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Distinguishing Admissions Specifically for COVID-19 From Incidental SARS-CoV-2 Admissions: National Retrospective Electronic Health Record Study.
Klann, Jeffrey G; Strasser, Zachary H; Hutch, Meghan R; Kennedy, Chris J; Marwaha, Jayson S; Morris, Michele; Samayamuthu, Malarkodi Jebathilagam; Pfaff, Ashley C; Estiri, Hossein; South, Andrew M; Weber, Griffin M; Yuan, William; Avillach, Paul; Wagholikar, Kavishwar B; Luo, Yuan; Omenn, Gilbert S; Visweswaran, Shyam; Holmes, John H; Xia, Zongqi; Brat, Gabriel A; Murphy, Shawn N.
  • Klann JG; Laboratory of Computer Science, Department of Medicine, Massachusetts General Hospital, Boston, MA, United States.
  • Strasser ZH; Laboratory of Computer Science, Department of Medicine, Massachusetts General Hospital, Boston, MA, United States.
  • Hutch MR; Department of Preventive Medicine, Northwestern University, Chicago, IL, United States.
  • Kennedy CJ; Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, United States.
  • Marwaha JS; Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.
  • Morris M; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.
  • Samayamuthu MJ; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.
  • Pfaff AC; Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.
  • Estiri H; Laboratory of Computer Science, Department of Medicine, Massachusetts General Hospital, Boston, MA, United States.
  • South AM; Section of Nephrology, Department of Pediatrics, Brenner Children's, Wake Forest School of Medicine, Winston Salem, NC, United States.
  • Weber GM; see Acknowledgments, .
  • Yuan W; see Acknowledgments, .
  • Avillach P; see Acknowledgments, .
  • Wagholikar KB; Laboratory of Computer Science, Department of Medicine, Massachusetts General Hospital, Boston, MA, United States.
  • Luo Y; Department of Preventive Medicine, Northwestern University, Chicago, IL, United States.
  • Omenn GS; Center for Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, United States.
  • Visweswaran S; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.
  • Holmes JH; Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
  • Xia Z; Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States.
  • Brat GA; see Acknowledgments, .
  • Murphy SN; Department of Neurology, Massachusetts General Hospital, Boston, MA, United States.
J Med Internet Res ; 24(5): e37931, 2022 05 18.
Article in English | MEDLINE | ID: covidwho-1862520
ABSTRACT

BACKGROUND:

Admissions 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. Electronic health record (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 versus 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.

OBJECTIVE:

The aims of this study are to, first, quantify the frequency of incidental hospitalizations over the first 15 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.

METHODS:

From a retrospective EHR-based cohort in 4 US health care systems in Massachusetts, Pennsylvania, and Illinois, a random sample of 1123 SARS-CoV-2 PCR-positive patients hospitalized from March 2020 to August 2021 was manually chart-reviewed and classified as "admitted with COVID-19" (incidental) versus specifically admitted for COVID-19 ("for COVID-19"). EHR-based phenotyping was used to find feature sets to filter out incidental admissions.

RESULTS:

EHR-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% to 75%). The top site-specific feature sets had 79%-99% specificity with 62%-75% sensitivity, while the best-performing across-site feature sets had 71%-94% specificity with 69%-81% sensitivity.

CONCLUSIONS:

A 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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Cohort study / Diagnostic study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2022 Document Type: Article Affiliation country: 37931

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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Cohort study / Diagnostic study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2022 Document Type: Article Affiliation country: 37931