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Objective: To develop a rule-based algorithm that detects temporalinformation of clinical events during pregnancy for women with COVID-19 byinferring gestational weeks and delivery dates from Electronic Health Records(EHR) from the National COVID Cohort Collaborate (N3C). Materials and Methods:The EHR are normalized by the Observational Medical Outcomes Partnership (OMOP)Clinical Data Model (CDM). EHR phenotyping resulted in 270,897 pregnant women(2018-06-01 to 2021-05-31). We developed a rule-based algorithm and performed amulti-level evaluation to test content validity and clinical validity of thealgorithm; and extreme value analysis for individuals with <150 or >300 days ofgestation. Results: The algorithm identified 296,194 pregnancies (16,659COVID-19 174 and 744 without COVID-19 peri-pandemic) in 270,897 pregnant women.For inferring gestational age, 95% cases (n=40) have moderate-high accuracy(Cohen Kappa = 0.62); 100% cases (n=40) have moderate-high granularity oftemporal information (Cohen Kappa = 1). For inferring delivery dates, theaccuracy is 100% (Cohen Kappa = 1). Accuracy of gestational age detection forextreme length of gestation is 93.3% (Cohen Kappa = 1). Mothers with COVID-19showed higher prevalence in obesity (35.1% vs. 29.5%), diabetes (17.8% vs.17.0%), chronic obstructive pulmonary disease (COPD) (0.2% vs. 0.1%),respiratory distress syndrome (ARDS) (1.8% vs. 0.2%). Discussion: We exploredthe characteristics of pregnant women by different timing of COVID-19 with ouralgorithm: the first to infer temporal information from complete antenatal careand detect the timing of SARS-CoV-2 infection for pregnant women using N3C.Conclusion: The algorithm shows excellent validity in inferring gestational ageand delivery dates, which supports national EHR cohorts on N3C studying theimpact of COVID-19 on pregnancy.

plos one

Temporal Events Detector for Pregnancy Care (TED-PC): A Rule-based  Algorithm to Infer Gestational Age and Delivery Date from Electronic Health  Records of Pregnant Women with and without COVID-19