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

Lyu, Tianchu; Liang, Chen; Liu, Jihong; Campbell, Berry; Hung, Peiyin; Shih, Yi-Wen; Ghumman, Nadia; Li, Xiaoming; National COVID Cohort Collaborative Consortium

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

Files

pone.0276923.pdf (2.24 MB)

Authors

Lyu, Tianchu

Liang, Chen

Liu, Jihong

Campbell, Berry

Hung, Peiyin

Shih, Yi-Wen

Ghumman, Nadia

Li, Xiaoming

National COVID Cohort Collaborative Consortium

Issue Date

2022-10-31

Type

Article

Language

en_US

Keywords

Temporal Events Detector , Pregnancy Care , Gestational Age , COVID-19

Abstract

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 1st, 2018 to May 31st, 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 (<150 or >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.

Citation

Lyu, T., Liang, C., Liu, J., Campbell, B., Hung, P., Shih, Y. W., Ghumman, N., Li, X., & National COVID Cohort Collaborative Consortium (2022). 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. PloS one, 17(10), e0276923. https://doi.org/10.1371/journal.pone.0276923

Publisher

PloS One