{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T09:24:45Z","timestamp":1775035485617,"version":"3.50.1"},"reference-count":34,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T00:00:00Z","timestamp":1667174400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000060","name":"National Institute of Allergy and Infectious Diseases","doi-asserted-by":"publisher","award":["3R01AI127203-05S2"],"award-info":[{"award-number":["3R01AI127203-05S2"]}],"id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000060","name":"National Institute of Allergy and Infectious Diseases","doi-asserted-by":"publisher","award":["R21AI170171"],"award-info":[{"award-number":["R21AI170171"]}],"id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Objective<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Materials and methods<\/jats:title>\n<jats:p>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<jats:sup>st<\/jats:sup>, 2018 to May 31<jats:sup>st<\/jats:sup>, 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 (&lt;150 or &gt;300).<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>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\u2019s Kappa = 0.62); 100% cases (n = 40) have moderate-high granularity of temporal information (Cohen\u2019s Kappa = 1). For inferring delivery dates, the accuracy is 100% (Cohen\u2019s Kappa = 1). The accuracy of gestational age detection for the extreme length of gestation is 93.3% (Cohen\u2019s 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%).<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Discussion<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec005\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0276923","type":"journal-article","created":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T17:43:13Z","timestamp":1667238193000},"page":"e0276923","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":8,"title":["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"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5937-4432","authenticated-orcid":true,"given":"Tianchu","family":"Lyu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9803-9880","authenticated-orcid":true,"given":"Chen","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jihong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Berry","family":"Campbell","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1529-0819","authenticated-orcid":true,"given":"Peiyin","family":"Hung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi-Wen","family":"Shih","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nadia","family":"Ghumman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5555-9034","authenticated-orcid":true,"given":"Xiaoming","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"on behalf of the National COVID Cohort Collaborative Consortium","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2022,10,31]]},"reference":[{"key":"pone.0276923.ref001","doi-asserted-by":"crossref","first-page":"m3320","DOI":"10.1136\/bmj.m3320","article-title":"Clinical manifestations, risk factors, and maternal and perinatal outcomes of coronavirus disease 2019 in pregnancy: living systematic review and meta-analysis","volume":"370","author":"J Allotey","year":"2020","journal-title":"BMJ"},{"issue":"44","key":"pone.0276923.ref002","doi-asserted-by":"crossref","first-page":"1641","DOI":"10.15585\/mmwr.mm6944e3","article-title":"Update: Characteristics of Symptomatic Women of Reproductive Age with Laboratory-Confirmed SARS-CoV-2 Infection by Pregnancy Status\u2014United States, January 22-October 3, 2020","volume":"69","author":"LD Zambrano","year":"2020","journal-title":"MMWR Morb Mortal Wkly Rep"},{"issue":"2","key":"pone.0276923.ref003","doi-asserted-by":"crossref","first-page":"e95","DOI":"10.1016\/S2589-7500(21)00250-8","article-title":"The effect of maternal SARS-CoV-2 infection timing on birth outcomes: a retrospective multicentre cohort study","volume":"4","author":"SN Piekos","year":"2022","journal-title":"The Lancet Digital Health"},{"issue":"3","key":"pone.0276923.ref004","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1097\/PSY.0b013e31820a62ce","article-title":"Timing of prenatal maternal exposure to severe life events and adverse pregnancy outcomes: a population study of 2.6 million pregnancies","volume":"73","author":"QA Class","year":"2011","journal-title":"Psychosom Med"},{"issue":"5","key":"pone.0276923.ref005","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.1172\/JCI87490","article-title":"Risks associated with viral infections during pregnancy","volume":"127","author":"K Racicot","year":"2017","journal-title":"The Journal of Clinical Investigation"},{"key":"pone.0276923.ref006","unstructured":"MedlinePlus. 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