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Optimizing an algorithm for the identification and classification of pregnancy outcomes in German claims data
Nadine Wentzell1, Tania Schink1, Ulrike Haug1,2
1Department of Clinical Epidemiology, Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany.
Pharmacoepidemiology and Drug Safety
|July 20, 2018
Summary
An optimized algorithm accurately identifies and classifies pregnancy outcomes in German healthcare data. This enhances studies on drug safety and utilization during pregnancy.
Area of Science:
- Pharmacoepidemiology
- Health Informatics
Background:
- Reliable identification of pregnancy outcomes is crucial for drug safety and utilization studies using administrative health data.
- The German Pharmacoepidemiological Research Database (GePaRD) is a valuable resource for such research.
Purpose of the Study:
- To optimize an existing algorithm for identifying and classifying pregnancy outcomes.
- Focus on enhancing accuracy for births within the GePaRD database.
Main Methods:
- Re-evaluation of existing algorithm codes and application to GePaRD data (2006-2014).
- Utilized longitudinal pregnancy records to improve algorithm specificity.
- Compared algorithm results with clinical expert classifications on a subset of cases.
Main Results:
- Identified over 1.2 million pregnancy outcomes, with 94% being live births.
- Classified live births into preterm (10%), term (78%), and post-term (12%).
- Algorithm demonstrated high accuracy, with 95% agreement with clinical expert review and rare implausible outcomes (0.03%).
Conclusions:
- The optimized algorithm provides plausible and reliable identification and classification of pregnancy outcomes.
- This enhanced algorithm serves as a foundation for future pharmacoepidemiological studies on drug safety in pregnancy using GePaRD data.
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