Who is pregnant? Defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C).

Autor:	Jones SE; Office of Data Science and Emerging Technologies, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 20852, United States., Bradwell KR; Palantir Technologies, Denver, CO 80202, United States., Chan LE; College of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331, United States., McMurry JA; Department of Biomedical Informatics, University of Colorado, Anschutz Medical Campus, Aurora, CO 80045, United States., Olson-Chen C; Department of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, NY 14620, United States., Tarleton J; Department of Obstetrics and Gynecology, Medical University of South Carolina, Charleston, SC 29425, United States., Wilkins KJ; Biostatistics Program, Office of the Director, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, United States., Ly V; Department of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, NY 14620, United States., Ljazouli S; Palantir Technologies, Denver, CO 80202, United States., Qin Q; Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY 14618, United States., Faherty EG; School of Public Health, University of Minnesota, Minneapolis, MN 55455, United States., Lau YK; Sema4, Stamford, CT 06902, United States., Xie C; Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY 14618, United States., Kao YH; Sema4, Stamford, CT 06902, United States., Liebman MN; IPQ Analytics, LLC, Kennett Square, PA 19348, United States., Mariona F; Beaumont Hospital, Dearborn, MI 48124, United States.; Wayne State University, Detroit, MI 48202, United States., Challa AP; Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN 37212, United States., Li L; Sema4, Stamford, CT 06902, United States., Ratcliffe SJ; Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22903, United States., Haendel MA; College of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331, United States., Patel RC; Department of Medicine and Global Health, University of Washington, Seattle, WA 98105, United States., Hill EL; Department of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, NY 14620, United States.; Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY 14618, United States.
Jazyk:	angličtina
Zdroj:	JAMIA open [JAMIA Open] 2023 Aug 16; Vol. 6 (3), pp. ooad067. Date of Electronic Publication: 2023 Aug 16 (Print Publication: 2023).
DOI:	10.1093/jamiaopen/ooad067
Abstrakt:	Objectives: To define pregnancy episodes and estimate gestational age within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C). Materials and Methods: We developed a comprehensive approach, named Hierarchy and rule-based pregnancy episode Inference integrated with Pregnancy Progression Signatures (HIPPS), and applied it to EHR data in the N3C (January 1, 2018-April 7, 2022). HIPPS combines: (1) an extension of a previously published pregnancy episode algorithm, (2) a novel algorithm to detect gestational age-specific signatures of a progressing pregnancy for further episode support, and (3) pregnancy start date inference. Clinicians performed validation of HIPPS on a subset of episodes. We then generated pregnancy cohorts based on gestational age precision and pregnancy outcomes for assessment of accuracy and comparison of COVID-19 and other characteristics. Results: We identified 628 165 pregnant persons with 816 471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, abortions), and 23.3% had unknown outcomes. Clinician validation agreed 98.8% with HIPPS-identified episodes. We were able to estimate start dates within 1 week of precision for 475 433 (58.2%) episodes. 62 540 (7.7%) episodes had incident COVID-19 during pregnancy. Discussion: HIPPS provides measures of support for pregnancy-related variables such as gestational age and pregnancy outcomes based on N3C data. Gestational age precision allows researchers to find time to events with reasonable confidence. Conclusion: We have developed a novel and robust approach for inferring pregnancy episodes and gestational age that addresses data inconsistency and missingness in EHR data. Competing Interests: K.R.B. and S.L. are employees of Palantir Technologies. Y.K. and L.L. are employees of Sema4. M.N.L. is Managing Director of IPQ Analytics, LLC. (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)
Databáze:	MEDLINE
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