Person:

Schneeweiss, Sebastian

BACKGROUND: Methods for near-real-time monitoring of new drugs in electronic healthcare data are needed. OBJECTIVE: In a novel application, we prospectively monitored ischemic, bleeding, and mortality outcomes among patients initiating prasugrel versus clopidogrel in routine care during the first 2 years following the approval of prasugrel. METHODS: Using the HealthCore Integrated Research Database, we conducted a prospective cohort study comparing prasugrel and clopidogrel initiators in the 6 months following the introduction of prasugrel and every 2 months thereafter. We identified patients who initiated antiplatelets within 14 days following discharge from hospitalizations for myocardial infarction (MI) or acute coronary syndrome. We matched patients using high-dimensional propensity scores (hd-PSs) and followed them for ischemic (i.e., MI and ischemic stroke) events, bleed (i.e., hemorrhagic stroke and gastrointestinal bleed) events, and all-cause mortality. For each outcome, we applied sequential alerting algorithms. RESULTS: We identified 1,282 eligible new users of prasugrel and 8,263 eligible new users of clopidogrel between September 2009 and August 2011. In hd-PS matched cohorts, the overall MI rate difference (RD) comparing prasugrel with clopidogrel was -23.1 (95 % confidence interval [CI] -62.8-16.7) events per 1,000 person-years and RDs were -0.5 (-12.9-11.9) and -2.8 (-13.2-7.6) for a composite bleed event outcome and death from any cause, respectively. No algorithms generated alerts for any outcomes. CONCLUSIONS: Near-real-time monitoring was feasible and, in contrast to the key pre-marketing trial that demonstrated the efficacy of prasugrel, did not suggest that prasugrel compared with clopidogrel was associated with an increased risk of gastrointestinal and intracranial bleeding.

Objective: To examine the relation between the type of stress ulcer prophylaxis administered and the risk of postoperative pneumonia in patients undergoing coronary artery bypass grafting. Design: Retrospective cohort study. Setting: Premier Research Database. Participants:: 21 214 patients undergoing coronary artery bypass graft surgery between 2004 and 2010; 9830 (46.3%) started proton pump inhibitors and 11 384 (53.7%) started H2 receptor antagonists in the immediate postoperative period. Main outcome measure Occurrence of postoperative pneumonia, assessed using appropriate diagnostic codes. Results: Overall, 492 (5.0%) of the 9830 patients receiving a proton pump inhibitor and 487 (4.3%) of the 11 384 patients receiving an H2 receptor antagonist developed postoperative pneumonia during the index hospital admission. After propensity score adjustment, an elevated risk of pneumonia associated with treatment with proton pump inhibitors compared with H2 receptor antagonists remained (relative risk 1.19, 95% confidence interval 1.03 to 1.38). In the instrumental variable analysis, use of a proton pump inhibitor (compared with an H2 receptor antagonist) was associated with an increased risk of pneumonia of 8.2 (95% confidence interval 0.5 to 15.9) cases per 1000 patients. Conclusions: Patients treated with proton pump inhibitors for stress ulcer had a small increase in the risk of postoperative pneumonia compared with patients treated with H2 receptor antagonists; this risk remained after confounding was accounted for using multiple analytic approaches.

We developed a semi-automated active monitoring system that uses sequential matched-cohort analyses to assess drug safety across a distributed network of longitudinal electronic healthcare data. In a retrospective analysis, we showed that the system would have identified cerivastatin-induced rhabdomyolysis. In this study, we evaluated whether the system would generate alerts for three drug-outcome pairs: rosuvastatin and rhabdomyolysis (known null association), rosuvastatin and diabetes mellitus, and telithromycin and hepatotoxicity (two examples for which alerting would be questionable). During >5 years of monitoring, rate differences (RDs) comparing rosuvastatin to atorvastatin were -0.1 cases of rhabdomyolysis per 1,000 person-years (95% CI, -0.4, 0.1) and -2.2 diabetes cases per 1,000 person-years (95% CI, -6.0, 1.6). The RD for hepatotoxicity comparing telithromycin to azithromycin was 0.3 cases per 1,000 person-years (95% CI, -0.5, 1.0). In a setting in which false positivity is a major concern, the system did not generate alerts for three drug-outcome pairs.

Background: Multivariable confounder adjustment in comparative studies of newly marketed drugs can be limited by small numbers of exposed patients and even fewer outcomes. Disease risk scores (DRSs) developed in historical comparator drug users before the new drug entered the market may improve adjustment. However, in a high dimensional data setting, empirical selection of hundreds of potential confounders and modeling of DRS even in the historical cohort can lead to over-fitting and reduced predictive performance in the study cohort. We propose the use of combinations of dimension reduction and shrinkage methods to overcome this problem, and compared the performances of these modeling strategies for implementing high dimensional (hd) DRSs from historical data in two empirical study examples of newly marketed drugs versus comparator drugs after the new drugs’ market entry—dabigatran versus warfarin for the outcome of major hemorrhagic events and cyclooxygenase-2 inhibitor (coxibs) versus nonselective non-steroidal anti-inflammatory drugs (nsNSAIDs) for gastrointestinal bleeds. Results: Historical hdDRSs that included predefined and empirical outcome predictors with dimension reduction (principal component analysis; PCA) and shrinkage (lasso and ridge regression) approaches had higher c-statistics (0.66 for the PCA model, 0.64 for the PCA + ridge and 0.65 for the PCA + lasso models in the warfarin users) than an unreduced model (c-statistic, 0.54) in the dabigatran example. The odds ratio (OR) from PCA + lasso hdDRS-stratification [OR, 0.64; 95 % confidence interval (CI) 0.46–0.90] was closer to the benchmark estimate (0.93) from a randomized trial than the model without empirical predictors (OR, 0.58; 95 % CI 0.41–0.81). In the coxibs example, c-statistics of the hdDRSs in the nsNSAID initiators were 0.66 for the PCA model, 0.67 for the PCA + ridge model, and 0.67 for the PCA + lasso model; these were higher than for the unreduced model (c-statistic, 0.45), and comparable to the demographics + risk score model (c-statistic, 0.67). Conclusions: hdDRSs using historical data with dimension reduction and shrinkage was feasible, and improved confounding adjustment in two studies of newly marketed medications. Electronic supplementary material The online version of this article (doi:10.1186/s12982-016-0047-x) contains supplementary material, which is available to authorized users.

INTRODUCTION: Several small studies have reported inconsistent findings about the safety of selective serotonin reuptake inhibitors (SSRIs) among patients undergoing coronary artery bypass grafting (CABG). We sought to investigate post-CABG bleeding and mortality outcomes related to antidepressant exposure. METHODS: We identified patients who underwent CABG between 2004 and 2008 in the Premier Perspective Comparative Database. We determined whether they received SSRIs, other antidepressants, or no antidepressants on any pre-CABG hospital day and used Cox proportional hazards models to compare bleeding and mortality rates among the exposure groups while adjusting for potential confounders based on administrative data, pre-CABG charge codes, and discharge diagnosis codes. RESULTS: We identified 132,686 eligible patients: 7112 exposed to SSRIs, 1905 exposed to other antidepressants, and 123,668 unexposed. As compared with no exposure, neither SSRIs (hazard ratio [HR] 0.98; 95 % confidence interval [CI] 0.90-1.07) nor other antidepressants (HR 1.11; 95 % CI 0.96-1.28) increased major bleeds, and neither SSRIs (HR 0.93; 95 % CI 0.80-1.07) nor other antidepressants (HR 0.84; 95 % CI 0.62-1.14) increased mortality. Both SSRIs (HR 1.14; 95 % CI 1.10-1.18) and other antidepressants (HR 1.11; 95 % CI 1.03-1.19) were associated with a slight increase in receipt of one or more packed red blood cell (pRBC) units, but neither were associated with substantial increases in receipt of three or more pRBC units (HR 1.06; 95 % CI 0.96-1.17 for SSRIs; HR 1.09; 95 % CI 0.91-1.31 for other antidepressants). CONCLUSION: In this large cohort study, neither SSRIs nor other antidepressants were associated with elevated rates of major bleed, or in-hospital mortality.

Abstract Purpose Defining a study population and creating an analytic dataset from longitudinal healthcare databases involves many decisions. Our objective was to catalogue scientific decisions underpinning study execution that should be reported to facilitate replication and enable assessment of validity of studies conducted in large healthcare databases. Methods: We reviewed key investigator decisions required to operate a sample of macros and software tools designed to create and analyze analytic cohorts from longitudinal streams of healthcare data. A panel of academic, regulatory, and industry experts in healthcare database analytics discussed and added to this list. Conclusion: Evidence generated from large healthcare encounter and reimbursement databases is increasingly being sought by decision‐makers. Varied terminology is used around the world for the same concepts. Agreeing on terminology and which parameters from a large catalogue are the most essential to report for replicable research would improve transparency and facilitate assessment of validity. At a minimum, reporting for a database study should provide clarity regarding operational definitions for key temporal anchors and their relation to each other when creating the analytic dataset, accompanied by an attrition table and a design diagram. A substantial improvement in reproducibility, rigor and confidence in real world evidence generated from healthcare databases could be achieved with greater transparency about operational study parameters used to create analytic datasets from longitudinal healthcare databases.

BACKGROUND: Patients, physicians, and other decision makers make implicit but inevitable trade-offs among risks and benefits of treatments. Many methods have been proposed to promote transparent and rigorous benefit-risk analysis (BRA). OBJECTIVE: To propose a framework for classifying BRA methods on the basis of key factors that matter most for patients by using a common mathematical notation and compare their results using a hypothetical example. METHODS: We classified the available BRA methods into three categories: 1) unweighted metrics, which use only probabilities of benefits and risks; 2) metrics that incorporate preference weights and that account for the impact and duration of benefits and risks; and 3) metrics that incorporate weights based on decision makers' opinions. We used two hypothetical antiplatelet drugs (a and b) to compare the BRA methods within our proposed framework. RESULTS: Unweighted metrics include the number needed to treat and the number needed to harm. Metrics that incorporate preference weights include those that use maximum acceptable risk, those that use relative-value-adjusted life-years, and those that use quality-adjusted life-years. Metrics that use decision makers' weights include the multicriteria decision analysis, the benefit-less-risk analysis, Boers' 3 by 3 table, the Gail/NCI method, and the transparent uniform risk benefit overview. Most BRA methods can be derived as a special case of a generalized formula in which some are mathematically identical. Numerical comparison of methods highlights potential differences in BRA results and their interpretation. CONCLUSIONS: The proposed framework provides a unified, patient-centered approach to BRA methods classification based on the types of weights that are used across existing methods, a key differentiating feature.