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A multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations.
Khera, Rohan; Mortazavi, Bobak J; Sangha, Veer; Warner, Frederick; Patrick Young, H; Ross, Joseph S; Shah, Nilay D; Theel, Elitza S; Jenkinson, William G; Knepper, Camille; Wang, Karen; Peaper, David; Martinello, Richard A; Brandt, Cynthia A; Lin, Zhenqiu; Ko, Albert I; Krumholz, Harlan M; Pollock, Benjamin D; Schulz, Wade L.
  • Khera R; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Mortazavi BJ; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA.
  • Sangha V; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA.
  • Warner F; Computer Science & Engineering, Texas A&M University, College Station, TX, USA.
  • Patrick Young H; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Ross JS; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA.
  • Shah ND; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA.
  • Theel ES; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA.
  • Jenkinson WG; Section of General Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Knepper C; Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA.
  • Wang K; Division of Health Care Delivery Research, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.
  • Peaper D; Division of Clinical Microbiology, Mayo Clinic Rochester, Rochester, MN, USA.
  • Martinello RA; Department of Quality, Experience, and Affordability, Mayo Clinic, Rochester, MN, USA.
  • Brandt CA; Division of Health Care Delivery Research, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.
  • Lin Z; Equity Research and Innovation Center, General Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Ko AI; Center for Medical Informatics, Yale School of Medicine, New Haven, CT, USA.
  • Krumholz HM; Center for Medical Informatics, Yale School of Medicine, New Haven, CT, USA.
  • Pollock BD; Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Schulz WL; Section of Infectious Diseases, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
NPJ Digit Med ; 5(1): 27, 2022 Mar 08.
Article in English | MEDLINE | ID: covidwho-1735293
ABSTRACT
Diagnosis codes are used to study SARS-CoV2 infections and COVID-19 hospitalizations in administrative and electronic health record (EHR) data. Using EHR data (April 2020-March 2021) at the Yale-New Haven Health System and the three hospital systems of the Mayo Clinic, computable phenotype definitions based on ICD-10 diagnosis of COVID-19 (U07.1) were evaluated against positive SARS-CoV-2 PCR or antigen tests. We included 69,423 patients at Yale and 75,748 at Mayo Clinic with either a diagnosis code or a positive SARS-CoV-2 test. The precision and recall of a COVID-19 diagnosis for a positive test were 68.8% and 83.3%, respectively, at Yale, with higher precision (95%) and lower recall (63.5%) at Mayo Clinic, varying between 59.2% in Rochester to 97.3% in Arizona. For hospitalizations with a principal COVID-19 diagnosis, 94.8% at Yale and 80.5% at Mayo Clinic had an associated positive laboratory test, with secondary diagnosis of COVID-19 identifying additional patients. These patients had a twofold higher inhospital mortality than based on principal diagnosis. Standardization of coding practices is needed before the use of diagnosis codes in clinical research and epidemiological surveillance of COVID-19.

Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study Language: English Journal: NPJ Digit Med Year: 2022 Document Type: Article Affiliation country: S41746-022-00570-4

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study Language: English Journal: NPJ Digit Med Year: 2022 Document Type: Article Affiliation country: S41746-022-00570-4