TY - JOUR
T1 - Temporal Events Detector for Pregnancy Care (TED-PC)
T2 - A rule-based algorithm to infer gestational age and delivery date from electronic health records of pregnant women with and without COVID-19
AU - the National COVID Cohort Collaborative Consortium
AU - Lyu, Tianchu
AU - Liang, Chen
AU - Liu, Jihong
AU - Campbell, Berry
AU - Hung, Peiyin
AU - Shih, Yi Wen
AU - Ghumman, Nadia
AU - Li, Xiaoming
AU - Haendel, Melissa A.
AU - Chute, Christopher G.
AU - Walden, Anita
AU - Gersing, Kenneth R.
AU - Misquitta, Leonie
AU - Rutter, Joni L.
AU - Burgoon, Penny Wung
AU - Bozzette, Samuel
AU - Deacy, Mariam
AU - Dillon, Christopher
AU - Erwin-Cohen, Rebecca
AU - Garbarini, Nicole
AU - Gordon, Valery
AU - Kurilla, Michael G.
AU - Marti, Emily Carlson
AU - Michael, Sam G.
AU - Portilla, Lili
AU - Schmitt, Clare
AU - Temple-O'Connor, Meredith
AU - Bennett, Tellen D.
AU - Eichmann, David A.
AU - Guinney, Justin
AU - Kibbe, Warren A.
AU - Liu, Hongfang
AU - Payne, Philip R.O.
AU - Pfaff, Emily R.
AU - Robinson, Peter N.
AU - Saltz, Joel H.
AU - Spratt, Heidi
AU - Starren, Justin
AU - Suver, Christine
AU - Wilcox, Adam B.
AU - Williams, Andrew E.
AU - Wu, Chunlei
AU - Gabriel, Davera
AU - Hong, Stephanie S.
AU - Kostka, Kristin
AU - Lehmann, Harold P.
AU - Moffitt, Richard A.
AU - Morris, Michele
AU - Palchuk, Matvey B.
AU - Bramante, Carolyn
N1 - Funding Information:
Research reported in this publication was supported by the National Institute of Allergy And Infectious Diseases of the National Institutes of Health under Award Numbers 3R01AI127203-05S2 and R21AI170171. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
Copyright: © 2022 Lyu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2022/10
Y1 - 2022/10
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85140938912&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85140938912&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0276923
DO - 10.1371/journal.pone.0276923
M3 - Article
C2 - 36315520
AN - SCOPUS:85140938912
SN - 1932-6203
VL - 17
JO - PloS one
JF - PloS one
IS - 10 October
M1 - e0276923
ER -
