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Distinguishing Admissions Specifically for COVID-19 from Incidental SARS-CoV-2 Admissions: A National EHR Research Consortium Study
Jeffrey G Klann; Zachary H Strasser; Meghan R Hutch; Chris J Kennedy; Jayson S Marwaha; Michele Morris; Malarkodi Jebathilagam Samayamuthu; Ashley C Pfaff; Hossein Estiri; Andrew M South; Griffin M Weber; William Yuan; Paul Avillach; Kavishwar B Wagholikar; Yuan Luo; - The Consortium for Clinical Characterization of COVID-19 by EHR (4CE); Gilbert S. Omenn; Shyam Visweswaran; John H Holmes; Zongqi Xia; Gabriel A Brat; Shawn N Murphy.
Afiliación
  • Jeffrey G Klann; Massachusetts General Hospital
  • Zachary H Strasser; Massachusetts General Hospital
  • Meghan R Hutch; Northwestern University
  • Chris J Kennedy; Massachusetts General Hospital
  • Jayson S Marwaha; Beth Israel Deaconess Medical Center
  • Michele Morris; University of Pittsburgh
  • Malarkodi Jebathilagam Samayamuthu; University of Pittsburgh
  • Ashley C Pfaff; Beth Israel Deaconess Medical Center, Harvard Medical School
  • Hossein Estiri; Massachusetts General Hospital
  • Andrew M South; Brenner Children's, Wake Forest School of Medicine
  • Griffin M Weber; Harvard Medical School
  • William Yuan; Harvard Medical School
  • Paul Avillach; Harvard Medical School
  • Kavishwar B Wagholikar; Massachusetts General Hospital
  • Yuan Luo; Northwestern University
  • - The Consortium for Clinical Characterization of COVID-19 by EHR (4CE); Harvard Medical School
  • Gilbert S. Omenn; University of Michigan
  • Shyam Visweswaran; University of Pittsburgh
  • John H Holmes; University of Pennsylvania Perelman School of Medicine
  • Zongqi Xia; University of Pittsburgh
  • Gabriel A Brat; Harvard Medical School
  • Shawn N Murphy; Massachusetts General Hospital
Preprint en En | PREPRINT-MEDRXIV | ID: ppmedrxiv-22270728
ABSTRACT
BackgroundAdmissions are generally classified as COVID-19 hospitalizations if the patient has a positive SARS-CoV-2 polymerase chain reaction (PCR) test. However, because 35% of SARS-CoV-2 infections are asymptomatic, patients admitted for unrelated indications with an incidentally positive test could be misclassified as a COVID-19 hospitalization. EHR-based studies have been unable to distinguish between a hospitalization specifically for COVID-19 versus an incidental SARS-CoV-2 hospitalization. Although the need to improve classification of COVID-19 disease vs. incidental SARS-CoV-2 is well understood, the magnitude of the problems has only been characterized in small, single-center studies. Furthermore, there have been no peer-reviewed studies evaluating methods for improving classification. ObjectiveThe aims of this study were to first, quantify the frequency of incidental hospitalizations over the first fifteen months of the pandemic in multiple hospital systems in the United States; and second, to apply electronic phenotyping techniques to automatically improve COVID-19 hospitalization classification. MethodsFrom a retrospective EHR-based cohort in four US healthcare systems in Massachusetts, Pennsylvania, and Illinois, a random sample of 1,123 SARS-CoV-2 PCR-positive patients hospitalized between 3/2020-8/2021 was manually chart-reviewed and classified as admitted-with-COVID-19 (incidental) vs. specifically admitted for COVID-19 (for-COVID-19). EHR-based phenotyping was used to find feature sets to filter out incidental admissions. ResultsEHR-based phenotyped feature sets filtered out incidental admissions, which occurred in an average of 26% of hospitalizations (although this varied widely over time, from 0%-75%). The top site-specific feature sets had 79-99% specificity with 62-75% sensitivity, while the best performing across-site feature set had 71-94% specificity with 69-81% sensitivity. ConclusionsA large proportion of SARS-CoV-2 PCR-positive admissions were incidental. Straightforward EHR-based phenotypes differentiated admissions, which is important to assure accurate public health reporting and research.
Licencia
cc_by_nc
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-MEDRXIV Tipo de estudio: Cohort_studies / Experimental_studies / Observational_studies / Prognostic_studies / Rct Idioma: En Año: 2022 Tipo del documento: Preprint
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-MEDRXIV Tipo de estudio: Cohort_studies / Experimental_studies / Observational_studies / Prognostic_studies / Rct Idioma: En Año: 2022 Tipo del documento: Preprint