A multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations

Journal article

AbstractDiagnosis 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.

Authors: Khera, R; Mortazavi, BJ; Sangha, V; Warner, F; Patrick Young, H

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: npj Digital Medicine
• Volume: 5
• Issue: 1
• Pages: 27-27
• Article number: 27
• Publication date: 2022-03-08
• DOI: 10.1038/s41746-022-00570-4
• EISSN: 2398-6352
• ISSN: 2398-6352
• Language: English
• Keywords: Coronavirus disease 2019 (COVID-19); Pediatrics; Disease; Test (biology); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Infectious disease (medical specialty); Emergency medicine; Internal medicine; Medicine; Diagnosis code; Epidemiology; Population