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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.
  • Lyu T; Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, United States of America.
  • Liang C; Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, United States of America.
  • Liu J; Department of Epidemiology & Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, United States of America.
  • Campbell B; Department of Obstetrics and Gynecology, School of Medicine, University of South Carolina, Columbia, South Carolina, United States of America.
  • Hung P; Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, United States of America.
  • Shih YW; Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, United States of America.
  • Ghumman N; Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, United States of America.
  • Li X; Department of Health Promotion Education and Behaviors, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, United States of America.
PLoS One ; 17(10): e0276923, 2022.
Article in English | MEDLINE | ID: covidwho-2098766
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.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Pregnancy Complications, Infectious / Premature Birth / COVID-19 Type of study: Cohort study / Diagnostic study / Experimental Studies / Observational study / Prognostic study Limits: Female / Humans / Pregnancy Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2022 Document Type: Article Affiliation country: Journal.pone.0276923

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pregnancy Complications, Infectious / Premature Birth / COVID-19 Type of study: Cohort study / Diagnostic study / Experimental Studies / Observational study / Prognostic study Limits: Female / Humans / Pregnancy Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2022 Document Type: Article Affiliation country: Journal.pone.0276923