Cardiovascular disease risk prediction for people with type 2 diabetes in a population-based cohort and in electronic health record data. Issue 4 (5th December 2020)

• Record Type: Journal Article
• Authors: Szymonifka, Jackie; Conderino, Sarah; Cigolle, Christine; Ha, Jinkyung; Kabeto, Mohammed; Yu, Jaehong; Dodson, John A; Thorpe, Lorna; Blaum, Caroline; Zhong, Judy
• Is Part Of: JAMIA open. Volume 3:Issue 4(2020)
• Page Start: 583
• Page End: 592
• Publication Date: 2020-12-05
• Subjects: electronic health records -- cohort analysis -- risk factors -- cardiovascular disease -- type 2 diabetes mellitus; Medical informatics -- Periodicals; 610.285
• DOI: 10.1093/jamiaopen/ooaa059
• Languages: English
• ISSNs: 2574-2531

Abstract

Abstract: Objective: Electronic health records (EHRs) have become a common data source for clinical risk prediction, offering large sample sizes and frequently sampled metrics. There may be notable differences between hospital-based EHR and traditional cohort samples: EHR data often are not population-representative random samples, even for particular diseases, as they tend to be sicker with higher healthcare utilization, while cohort studies often sample healthier subjects who typically are more likely to participate. We investigate heterogeneities between EHR- and cohort-based inferences including incidence rates, risk factor identifications/quantifications, and absolute risks. Materials and methods: This is a retrospective cohort study of older patients with type 2 diabetes using EHR from New York University Langone Health ambulatory care (NYULH-EHR, years 2009–2017) and from the Health and Retirement Survey (HRS, 1995–2014) to study subsequent cardiovascular disease (CVD) risks. We used the same eligibility criteria, outcome definitions, and demographic covariates/biomarkers in both datasets. We compared subsequent CVD incidence rates, hazard ratios (HRs) of risk factors, and discrimination/calibration performances of CVD risk scores. Results: The estimated subsequent total CVD incidence rate was 37.5 and 90.6 per 1000 person-years since T2DM onset in HRS and NYULH-EHR respectively. HR estimates were comparable between the datasets for most demographic covariates/biomarkers. Common CVD risk scores underestimated observed total CVD risks in NYULH-EHR. Discussion and conclusion: EHR-estimated HRs of demographic and major clinical risk factors for CVD were mostly consistent with the estimates from a national cohort, despite high incidences and absolute risks of total CVD outcome in the EHR samples.