Performance of a computable phenotype for identification of patients with diabetes within PCORnet: The Patient‐Centered Clinical Research Network

Abstract

Abstract Purpose PCORnet, the National Patient‐Centered Clinical Research Network, represents an innovative system for the conduct of observational and pragmatic studies. We describe the identification and validation of a retrospective cohort of patients with type 2 diabetes (T2DM) from four PCORnet sites. Methods We adapted existing computable phenotypes (CP) for the identification of patients with T2DM and evaluated their performance across four PCORnet sites (2012‐2016). Patients entered the cohort on the earliest date they met one of three CP categories: (CP1) coded T2DM diagnosis (ICD‐9/ICD‐10) and an antidiabetic prescription, (CP2) diagnosis and glycosylated hemoglobin (HbA1c) ≥6.5%, or (CP3) an antidiabetic prescription and HbA1c ≥6.5%. We required evidence of health care utilization in each of the 2 prior years for each patient, as we also developed an incident T2DM CP to identify the subset of patients without documentation of T2DM in the 365 days before t 0 . Among a systematic sample of patients, we calculated the positive predictive value (PPV) for the T2DM CP and incident‐T2DM CP using electronic health record (EHR) review as reference. Results The CP identified 50 657 patients with T2DM. The PPV of patients randomly selected for validation was 96.2% ( n = 1572; CI:95.1‐97.0) and was consistently high across sites. The PPV for the incident‐T2DM CP was 5.8% (CI:4.5‐7.5). Conclusions The T2DM CP accurately and efficiently identified patients with T2DM across multiple sites that participate in PCORnet, although the incident T2DM CP requires further study. PCORnet is a valuable data source for future epidemiological and comparative effectiveness research among patients with T2DM.

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