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

Objective: To develop a rule-based algorithm that detects temporal information of clinical events during pregnancy for women with COVID-19 by inferring 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 a multilevel evaluation to test content validity and clinical validity, and extreme length of gestation (300).Results: The algorithm identified 296,194 pregnancies (16,659 COVID-19, 174,744 without COVID-19) in 270,897 pregnant women. For inferring gestational age, 95% cases (n=40) have moderate-high accuracy (Cohen’s Kappa=0.62); 100% cases (n=40) have moderate-high granularity of temporal information (Kappa=1). For inferring delivery dates, the accuracy is 100% (Kappa=1). Accuracy of gestational age detection for extreme length of gestation is 93.3% (Kappa=1). Mothers with COVID-19 showed higher prevalence in obesity or overweight (35.1% vs. 29.5%), diabetes (17.8% vs. 17.0%), chronic obstructive pulmonary disease (0.2% vs. 0.1%), respiratory distress syndrome or acute respiratory failure (1.8% vs. 0.2%).Discussion: We explored the characteristics of pregnant women by different timing of COVID-19 with our algorithm: the first to infer temporal information from EHR and detect the timing of SARS-CoV-2 infection for pregnant women.Conclusion: The algorithm shows excellent validity in inferring gestational age and delivery dates, which supports national EHR cohorts on N3C studying the impact of COVID-19 on pregnancy.