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Using Automated Databases to Assess Fetal Effects of Maternal Medication Use William Cooper, M.D., M.P.H. Vanderbilt University School of Medicine FDA/OWH.

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Presentation on theme: "Using Automated Databases to Assess Fetal Effects of Maternal Medication Use William Cooper, M.D., M.P.H. Vanderbilt University School of Medicine FDA/OWH."— Presentation transcript:

1 Using Automated Databases to Assess Fetal Effects of Maternal Medication Use William Cooper, M.D., M.P.H. Vanderbilt University School of Medicine FDA/OWH Pregnancy and Prescription Medication Use Symposium

2 Objectives Discuss role of automated databases in conducting studies of maternal drug exposures and fetal effects Review findings from OWH funded studies Consider future directions

3 Lessons Learned from Thalidomide Epidemiology played a critical role –Response to signal –Epidemiologic assessment Placenta not protective Need for regulation and monitoring of medications and effects on fetus

4 Population-Based Studies Provide estimates of potential exposures Test signals with adequate power Provide directions for targeted study –Maternal differences –Fetal differences Opportunities for informing policies –Identify medications with low risk –Identify medications with high risk

5 Role of Epidemiologic Studies Assess Risk Signal Detection Case-control Studies Cohort Studies

6 Administrative Data: Tennessee Linked data system dating to 1974 * Critical elements validated Used in over 300 research studies Filled pharmacy claims –Validated as measure of drug exposure † –Avoids maternal recall bias * Ray et al Annals Epidem 1989 Cooper & Kuhlthau Amb Pediatr 2001 † Landry et al Gertontologist 1988; Leister Med Care 1981; Johnson J Am Ger Soc 1991

7 Evaluation: Administrative Data *Ray et al Am J Epidem 1989; Cooper et al Paediatr Perinatal Epi 2008 MEDICAL RECORDS DEATH CERTIFICATE TENNCARE FILES ALL-PAYERS DATA CENSUS DATA BIRTH CERTIFICATE LINK FILE

8 Unique Considerations: Databases Choosing the right question Cohort identification Exposure ascertainment Outcome –Identification –Validation

9 Choosing the Right Question Signal –Vigilant health care providers –FDA adverse event reports –Registries (HIV-infected women, Accutane) –Surveillance (CDC, Teratology Services) Public Health Importance –Bioterrorism Antibiotics and Fetal Risks –FDA OWH Biologic Plausibility

10 Choosing the Right Question Feasibility –Timing of exposure (trimester) –Potential outcomes measurable –Sufficient use Statistical power Public health guidance needed for safety

11 Antidepressant Exposures Cooper, Willy et al, Am J Obstet Gynecol 2007;197

12 Cohort Selection of pregnancies –Enrollment throughout pregnancy * –Complete information Selection of appropriate comparison group –Non-users –Active comparators * Cooper et al, New Engl J Med 2006; 354;23-31

13 Exposures *LMP validated 94% of time (Cooper et al Pharmepi Drug Safety 2008) 1st 2nd 3rd PRE LMP* DOB Date of prescription through days supply Drugs with long half-lives (biologics) Drugs in combination Drugs with overlap

14 Antibiotics and Pregnancy* GroupAny Cipro † Any Azithro AnyDoxyAnyAmoxAnyErythNone N infants 5881,4591,84314,5342,1283,400 Mean age at delivery 23.221.622.622.622.023.1 Black race, % 46.368.163.241.454.556.4 Chronic illness, % 25.519.819.320.920.215.3 Smoking, % 35.217.825.130.429.725.6 Level III hospital, % 41.344. * Cooper et al, Paed Perinatal Epidemiol 2008; 23:18 † Mutually exclusive categories (i.e. Any cipro, Any azithro (no cipro), etc.

15 Outcomes Major Congenital Malformations –CDC * definitions –Possible: vital records or claims in first year of life –Confirmed: review of medical records † Blinded adjudication by two investigators Malformation-specific confirmation rules –e.g. Transposition of the Great Vessels Echo, cardiac cath, surgical note, or autopsy finding * Metropolitan Atlanta Congenital Defects Program † † Cooper et al Pharmacoepidemiol Drug Safety 2008

16 Confirmed Defects [n=869 (2.9%)] N infants Cardiovascular (n=304) Atrial septal defect 141 Patent ductus arteriosus (term infants) 121 Ventricular septal defect 76 Central nervous system (n=82) Spina bifida 8 Microcephaly and eye anomaly 17 Hydrocephalus25 Renal/Genitourinary (n=166) Renal dysplasia 27 Genital anomalies 89 Musculo-skeletal defects (n=207) Polydactyly147 Upper limb defects 21 Craniofacial anomalies 10 * Cooper et al, Paed Perinatal Epidemiol 2008; 23:18

17 Positive Predictive Value Birth certificates Inpatient claims Eithersource All defects 69.9%69.9%67.7% Cardiac35.7%74.5%67.7% GI94.1%73.6%73.5% Musculo94.1%65.0%67.8% GU65.0%68.4%67.3% CNS63.3%48.9%47.1% Orofacial93.6%93.3%90.9% * Cooper et al, Pharmacoepi Drug Safety 2008; 17:455

18 Antibiotics & Malformations Cooper et al, Paediatric and Perinatal Epidemiology 2009

19 Antibiotics: Implications Antibiotics that might be needed in the event of bioterrorism attack should not result in a greater incidence of overall congenital malformations in infants whose mothers take these medications.

20 Studies of Pregnancy Exposures Drug(s)OutcomesPublication Macrolides ↑ Pyloric stenosis Obstet Gynecol 2002; 100:101 Category X Risk groups for use Paed Perinatal Epi 2004; 18:106 ACE inhibitors ↑ Heart, CNS New Engl J Med 2006; 354:2443 SSRI antidep. ↑ use Am J Ob Gyn 2007; 196:e1 ACE inhibitors ↑ use Am J Ob Gyn 2008; 198:e1 Antibiotics No ↑ malformation risk Paed Perinatal Epi 2009; 23:18 Harmful drugs ↑ use in Haiti Acad Pediatr 2010;10:395

21 Ongoing Work Immunosuppressives (NIAMS, AHRQ) HIV Medications (NICHD) Medication Exposures in Pregnancy Research and Evaluation Program [MEPREP] (FDA)

22 Immunosuppressives in Pregnancy Immunosuppressives –Biologics, methotrexate, others –Used to treat autoimmune conditions –Little or no information to guide pregnancy use Outcomes –Malformations and perinatal outcomes Participating sites –Vanderbilt (lead site) –Kaiser Permanente Northern California Southern California  

23 Distributed Data Network Common protocol VUKPSCKPNC Standardized datasets at local sites Limited use data files sent to lead site - combined for analysis Lead site for each study generates code, sends to local sites Case Reviews at local sites sent to lead site for confirmation

24 In Utero HIV Medications Several treatments, all understudied Collaboration with two other sites –Harvard School of Public Health –Brigham and Women’s Pharmacoepidemiology Unit Outcomes –Malformations –NICU hospitalization –Death

25 Pregnancy Network (MEPREP) Multiple sites –HMO Research Network (15 health plans) –Kaiser Permanente (2 health plans) –Vanderbilt (Tennessee Medicaid) Standardized data files/distributed data Increased sample size and distribution Collaboration with FDA

26 What is Needed? Active surveillance Follow-up of signal with studies –No study design is perfect –Draw on strengths of various designs –Consortia to assess signal Methods for conveying information –Information for policy makers –Information for health care providers –Information for women of child-bearing age

27 AcknowledgmentsCollaborators –Vanderbilt Wayne Ray Gerald Hickson Mike Stein –Harvard Sonia Hernandez-Diaz –Kaiser Permanente De-Kun Li Craig Cheetham –FDA Mary Willy Judy Staffa Rita Ouellet-Hellstrom Pam Scott Kristin Phucas Biostatistics –Patrick Arbogast –Lisa Kaltenbach –Hua Ding Trainees –Michael Bowen –Megan Cevasco –Brooke Thompson –Stephen Pont –Astride Jules Research Staff –Programmers Judy Dudley Kathi Hall Tony Morrow –Research Nurses Pat Gideon Leanne Balmer Michelle DeRanieri Dee Wood –Research Coordinators Shannon Stratton Lynne Caples Funders –AHRQ HS 13084 –NIAMS AR 07001 –FDA 223-02-3003, 223-05-10100 –NICHD HD 056940

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