National Health Policy Conference, Washington, DC February 6, 2006 1 Drug Safety Challenges: Considerations for Sources of Data Gerald J. Dal Pan, MD,

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Presentation transcript:

National Health Policy Conference, Washington, DC February 6, Drug Safety Challenges: Considerations for Sources of Data Gerald J. Dal Pan, MD, MHS Director Office of Drug Safety Center for Drug Evaluation and Research FDA

National Health Policy Conference, Washington, DC February 6, Sources of Risk From Medical Products Known Side Effects Unavoidable Avoidable Medication and Device Errors Product Defects Preventable Adverse Events Remaining Uncertainties: Unexpected side effects Unstudied uses Unstudied populations Injury or Death

National Health Policy Conference, Washington, DC February 6, Post-marketing Drug Safety Risk Assessment: Identification of New Adverse Events A fundamental goal of post-marketing drug safety programs Must account for many different types of risk Must account for many potentially confounding factors Must account for time course of events

National Health Policy Conference, Washington, DC February 6, Post-marketing Drug Safety Risk Assessment: What Pre-marketing Safety Data Tell Us Pre-clinical Pharmacology And Toxicology Clinical Pharmacology Clinical Safety Data Open-label Studies Clinical Safety Data Controlled Studies Pre-Marketing Safety Data Product Label

National Health Policy Conference, Washington, DC February 6, Post-marketing Drug Safety Risk Assessment: Identification of New Adverse Events Pre-marketing Safety Data Market Introduction Post-marketing Period

National Health Policy Conference, Washington, DC February 6, How post-marketing adverse event reports get to FDA Patients, consumer, and healthcare professionals FDA MedWatch Manufacturer FDA FDAs Adverse Event Reporting System (AERS) database voluntary regulatory requirements

National Health Policy Conference, Washington, DC February 6, Post-marketing Drug Safety Risk Assessment: Identification of New Adverse Events Pre-marketing Safety Data Market Introduction Post-marketing Period ???

National Health Policy Conference, Washington, DC February 6, Post-marketing Drug Safety Risk Assessment: Identification of New Adverse Events Pre-marketing Safety Data Market Introduction Post-marketing Period Rare but serious adverse event Aplastic anemia Drug-induced liver injury

National Health Policy Conference, Washington, DC February 6, Post-marketing Drug Safety Risk Assessment: Identification of New Adverse Events Pre-marketing Safety Data Market Introduction Post-marketing Period Also common in the population Myocardial infarction

National Health Policy Conference, Washington, DC February 6, Post-marketing Drug Safety Risk Assessment: Identification of New Adverse Events Pre-marketing Safety Data Market Introduction Post-marketing Period Also a manifestation of the underlying disease Myocardial infarction

National Health Policy Conference, Washington, DC February 6, Post-marketing Drug Safety Risk Assessment: Identification of New Adverse Events Pre-marketing Safety Data Market Introduction Post-marketing Period How do we separate a potential signal from the background?

National Health Policy Conference, Washington, DC February 6, Post-marketing Drug Safety Risk Assessment: Investigation of New Adverse Event Risk Pre-marketing Safety Data Market Introduction Post-marketing Period Rare but serious adverse event Intensive case evaluation Look back at pre-marketing safety database

National Health Policy Conference, Washington, DC February 6, Post-marketing Drug Safety Risk Assessment: Investigation of New Adverse Event Risk Pre-marketing Safety Data Market Introduction Post-marketing Period Common in the population OR Manifestation of the underlying disease Intensive case evaluation Look back at pre-marketing safety database Still hard to establish and quantify risk

National Health Policy Conference, Washington, DC February 6, Post-marketing Drug Safety Risk Assessment: Investigation of New Adverse Event Risk Clinical Trial Random Allocation Treatment of Interest Control TreatmentFollow-up Period

National Health Policy Conference, Washington, DC February 6, Post-marketing Drug Safety Risk Assessment: Investigation of New Adverse Event Risk Clinical Trial Random Allocation Treatment of Interest Control TreatmentFollow-up Period

National Health Policy Conference, Washington, DC February 6, Post-marketing Drug Safety Risk Assessment: Investigation of New Adverse Event Risk Clinical Trial Random Allocation Treatment of Interest Control TreatmentFollow-up Period Excess Risk Risk Ratio

National Health Policy Conference, Washington, DC February 6, Alternative Sources of Information Large health care utilization databases Electronic medical record systems Registries Can be used for active surveillance or to answer specific drug safety questions

National Health Policy Conference, Washington, DC February 6, Heath Care Utilization Databases Large, population-based, integrated pharmacy and medical claims databases –filled prescriptions –professional services –hospitalizations Can capture real-world practice patterns, in the context of the system that gives rise to the data (in US, generally within a given health insurance plan or set of plans)

National Health Policy Conference, Washington, DC February 6, Heath Care Utilization Databases Strengths –size –based on actual care –data already collected Limitations –specific clinical data not present –lack of some important health-related information (eg, smoking status) –only captures what is billed for –frequent patient turnover as insurance changes

National Health Policy Conference, Washington, DC February 6, Post-marketing Drug Safety Risk Assessment: Investigation of New Adverse Event Risk Epidemiological Study - Cohort Study Start observation Time Relative risk or hazard ratio

National Health Policy Conference, Washington, DC February 6, Example of a cohort study: Statins and hospitalized rhabdomyolysis Cohort: Drug-specific inception cohorts of statin and fibrate users, based on data from 11 US health plans using automated claims covering prescription drugs, outpatient care, hospitalizations, and medical procedures Exposure: Algorithm developed to calculate person-time on drug for each patient based on prescription claims. Separate classifications for monotherapy and statin-fibrate combination therapy Outcome: Medical record review of all patients based on hospitalization claims with at least one ICD-9-CM code suggestive of severe muscle injury, followed by a blinded review to determine cases of rhabdomyolysis. Source: Graham D et al. JAMA 2004;292:

National Health Policy Conference, Washington, DC February 6, Example of a cohort study: Statins and hospitalized rhabdomyolysis Analysis: Relative risk estimates of rhabdomyolysis, adjusted for age, sex, and diabetes mellitus were calculated using Poisson regression. Incidence rates per 10,000 person-years of treatment, with 95% CIs, were calculated. Source: Graham D et al. JAMA 2004;292:

National Health Policy Conference, Washington, DC February 6, Electronic Medical Records Contain more information than claims databases: –medications prescribed –detail clinical information (eg, symptoms and signs) –physical examination results –results of diagnostic tests Example: General Practitioner Research Database (GPRD)

National Health Policy Conference, Washington, DC February 6, Post-marketing Drug Safety Risk Assessment: Investigation of New Adverse Event Risk Registry Case-control studies Estimate magnitude of problem Study natural history or survival Persons with disease of interest

National Health Policy Conference, Washington, DC February 6, Post-marketing Drug Safety Risk Assessment: Investigation of New Adverse Event Risk Registry Risk factors for exposure Estimate magnitude of exposure Outcome of exposure Persons with exposure of interest

National Health Policy Conference, Washington, DC February 6, Use of a Postmarketing Registry: Antiepileptic Drugs and Teratogenicity Pregnant women with epilepsy on valproic acid Enrollment7 months Birth Postpartum Outcome ascertainment 149 VPA-exposed, 16 with major malformations (10.7%, 95% CI: ) Internal comparator rate: 2.9% (95% CI: ) External comparator rate: 1.62% Source: Wyszynski DF et al. Neurology 2005;64:

National Health Policy Conference, Washington, DC February 6, New Database Acquisitions Four organizations with linked pharmacy-medical claims databases Contracts signed September 2005 Allows for collaborations between FDA epidemiologists and experts at these organizations Four organizations: –HMO Research Network/Harvard Pilgrim Health –Kaiser Family Foundation –Vanderbilt University –Ingenix (i3Drug Safety)

National Health Policy Conference, Washington, DC February 6, New Database Acquisitions Four organizations: –Harvard Pilgrim Health/HMO Research Network Eight geographically diverse health plans with 3.2 million members Electronic medical records available for 6 of 8 sites –Kaiser Family Foundation 6.1 current members in northern and southern California Fully integrated databases, linked to vital statistics and cancer registries Unique formulary limited to selected drugs and indications –Vanderbilt University Two state Medicaid populations (Tennessee and Washington) 2.2 millions members, some at high medical risk (eg, the poor, nursing home residents) –Ingenix Geographically diverse insured population of 12 million members Some laboratory data also available

National Health Policy Conference, Washington, DC February 6, Active Surveillance Request for Information issued April 2005 Responses received June 2005 Responses currently under review Agency will decide on next steps

National Health Policy Conference, Washington, DC February 6, CMS Interactions ODS epidemiogists are working with CMS and AHRQ staff to understand better the nature of CMS data Current efforts focused on using Part B data for a pilot drug safety study Still in learning/exploratory stages