Main Outcomes and Measures: The analysis examined associations of PCP tolerance for uncertainty with the tendency to order diagnostic tests, the frequency of outpatient visits, hospital admissions, emergency department visits, and patient experience data (focused on physician communication and overall rating). A 2-stage hierarchical framework was used to account for clustering of patients under PCPs. Binary outcomes were modeled using a hierarchical logistic model, and count outcomes were modeled using hierarchical Poisson or negative binomial models. The analysis was adjusted for patient demographic variables (age, sex, and race and ethnicity), socioeconomic factors (payer and neighborhood income), and clinical comorbidities. Results: Of 217 included physicians, 137 (63.1%) were women, and 174 (80.2%) were adult PCPs. A total of 62 physicians (28.6%) reported low tolerance, 59 (27.2%) reported medium tolerance, and 96 (44.2%) reported high tolerance for uncertainty. Physicians with a low tolerance for uncertainty were less likely to order complete blood cell counts (odds ratio [OR], 0.66; 95% CI, 0.50-0.88), thyroid tests (OR, 0.67; 95% CI, 0.52-0.88), a basic metabolic profile (OR, 0.78; 95% CI, 0.60-1.00), and liver function tests (OR, 0.72; 95% CI, 0.53-0.99) than physicians with a high tolerance for uncertainty. Physicians who reported higher tolerance for uncertainty were more likely to receive higher patient experience scores for listening to patients carefully (OR, 0.65; 95% CI, 0.50-0.83) and higher overall ratings (OR, 0.80; 95% CI, 0.66-0.98) than physicians with medium tolerance. Conversely, no association was found between physician tolerance for uncertainty and patient outpatient visits, hospital admissions, or emergency department visits. Conclusions and Relevance: In clinical practice, identifying and effectively managing inappropriate variations and improving patient experience have proven to be difficult, despite increased attention to these issues. This study supports the hypothesis that physicians' tolerance for uncertainty is associated with differences in resource use and patient experience. Whether enhancing physicians' tolerance for uncertainty could help reduce unwarranted practice variations, improve quality and patient safety, and improve patient's experience remains to be established.

Comparing neighborhood-based indices of socioeconomic risk factors and potentially preventable emergency department utilization.

BACKGROUND: Neighborhood stress score (NSS) and area deprivation index (ADI) are two neighborhood-based composite measures used to quantify an individual's socioeconomic risk based on home location. In this analysis, we compare the relationships between an individual's socioeconomic risk, based on each of these measures, and potentially preventable acute care utilization. METHODS: Using emergency department (ED) visit data from two academic medical centers in Boston, Massachusetts, we conducted adjusted Poisson regressions of ADI decile and NSS decile with counts of low acuity ED visits, admissions for ambulatory care sensitive conditions (ACSCs), and patients with high frequency ED utilization at the census block group (CBG) level within the greater Boston area. RESULTS: Both NSS and ADI decile were associated with elevated rates of utilization, although the associated incidence rate ratios (IRRs) for NSS were higher than those for ADI across all three measures. NSS decile was associated with IRRs of 1.11 [95% CI: 1.10-1.12], 1.16 [1.14-1.17], and 1.22 [1.19-1.25] for ACSC admissions, low acuity ED visits, and patients with high frequency ED utilization, respectively; compared with 1.04 [1.04-1.05], 1.11 [1.10-1.11], and 1.10 [1.08-1.12] for ADI decile. CONCLUSION: ADI and NSS both represent effective tools to assess the potential impact of geographically-linked socioeconomic drivers of health on potentially preventable acute care utilization. NSS decile was associated with a greater effect size for each measure of utilization suggesting that this may be a stronger predictor, however, additional research is necessary to evaluate these findings in other contexts.

Predictors of MRI Leakage Among Patients Attributed to an Academic Medical Center Commercial Risk-Shared Insurance Contract.

PURPOSE: For health care organizations engaged in risk-shared insurance contracts, leakage of advanced diagnostic imaging to imaging sites not affiliated with the risk-sharing organization may undermine performance on financial and quality metrics. The goal of this study was to identify factors that are predictive of leakage of MRI examinations among patients attributed to an academic health care organization's risk-shared commercial insurance contract. METHODS: Administrative claims data from 2015 through 2016 for patients attributed to a single risk-shared commercial insurance contract at a large academic medical center (AMC) were analyzed. Primary outcome was MRI leakage: an outpatient MRI study performed at a site not affiliated with the AMC's integrated health care system. Ordering provider alignment with the AMC's risk-shared insurance contract was categorized as strong, weak, or none. Multivariate regression analyses were conducted to evaluate the relationship between provider alignment and MRI leakage, while adjusting for selected covariates. RESULTS: Among 8,215 patients meeting inclusion criteria, there were 13,272 MRI encounters. The overall proportion of leaked MRI studies was 12.7%. MRI studies ordered by providers with weak AMC alignment (odds ratio, 3.16; 95% confidence interval, 2.49-4.02) or no AMC alignment (odds ratio, 3.68; 95% confidence interval, 3.12-4.33) were more likely to leak than MRI studies ordered by providers with strong AMC alignment. CONCLUSIONS: An ordering provider with no alignment with an AMC's commercial risk-shared insurance contract was the strongest predictor of MRI leakage. Population health management initiatives aimed at reducing leakage should consider the impact of provider networks and clinical referral patterns that drive imaging utilization.

Development of an electronic health record-based chronic kidney disease registry to promote population health management.

BACKGROUND: Electronic health record (EHR) based chronic kidney disease (CKD) registries are central to population health strategies to improve CKD care. In 2015, Partners Healthcare System (PHS), encompassing multiple academic and community hospitals and outpatient care facilities in Massachusetts, developed an EHR-based CKD registry to identify opportunities for quality improvement, defined as improvement on both process measures and outcomes measures associated with clinical care. METHODS: Patients are included in the registry based on the following criteria: 1) two estimated glomerular filtration rate (eGFR) results < 60 ml/min/1.73m2 separated by 90 days, including the most recent eGFR being < 60 ml/min/1.73m2; or 2) the most recent two urine protein values > 300 mg protein/g creatinine on either urine total protein/creatinine ratio or urine albumin/creatinine ratio; or 3) an EHR problem list diagnosis of end stage renal disease (ESRD). The registry categorizes patients by CKD stage and includes rates of annual testing for eGFR and proteinuria, blood pressure control, use of angiotensin converting enzyme inhibitors (ACE-Is) or angiotensin receptor blockers (ARBs), nephrotoxic medication use, hepatitis B virus (HBV) immunization, vascular access placement, transplant status, CKD progression risk; number of outpatient nephrology visits, and hospitalizations. RESULTS: The CKD registry includes 60,503 patients and has revealed several opportunities for care improvement including 1) annual proteinuria testing performed for 17% (stage 3) and 31% (stage 4) of patients; 2) ACE-I/ARB used in 41% (stage 3) and 46% (stage 4) of patients; 3) nephrotoxic medications used among 23% of stage 4 patients; and 4) 89% of stage 4 patients lack HBV immunity. For advanced CKD patients there are opportunities to improve vascular access placement, transplant referrals and outpatient nephrology contact. CONCLUSIONS: A CKD registry can identify modifiable care gaps across the spectrum of CKD care and enable population health strategy implementation. No linkage to Social Security Death Master File or US Renal Data System (USRDS) databases limits our ability to track mortality and progression to ESRD.

Measuring individual physician clinical productivity in an era of consolidated group practices.

BACKGROUND: As physician groups consolidate and value-based payment replaces traditional fee-for-service systems, physician practices have greater need to accurately measure individual physician clinical productivity within team-based systems. We compared methodologies to measure individual physician outpatient clinical productivity after adjustment for shared practice resources. METHODS: For cardiologists at our hospital between January 2015 and June 2016, we assessed productivity by examining completed patient visits per clinical session per week. Using mixed-effects models, we sequentially accounted for shared practice resources and underlying baseline characteristics. We compared mixed-effects and Generalized Estimating Equations (GEE) models using K-fold cross validation, and compared mixed-effect, GEE, and Data Envelopment Analysis (DEA) models based on ranking of physicians by productivity. RESULTS: A mixed-effects model adjusting for shared practice resources reduced variation in productivity among providers by 63% compared to an unadjusted model. Mixed-effects productivity rankings correlated strongly with GEE rankings (Spearman 0.99), but outperformed GEE on K-fold cross validation (root mean squared error 2.66 vs 3.02; mean absolute error 1.89 vs 2.20, respectively). Mixed-effects model rankings had moderate correlation with DEA model rankings (Spearman 0.692), though this improved upon exclusion of outliers (Spearman 0.755). CONCLUSIONS: Mixed-effects modeling accounts for significant variation in productivity secondary to shared practice resources, outperforms GEE in predictive power, and is less vulnerable to outliers than DEA. IMPLICATIONS: With mixed-effects regression analysis using otherwise easily accessible administrative data, practices can evaluate physician clinical productivity more fairly and make more informed management decisions on physician compensation and resource allocation.

Behavioral Health Factors as Predictors of Emergency Department Use in the High-Risk, High-Cost Medicare Population.

OBJECTIVE: This study measured the presence, extent, and type of behavioral health factors in a high-cost Medicare population and their association with the probability and intensity of emergency department (ED) use. METHODS: Retrospective claims analysis and a comprehensive electronic medical record-based review were conducted for patients enrolled in a 65-month prospective care management program at an academic tertiary medical center (N=3,620). A two-part model used multivariable logistic regression to evaluate the effect of behavioral health factors on the probability of ED use, complemented by a Poisson model to measure the number of ED visits. Control variables included demographic characteristics, poststudy survival, and hierarchical condition category risk score. RESULTS: After analyses controlled for comorbidities and other relevant variables, patients with two or more behavioral health diagnosis categories or two or more behavioral health medications were about twice as likely as those without such categories or medications to use the ED. Patients with a diagnosis category of psychosis, neuropsychiatric disorders, sleep disorders, or adjustment disorders were significantly more likely than those without these disorders to use the ED. Most primary ED diagnoses were not of behavioral health conditions. CONCLUSIONS: Behavioral health factors had a substantial and significant effect on the likelihood and number of ED visits in a population of high-cost Medicare patients. Attention to behavioral health factors as independent predictors of ED use may be useful in influencing ED use in high-cost populations.

Variation in the Echocardiographic Surveillance of Primary Mitral Regurgitation.

BACKGROUND: Clinical outcomes after surgical treatment of mitral regurgitation are worse if intervention occurs after deterioration of left ventricular size and function. Transthoracic echocardiographic (TTE) surveillance of patients with mitral regurgitation is indicated to avoid adverse ventricular remodeling. Overly frequent TTEs can impair patient access and reduce value in care delivery. This balance between timely surveillance and overutilization of TTE in valvular disease provides a model to study variation in the delivery of healthcare services. We investigated patient and provider factors contributing to variation in TTE utilization and hypothesized that variation was attributable to provider practice even after adjustment for patient characteristics. METHODS AND RESULTS: We obtained records of all TTEs from 2001 to 2016 completed at a large echocardiography laboratory. The outcome variable was time interval between TTEs. We constructed a mixed-effects linear regression model with the individual physician as the random effect in the model and used intraclass correlation coefficient to assess the proportion of outcome variation because of provider practice. Our study cohort was 55 773 TTEs corresponding to 37 843 intervals ordered by 635 providers. The mean interval between TTEs was 12.4 months, 17.0 months, 18.3 months, and 17.4 months for severe, moderate, mild, and trace mitral regurgitation, respectively, with 20% of providers deemed overutilizers of TTEs and 25% underutilizers. CONCLUSIONS: We conclude that there is substantial variation in follow-up intervals for TTE assessment of mitral regurgitation, despite risk-adjustment for patient variables, likely because of provider factors.

Utilization Management of High-Cost Imaging in an Outpatient Setting in a Large Stable Patient and Provider Cohort over 7 Years.

Purpose To quantify the effect of a comprehensive, long-term, provider-led utilization management (UM) program on high-cost imaging (computed tomography, magnetic resonance imaging, nuclear imaging, and positron emission tomography) performed on an outpatient basis. Materials and Methods This retrospective, 7-year cohort study included all patients regularly seen by primary care physicians (PCPs) at an urban academic medical center. The main outcome was the number of outpatient high-cost imaging examinations per patient per year ordered by the patient's PCP or by any specialist. The authors determined the probability of a patient undergoing any high-cost imaging procedure during a study year and the number of examinations per patient per year (intensity) in patients who underwent high-cost imaging. Risk-adjusted hierarchical models were used to directly quantify the physician component of variation in probability and intensity of high-cost imaging use, and clinicians were provided with regular comparative feedback on the basis of the results. Observed trends in high-cost imaging use and provider variation were compared with the same measures for outpatient laboratory studies because laboratory use was not subject to UM during this period. Finally, per-member per-year high-cost imaging use data were compared with statewide high-cost imaging use data from a major private payer on the basis of the same claim set. Results The patient cohort steadily increased in size from 88 959 in 2007 to 109 823 in 2013. Overall high-cost imaging utilization went from 0.43 examinations per year in 2007 to 0.34 examinations per year in 2013, a decrease of 21.33% (P < .0001). At the same time, similarly adjusted routine laboratory study utilization decreased by less than half that rate (9.4%, P < .0001). On the basis of unadjusted data, outpatient high-cost imaging utilization in this cohort decreased 28%, compared with a 20% decrease in statewide utilization (P = .0023). Conclusion Analysis of high-cost imaging utilization in a stable cohort of patients cared for by PCPs during a 7-year period showed that comprehensive UM can produce a significant and sustained reduction in risk-adjusted per-patient year outpatient high-cost imaging volume. © RSNA, 2017.

Images of imaging: how to process and display imaging utilization for large populations.

OBJECTIVE: We propose a method of processing and displaying imaging utilization data for large populations. CONCLUSION: The comprehensive and finely grained picture of imaging utilization yielded by our methods is a first step toward population-based imaging utilization management. We believe that our methods for the categorization and display of imaging utilization will prove to be widely useful.

Provider Feedback about Imaging Appropriateness by Using Scores from Order Entry Decision Support: Raw Rates Misclassify Outliers.

PURPOSE: To determine the relevant physician- and practice-related factors that jointly affect the rate of low-utility imaging examinations (score of 1-3 out of 9) ordered by means of an order entry system that provides normative appropriateness feedback. MATERIALS AND METHODS: This HIPAA-compliant study was approved by the institutional review board under an expedited protocol for analyzing anonymous aggregated administrative data. This is a retrospective study of approximately 250 000 consecutive scheduled outpatient advanced imaging examinations (computed tomography, magnetic resonance imaging, nuclear medicine) ordered by 164 primary care and 379 medical specialty physicians from 2008 to 2012. A hierarchical logistic regression model was used to identify multiple predictors of the probability that an examination received a low utility score. Physician- and practice-specific random effects were estimated to articulate (odds ratio) and quantify (intraclass correlation) interphysician variation. RESULTS: Fixed effects found to be statistically significant predictors of low-utility imaging included examination type, whether the examination was cancelled, status of the person entering the order, and the total number of examinations ordered by the clinician. Neither patient age nor sex had any effect, and there were no secular trends (year of study). The remaining amount of interphysician variation was moderate (intraclass correlation, 22%), whereas the variation between medical specialties and primary care practices was low (intraclass correlation, 5%). The estimated physician-specific effects had reliability of 70%, which makes them just suitable for identifying outliers. CONCLUSION: The authors found that 22% of the variation in the rate of low-utility examinations is attributable to ordering providers and 5% to their specialty or clinic.

An electronic health records study of long-term weight gain following antidepressant use.

IMPORTANCE: Short-term studies suggest antidepressants are associated with modest weight gain but little is known about longer-term effects and differences between individual medications in general clinical populations. OBJECTIVE: To estimate weight gain associated with specific antidepressants over the 12 months following initial prescription in a large and diverse clinical population. DESIGN, SETTING, AND PARTICIPANTS: We identified 22,610 adult patients who began receiving a medication of interest with available weight data in a large New England health care system, including 2 academic medical centers and affiliated outpatient primary and specialty care clinics. We used electronic health records to extract prescribing data and recorded weights for any patient with an index antidepressant prescription including amitriptyline hydrochloride, bupropion hydrochloride, citalopram hydrobromide, duloxetine hydrochloride, escitalopram oxalate, fluoxetine hydrochloride, mirtazapine, nortriptyline hydrochloride, paroxetine hydrochloride, venlafaxine hydrochloride, and sertraline hydrochloride. As measures of assay sensitivity, additional index prescriptions examined included the antiasthma medication albuterol sulfate and the antiobesity medications orlistat, phentermine hydrochloride, and sibutramine hydrochloride. Mixed-effects models were used to estimate rate of weight change over 12 months in comparison with the reference antidepressant, citalopram. MAIN OUTCOME AND MEASURE: Clinician-recorded weight at 3-month intervals up to 12 months. RESULTS: Compared with citalopram, in models adjusted for sociodemographic and clinical features, significantly decreased rate of weight gain was observed among individuals treated with bupropion (ß [SE]: -0.063 [0.027]; P = .02), amitriptyline (ß [SE]: -0.081 [0.025]; P = .001), and nortriptyline (ß [SE]: -0.147 [0.034]; P < .001). As anticipated, differences were less pronounced among individuals discontinuing treatment prior to 12 months. CONCLUSIONS AND RELEVANCE: Antidepressants differ modestly in their propensity to contribute to weight gain. Short-term investigations may be insufficient to characterize and differentiate this risk.

Use of imaging in the emergency department: physicians have limited effect on variation.

PURPOSE: To quantify interphysician variation in imaging use during emergency department (ED) visits and examine the contribution of factors to this variation at the patient, visit, and physician level. MATERIALS AND METHODS: This study was HIPAA compliant and approved by the institutional review board of Partners Healthcare System (Boston, Mass), with waiver of informed consent. In this retrospective study of 88 851 consecutive ED visits during 2011 at a large urban teaching hospital, a hierarchical logistic regression model was used to identify multiple predictors for the probability that low- or high-cost imaging would be ordered during a given visit. Physician-specific random effects were estimated to articulate (by odds ratio) and quantify (by intraclass correlation coefficient [ICC]) interphysician variation. RESULTS: Patient- and visit-level factors found to be statistically significant predictors of imaging use included measures of ED busyness, prior ED visit, referral source to the ED, and ED arrival mode. Physician-level factors (eg, sex, years since graduation, annual workload, and residency training) did not correlate with imaging use. The remaining amount of interphysician variation was very low (ICC, 0.97% for low-cost imaging; ICC, 1.07% for high-cost imaging). These physician-specific odds ratios of imaging estimates were moderately reliable at 0.78 (95% confidence interval [CI]: 0.77, 0.79) for low-cost imaging and 0.76 (95% CI: 0.74, 0.78) for high-cost imaging. CONCLUSION: After careful and comprehensive case-mix adjustment by using hierarchical logistic regression, only about 1% of the variability in ED imaging utilization was attributable to physicians.

QT interval and antidepressant use: a cross sectional study of electronic health records.

OBJECTIVE: To quantify the impact of citalopram and other selective serotonin reuptake inhibitors on corrected QT interval (QTc), a marker of risk for ventricular arrhythmia, in a large and diverse clinical population. DESIGN: A cross sectional study using electrocardiographic, prescribing, and clinical data from electronic health records to explore the relation between antidepressant dose and QTc. Methadone, an opioid known to prolong QT, was included to demonstrate assay sensitivity. SETTING: A large New England healthcare system comprising two academic medical centres and outpatient clinics. PARTICIPANTS: 38,397 adult patients with an electrocardiogram recorded after prescription of antidepressant or methadone between February 1990 and August 2011. MAIN OUTCOME MEASURES: Relation between antidepressant dose and QTc interval in linear regression, adjusting for potential clinical and demographic confounding variables. For a subset of patients, change in QTc after drug dose was also examined. RESULTS: Dose-response association with QTc prolongation was identified for citalopram (adjusted beta 0.10 (SE 0.04), P<0.01), escitalopram (adjusted beta 0.58 (0.15), P<0.001), and amitriptyline (adjusted beta 0.11 (0.03), P<0.001), but not for other antidepressants examined. An association with QTc shortening was identified for bupropion (adjusted beta 0.02 (0.01) P<0.05). Within-subject paired observations supported the QTc prolonging effect of citalopram (10 mg to 20 mg, mean QTc increase 7.8 (SE 3.6) ms, adjusted P<0.05; and 20 mg to 40 mg, mean QTc increase 10.3 (4.0) ms, adjusted P<0.01). CONCLUSIONS: This study confirmed a modest prolongation of QT interval with citalopram, and identified additional antidepressants with similar observed risk. Pharmacovigilance studies using electronic health record data may be a useful method of identifying potential risk associated with treatments.

Antidepressant response in patients with major depression exposed to NSAIDs: a pharmacovigilance study.

OBJECTIVE It has been suggested that there is a mechanism by which nonsteroidal anti-inflammatory drugs (NSAIDs) may interfere with antidepressant response, and poorer outcomes among NSAID-treated patients were reported in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study. To attempt to confirm this association in an independent population-based treatment cohort and explore potential confounding variables, the authors examined use of NSAIDs and related medications among 1,528 outpatients in a New England health care system. METHOD Treatment outcomes were classified using a validated machine learning tool applied to electronic medical records. Logistic regression was used to examine the association between medication exposure and treatment outcomes, adjusted for potential confounding variables. To further elucidate confounding and treatment specificity of the observed effects, data from the STAR*D study were reanalyzed. RESULTS NSAID exposure was associated with a greater likelihood of depression classified as treatment resistant compared with depression classified as responsive to selective serotonin reuptake inhibitors (odds ratio=1.55, 95% CI=1.21-2.00). This association was apparent in the NSAIDs-only group but not in those using other agents with NSAID-like mechanisms (cyclooxygenase-2 inhibitors and salicylates). Inclusion of age, sex, ethnicity, and measures of comorbidity and health care utilization in regression models indicated confounding; association with outcome was no longer significant in fully adjusted models. Reanalysis of STAR*D results likewise identified an association in NSAIDs but not NSAID-like drugs, with more modest effects persisting after adjustment for potential confounding variables. CONCLUSIONS These results support an association between NSAID use and poorer antidepressant outcomes in major depressive disorder but indicate that some of the observed effect may be a result of confounding.

Determinants of diagnostic imaging utilization in primary care.

OBJECTIVES: To examine patient and physician factors affecting utilization of diagnostic imaging in primary care. DATA SOURCES/STUDY SETTING: Patient-level data from a large academic group practice over the period July 1, 2007, through June 30, 2009. STUDY DESIGN: This is a retrospective cohort study of 85,277 patients cared for by 148 primary care physicians (PCPs). The dependent variable is the number of outpatient imaging exams ordered by each patient's PCP over the study period. Independent variables include 17 patient factors describing both clinical need and demographic characteristics and 7 physician factors. DATA COLLECTION: Data were collected from the electronic medical record and associated administrative databases. PRINCIPAL FINDINGS: Patient factors having a statistically significant effect on both the probability race, more than 10 medications, congestive heart failure, diabetes, hypertension, other problems, visits to the PCP, visits to specialists, and imaging exams ordered by specialists. For physician factors, experience, gender, and having another degree were statistically significant in both portions of the model. CONCLUSIONS: Both patient and physician factors have a substantial effect on primary care outpatient diagnostic imaging utilization. Several of these significantly influence both the probability that any images will be ordered and the intensity (number) of imaging.

Incident user cohort study of risk for gastrointestinal bleed and stroke in individuals with major depressive disorder treated with antidepressants.

OBJECTIVE: To examine the association between exposure to newer antidepressants and risk of gastrointestinal (GI) and other bleeding complications among individuals with major depressive disorder (MDD). DESIGN: This study uses an incident user cohort design to compare associations between incidence of vascular/bleeding events and the relative affinity (low, moderate or high) of the antidepressant for the serotonin transporter during an exposure risk period for each patient. SETTING: New England healthcare system electronic medical record database. PARTICIPANTS: 36 389 individuals with a diagnosis of MDD and monotherapy with a selective serotonin reuptake inhibitor, serotonin-norepinephrine reuptake inhibitor or other new-generation antidepressant were identified from among 3.1 million patients in a New England healthcare system. PRIMARY AND SECONDARY OUTCOME MEASURES: Rates of bleeding or other vascular complications, including acute liver failure, acute renal failure, asthma, breast cancer and hip fractures. RESULTS: 601 GI bleeds were observed in the 21 462 subjects in the high-affinity group versus 333 among the 14 927 subjects in the lower affinity group (adjusted RR: 1.17, 95% CI 1.02 to 1.34). Similarly, 776 strokes were observed in the high-affinity group versus 434 in the lower affinity treatment group (adjusted RR: 1.18, 95% CI 1.06 to 1.32). No significant association with risk for a priori negative control outcomes, including acute liver failure, acute renal failure, asthma, breast cancer and hip fractures, was identified. CONCLUSIONS: Use of antidepressants with high affinity for the serotonin transporter may confer modestly elevated risk for GI and other bleeding complications. While multiple methodologic limitations must be considered, these results suggest that antidepressants with lower serotonin receptor affinity may be preferred in patients at greater risk for such complications.