Abstract Objective To develop a cohort identification algorithm that captures pregnancy episodes in electronic health record data and characterize demographic and clinical data for eligible patients. Materials and Methods We used data extracted from EHRs and conformed to the PCORnet Common Data Model between July 2016 and June 2023 for this study. Results Our phenotype identified 74 480 pregnant people associated with 95 503 pregnancies between July 2016 to June 2023. The cohort was 44.4% non-Hispanic White, 37.5% non-Hispanic Black, and 11.4% Hispanic. The median maternal age in the cohort was 29. Most pregnancy episodes (80.1%) resulted in live birth. The rate of deliveries with a severe maternal morbidity (SMM) was 179 per 10 000 deliveries, 51.1% of which were associated with a non-Hispanic Black person (p .01). The preterm birth rate was 12.8%, and the percentage of deliveries with timely postpartum care was 73.9%. The rate of deliveries where the person had a diagnosis of gestational diabetes was 9.1%, and the rate for hypertensive disorders was 24.2%. Discussion Clinical information about a pregnancy episode can be challenging to visualize using EHR data alone, even as it is increasingly critical to be able to do so for public health and policy research. The methods we describe result in a database of pregnancy episodes that distills complex data into a useful format for pragmatic researchers. Conclusion We established a replicable method of identifying pregnancy episodes using electronic health record data using coding ontologies common in clinical practice and research. We demonstrated proof of concept by running simple maternal health metrics on the cohort.
Crull et al. (Tue,) studied this question.
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