Near-Real-Time Monitoring of New Drugs: An Application Comparing Prasugrel Versus Clopidogrel

BackgroundMethods for near-real-time monitoring of new drugs in electronic healthcare data are needed.ObjectiveIn 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.MethodsUsing 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.ResultsWe 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.ConclusionsNear-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.

[1]  E. Antman,et al.  Prasugrel versus clopidogrel in patients with acute coronary syndromes. , 2007, The New England journal of medicine.

[2]  Malcolm Maclure,et al.  Design considerations in an active medical product safety monitoring system , 2012, Pharmacoepidemiology and drug safety.

[3]  Bruce H Fireman,et al.  Confounding Adjustment in Comparative Effectiveness Research Conducted Within Distributed Research Networks , 2013, Medical care.

[4]  J. Gagne You can observe a lot (about medical products) by watching (those who use them). , 2013, Epidemiology.

[5]  Jeremy A Rassen,et al.  Active Safety Monitoring of New Medical Products Using Electronic Healthcare Data: Selecting Alerting Rules , 2012, Epidemiology.

[6]  M Alan Brookhart,et al.  Covariate selection in high-dimensional propensity score analyses of treatment effects in small samples. , 2011, American journal of epidemiology.

[7]  Sebastian Schneeweiss,et al.  Active safety monitoring of newly marketed medications in a distributed data network: application of a semi-automated monitoring system , 2012, Clinical pharmacology and therapy.

[8]  Brian Sauer,et al.  Guidelines for good database selection and use in pharmacoepidemiology research , 2012, Pharmacoepidemiology and drug safety.

[9]  Sebastian Schneeweiss,et al.  Using high‐dimensional propensity scores to automate confounding control in a distributed medical product safety surveillance system , 2012, Pharmacoepidemiology and drug safety.

[10]  Richard Platt,et al.  The U.S. Food and Drug Administration's Mini‐Sentinel program: status and direction , 2012, Pharmacoepidemiology and drug safety.

[11]  Mary K. Kowal,et al.  Availability of comparative efficacy data at the time of drug approval in the United States. , 2011, JAMA.

[12]  R. Platt,et al.  Developing the Sentinel System--a national resource for evidence development. , 2011, The New England journal of medicine.

[13]  Sebastian Schneeweiss,et al.  A basic study design for expedited safety signal evaluation based on electronic healthcare data , 2010, Pharmacoepidemiology and drug safety.

[14]  J. Avorn,et al.  High-dimensional Propensity Score Adjustment in Studies of Treatment Effects Using Health Care Claims Data , 2009, Epidemiology.

[15]  Sander Greenland,et al.  Modern Epidemiology 3rd edition , 1986 .

[16]  R J Glynn,et al.  Assessing the Comparative Effectiveness of Newly Marketed Medications: Methodological Challenges and Implications for Drug Development , 2011, Clinical pharmacology and therapeutics.

[17]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[18]  Louette R. Johnson Lutjens Research , 2006 .

[19]  Jeremy A Rassen,et al.  An Event‐Based Approach for Comparing the Performance of Methods for Prospective Medical Product Monitoring , 2012, Pharmacoepidemiology and drug safety.

[20]  J. Avorn,et al.  Early Steps in the Development of a Claims-Based Targeted Healthcare Safety Monitoring System and Application to Three Empirical Examples , 2012, Drug Safety.

[21]  C. Wolfe,et al.  Utility of electronic patient records in primary care for stroke secondary prevention trials , 2011, BMC public health.

[22]  Sebastian Schneeweiss,et al.  A combined comorbidity score predicted mortality in elderly patients better than existing scores. , 2011, Journal of clinical epidemiology.

[23]  Lonny Reisman,et al.  Full coverage for preventive medications after myocardial infarction. , 2011, The New England journal of medicine.

[24]  W. Ray,et al.  Evaluating medication effects outside of clinical trials: new-user designs. , 2003, American journal of epidemiology.

[25]  Sebastian Schneeweiss,et al.  Validation of claims‐based diagnostic and procedure codes for cardiovascular and gastrointestinal serious adverse events in a commercially‐insured population , 2010, Pharmacoepidemiology and drug safety.

[26]  W. Longstreth,et al.  Validating Administrative Data in Stroke Research , 2002, Stroke.

[27]  M. Graffar [Modern epidemiology]. , 1971, Bruxelles medical.

[28]  R. Willke,et al.  Treatment dynamics of newly marketed drugs and implications for comparative effectiveness research. , 2013, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[29]  S. Schneeweiss,et al.  Developments in Post‐marketing Comparative Effectiveness Research , 2007, Clinical pharmacology and therapeutics.

[30]  N. Deshpande,et al.  Prasugrel versus clopidogrel for acute coronary syndromes without revascularization , 2013 .

[31]  Deepak L. Bhatt Prasugrel in clinical practice. , 2009, The New England journal of medicine.

[32]  L. Smeeth,et al.  Pragmatic randomised trials using routine electronic health records: putting them to the test , 2012, BMJ : British Medical Journal.

[33]  Michael E Matheny,et al.  Automated surveillance to detect postprocedure safety signals of approved cardiovascular devices. , 2010, JAMA.

[34]  M Maclure,et al.  Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data. , 2001, American journal of epidemiology.