Validation of diagnosis codes to identify hospitalized COVID-19 patients in health care claims data.

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Health plan claims may provide complete longitudinal data for timely, real-world population-level COVID-19 assessment. However, these data often lack laboratory results, the standard for COVID-19 diagnosis.


We assessed the validity of ICD-10-CM diagnosis codes for identifying patients hospitalized with COVID-19 in U.S. claims databases, compared to linked laboratory results, among six FDA Sentinel System data partners (two large national insurers, four integrated delivery systems) from February 20 - October 17, 2020. We identified patients hospitalized with COVID-19 according to five ICD-10-CM diagnosis code-based algorithms, which included combinations of codes U07.1, B97.29, general coronavirus codes, and diagnosis codes for severe symptoms. We calculated the positive predictive value (PPV) and sensitivity of each algorithm relative to laboratory test results. We stratified results by data source type and across three time periods: February 20-March 31 (Time A), April 1-April 30 (Time B), May 1-October 17 (Time C).


The five algorithms identified between 34,806 - 47,293 patients across the study periods; 23% with known laboratory results contributed to PPV calculations. PPVs were high and similar across algorithms. PPV of U07.1 alone was stable around 93% for integrated delivery systems, but declined over time from 93% to 70% among national insurers. Overall PPV of U07.1 across all data partners was 94.1% (95% CI, 92.3%-95.5%) in Time A and 81.2% (95% CI, 80.1%-82.2%) in Time C. Sensitivity was consistent across algorithms and over time, at 94.9% (95% CI, 94.2%-95.5%).


Our results support the use of code U07.1 to identify hospitalized COVID-19 patients in U.S. claims data. This article is protected by copyright. All rights reserved.

Pharmacoepidemiol Drug Saf
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Validation of diagnosis codes to identify hospitalized COVID-19 patients in health care claims data.
Kluberg SA, Hou L, Dutcher SK, Billings M, Kit B, Toh S, Dublin S, Haynes K, Kline AM, Maiyani M, Pawloski PA, Watson ES, Cocoros NM