Andrew D Wiese1, Christianne L Roumie2,3,4, John B Buse5, Herodes Guzman5, Robert Bradford5, Emily Zalimeni5, Patricia Knoepp5, Heather L Morris6, William T Donahoo7, Nada Fanous7, Britany F Epstein7, Bonnie L Katalenich8, Sujata G Ayala9, Megan M Cook9, Katherine J Worley10, Katherine N Bachmann11,12,13, Carlos G Grijalva1,4, Russell L Rothman1,2,3, Rosette J Chakkalakal2. 1. Department of Health Policy, Vanderbilt University Medical Center, Nashville, TN, USA. 2. Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA. 3. Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA. 4. Veterans Health Administration-Tennessee Valley Healthcare System, Geriatric Research Education Clinical Center (GRECC), Nashville, TN, USA. 5. Department of Medicine, University of North Carolina, Chapel Hill, NC, USA. 6. Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA. 7. Department of Medicine, University of Florida, Gainesville, FL, USA. 8. LA CaTS Clinical Translational Unit, Tulane University School of Medicine, Tulane, LA, USA. 9. Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, USA. 10. Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA. 11. Veterans Health Administration-Tennessee Valley Healthcare System, CSR&D, Nashville, TN, USA. 12. Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University Medical Center, Nashville, TN, USA. 13. Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
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 t0 . 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.
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 t0 . 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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