Add like
Add dislike
Add to saved papers

Optimizing an algorithm for the identification and classification of pregnancy outcomes in German claims data.

PURPOSE: For studying drug utilization and safety in pregnancy based on administrative health care data, the reliable identification and classification of pregnancy outcomes in the data is essential. We aimed to optimize an existing algorithm for the identification and classification of pregnancy outcomes in the German Pharmacoepidemiological Research Database (GePaRD) with a particular focus on births.

METHODS: We reconsidered all codes used by the original algorithm and applied it to data of GePaRD from 2006 to 2014. Longitudinal records of pregnancies were used to identify targets for enhancing the algorithm's specificity. We checked the plausibility of the results, eg, regarding the age distribution of persons with pregnancy outcomes. Based on 20 longitudinal records of pregnancies, we compared the outcome classification by clinical experts with the results of the modified algorithm.

RESULTS: Our algorithm identified 1 235 261 pregnancy outcomes in the database, with the majority (94%) being live births, classified as preterm (10%), term (78%), and (12%) births after the expected delivery date. The median age of pregnant women was 32 years (Q1 28; Q3 35). Implausible sequence of outcomes (for example, an induced abortion within a pregnancy categorized as ending in a live birth) were rare (0.03%). The case profile review by clinical experts resulted in the same outcome type and date as the algorithm in 95%.

CONCLUSIONS: Our algorithm led to plausible results regarding the identification and classification of pregnancy outcomes. It will be an important foundation for studies on drug utilization and drug safety during pregnancy based on GePaRD.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

For the best experience, use the Read mobile app

Mobile app image

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.

By using this service, you agree to our terms of use and privacy policy.

Your Privacy Choices Toggle icon

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app