1. DETERMINATION OF PREGNANCY EPISODES AND OUTCOMES WITHIN A DISTRIBUTED NETWORK OF OBSERVATIONAL DATABASES Amy Matcho 1, Patrick B. Ryan, PhD 1, Daniel Fife, MD 1, Dina Gifkins, PhD 1, Chris Knoll, Andrew Friedman, MD 1 1 Janssen Research & Development, LLC, Raritan, NJ BACKGROUND  Claims databases and electronic health records (EHR) are valuable resources for evaluating prescription drug effects during pregnancy  Direct measures of gestational age are generally not available and an algorithm must be developed and validated to identify entire pregnancy episode Study objectives included:  Identify pregnancy episodes including pregnancy start and outcome on a network of population-based databases using the OMOP CDM  Determine pregnancy outcomes including live births, stillbirths, abortions and ectopic pregnancies  Examine all possible episode start markers in our data sources and develop a hierarchy for usage CONFLICT OF INTEREST STATEMENT All authors are full time employees of Janssen Research and Development, a unit of Johnson and Johnson. The work on this study was part of their employment. They also hold pension rights from the company and own stock and stock options. METHODS An algorithm to define pregnancy episodes was developed for:  Truven MarketScan Commercial Claims and Encounters (Truven CCAE)  Truven MarketScan Multi-state Medicaid (Truven MDCD)  Optum ClinFormatics (Optum)  Clinical Practice Research Datalink (CPRD) Our pregnancy episodes by definition begin with estimated last menstrual period date and end with pregnancy outcome date. Step 1 All valid pregnancy outcomes for a patient were identified:  Outcome categories assessed in order of: live birth, stillbirth, ectopic pregnancy, induced abortion, spontaneous abortion and delivery record only  Starting with live births, each outcome found for a patient was assessed against all previously determined valid outcomes for occurrence within clinically plausible time windows  Outcome invalid if antenatal GP visit or pregnancy confirmation occurred within 42 days (except live births)  Ectopic pregnancy outcome required to have procedure or methotrexate  Delivery only and live birth were combined into ‘Live birth’ and spontaneous and induced abortion were combined into ‘Abortion’ outcomes Step 2  Pregnancy starts estimated using hierarchy of pregnancy markers in this order: last menstrual period, gestational age, fertility procedures, ultrasounds (U/S) of nuchal fold thickness (nuchal ultrasound), alpha-fetoprotein tests, amenorrhea diagnoses, urine pregnancy tests and average gestational age estimates  Hierarchy primarily determined from calculated proximity of category start estimation to fertility procedure starts (which accurately date conception)  Only start markers for an episode within minimum and maximum terms per outcome were included  Adjustment performed for conflicts with prior outcomes  First start marker found in hierarchy for an episode used to estimate start Validation included manual review of 700 electronic pregnancy episode patient profiles and completion of a survey with 6 questions that assessed operating characteristics of the algorithm. A 2nd validation was performed comparing proximity of algorithm final start estimation to fertility procedures start estimation. RESULTS Figure 1: In CPRD, ~50% of live birth episodes used last menstrual period record for start estimation. Majority of start estimations for Optum episodes were performed using alpha fetoprotein and nuchal ultrasound tests. Figure 2: Pregnancy episode length distributions generally aligned as expected, however a higher proportion of episodes 290-299 days long occurred when amenorrhea and urine pregnancy test markers were used to estimate start.  Electronic pregnancy episode profile validation: survey results indicated all operating characteristics of the algorithm agreed at least 90% of the time with reviewer chosen characteristics  2nd validation sensitivity results were as follows: 73.3% of algorithm estimated starts for live births were within 2 weeks proximity of fertility procedure estimated starts; ectopic pregnancy 76.5%; stillbirth 46.5% and abortion 36.3% CONCLUSION An algorithm that identifies pregnancy episodes and outcomes was successfully developed on a network of distributed databases with acceptable accuracy concerning all operating characteristics. It can be used to examine drug effects during pregnancy for all outcome types. Mother-child linkages can be developed in the future to examine associations between exposures and congenital disorders. Acknowledgements: We are grateful for pregnancy episode electronic profile review provided by Yiting Wang, Helen Pai and Jamie Price LIMITATIONS  Pregnancy episode information generated by the algorithm was provided within the electronic pregnancy episode profiles. In the absence of conflicting data a reviewer may have tended to agree with the algorithm even if a shorter or longer pregnancy term would have been a more reasonable estimation for that episode  The assisted fertility population may differ from the general population in characteristics such as pregnancy term and care-seeking behavior which may have affected start marker hierarchy determination and 2 nd validation  Care was taken to avoid defining rule-out diagnoses and repeat billings as separate pregnancy episodes in the algorithm but challenging data presentations may have been handled incorrectly by the algorithm