High concordance between chart review adjudication and electronic medical record data to identify prevalent and incident diabetes mellitus among persons with and without HIV.

McGinnis KA, Justice AC, Bailin S, Wellons M, Freiberg M, Koethe JR. High concordance between chart review adjudication and electronic medical record data to identify prevalent and incident diabetes mellitus among persons with and without HIV. Pharmacoepidemiology and drug safety. 2020 Nov 1; 29(11):1432-1439.

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Abstract:

BACKGROUND: Electronic medical records (EMR) represent a rich source of data, but the value of EMR for health research relies on accurate ascertainment of clinical diagnoses. Identifying diabetes in EMR is complicated by the variety of accepted diagnostic criteria, some of which can be confounded by conditions such as HIV infection. We compared EMR-based criteria for estimating diabetes prevalence and incidence in the Veterans Health Administration (VHA), overall and by HIV status, against physician chart review and adjudication. RESEARCH DESIGN AND METHODS: We used laboratory values (serum glucose and hemoglobin A1c% [HbA1c]), ICD-9 codes, and medication records from the United States Veterans Aging Cohort Study Biomarker Cohort to identify veterans with any indication of diabetes in the EMR for subsequent physician adjudication. Sensitivity, specificity, PPV, NPV, and kappa statistics were used to evaluate agreement of EMR-based diabetes diagnoses with chart review adjudicated diagnoses. RESULTS: EMR entries were reviewed for 1546 persons with HIV (PWH) and 843 HIV-negative participants through 2015. Agreement was at least moderate overall (kappa? = 0.42) for all pre-specified measures and among PWH vs HIV-negative, and African-American vs white sub-groups. Having at least one HbA1c = 6.5% provided substantial agreement with chart adjudication for prevalent and incident diabetes (kappa = 0.89 and 0.73). CONCLUSIONS: Identification of those with diabetes nationally within the VHA can be used in future studies to evaluate treatments, health outcomes, and adjust for diabetes in epidemiologic studies. Our methodology may provide insights for other organizations seeking to use EMR data for accurate determination of diabetes.