Development and Validation of Claims-based Algorithms for Identifying Hospitalized Patients With COVID-19 and Their...

This study aimed to develop and validate claims-based algorithms for identifying hospitalized patients with coronavirus disease 2019 (COVID-19) and disease severity.

We used claims data including all patients at the National Center for Global and Medicine Hospital between January 1, 2020, and December 31, 2021. The claims-based algorithms for three statuses with COVID-19 (hospitalizations, moderate or higher status, and severe status) were developed using diagnosis codes (International Classification of Diseases, 10^th revision code: U07.1, B34.2) and relevant medical procedure code. True cases were determined using the COVID-19 inpatient registry and electronic health records. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each algorithm at 6-month intervals.

Of the 75,711 total patients, the number of true cases was 1,192 for hospitalizations, 622 for moderate or higher status, and 55 for severe status. The diagnosis code-only algorithm for hospitalization had sensitivities 90.4% to 94.9% and PPVs 9.3% to 19.4%. Among the algorithms consisting of both diagnosis codes and procedure codes, high sensitivity and PPV were observed during the following periods: 93.9% and 97.1% for hospitalization (January-June 2021), 90.4% and 87.5% for moderate or higher status (July-December 2021), and 92.3% and 85.7% for severe status (July-December 2020), respectively. Almost all algorithms had specificities and NPVs of approximately 99%.

The diagnosis code-only algorithm for COVID-19 hospitalization showed low validity throughout the study period. The algorithms for hospitalizations, moderate or higher status, and severe status with COVID-19, consisting of both diagnosis codes and procedure codes, showed high validity in some periods.