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

• Published in: PloS one Vol. 17; no. 10; p. e0276923
• Main Authors: Lyu, Tianchu, Liang, Chen, Liu, Jihong, Campbell, Berry, Hung, Peiyin, Shih, Yi-Wen, Ghumman, Nadia, Li, Xiaoming
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 31.10.2022; Public Library of Science (PLoS)
• Subjects: Age; Algorithms; Analysis; Biology and life sciences; Body weight; Childbirth & labor; Chronic obstructive pulmonary disease; Computer and Information Sciences; Coronaviruses; COVID-19; Diabetes mellitus; Electronic health records; Electronic medical records; Electronic records; Gestation; Gestational age; Health aspects; Infections; Information retrieval; Lung diseases; Management; Medical records; Medicine and Health Sciences; Obstructive lung disease; Overweight; Pandemics; Phenotyping; Physical Sciences; Pregnancy; Pregnant women; Research and Analysis Methods; Respiratory distress syndrome; Respiratory failure; Severe acute respiratory syndrome coronavirus 2; Viral diseases; Viral infections; Womens health; United States
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0276923

Objective Identifying the time of SARS-CoV-2 viral infection relative to specific gestational weeks is critical for delineating the role of viral infection timing in adverse pregnancy outcomes. However, this task is difficult when it comes to Electronic Health Records (EHR). In combating the COVID-19 pandemic for maternal health, we sought to develop and validate a clinical information extraction algorithm to detect the time of clinical events relative to gestational weeks. Materials and methods We used EHR from the National COVID Cohort Collaborative (N3C), in which the EHR are normalized by the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). We performed EHR phenotyping, resulting in 270,897 pregnant women (June 1.sup.st, 2018 to May 31.sup.st, 2021). We developed a rule-based algorithm and performed a multi-level 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 (Cohen's Kappa = 1). For inferring delivery dates, the accuracy is 100% (Cohen's Kappa = 1). The accuracy of gestational age detection for the extreme length of gestation is 93.3% (Cohen's 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 gestational weeks of SARS-CoV-2 infection with our algorithm. TED-PC is the first to infer the exact gestational week linked with every clinical event from EHR and detect the timing of SARS-CoV-2 infection in pregnant women. Conclusion The algorithm shows excellent clinical validity in inferring gestational age and delivery dates, which supports multiple EHR cohorts on N3C studying the impact of COVID-19 on pregnancy.