Identifying Primary Care Patients at Risk for Future Diabetes and Cardiovascular Disease Using Electronic Health Records

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Identifying Primary Care Patients at Risk for Future Diabetes and Cardiovascular Disease Using Electronic Health Records

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Title: Identifying Primary Care Patients at Risk for Future Diabetes and Cardiovascular Disease Using Electronic Health Records
Author: Hivert, Marie-France; Shrader, Peter; Grant, Richard William; Meigs, James Benjamin

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Citation: Hivert, Marie-France, Richard W. Grant, Peter Shrader, and James B. Meigs. 2009. Identifying primary care patients at risk for future diabetes and cardiovascular disease using electronic health records. BMC Health Services Research 9:170.
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Abstract: Background: Prevention of diabetes and coronary heart disease (CHD) is possible but identification of at-risk patients for targeting interventions is a challenge in primary care. Methods: We analyzed electronic health record (EHR) data for 122,715 patients from 12 primary care practices. We defined patients with risk factor clustering using metabolic syndrome (MetS) characteristics defined by NCEP-ATPIII criteria; if missing, we used surrogate characteristics, and validated this approach by directly measuring risk factors in a subset of 154 patients. For subjects with at least 3 of 5 MetS criteria measured at baseline (2003-2004), we defined 3 categories: No MetS (0 criteria); At-risk-for MetS (1-2 criteria); and MetS (≥ 3 criteria). We examined new diabetes and CHD incidence, and resource utilization over the subsequent 3-year period (2005-2007) using age-sex-adjusted regression models to compare outcomes by MetS category. Results: After excluding patients with diabetes/CHD at baseline, 78,293 patients were eligible for analysis. EHR-defined MetS had 73% sensitivity and 91% specificity for directly measured MetS. Diabetes incidence was 1.4% in No MetS; 4.0% in At-risk-for MetS; and 11.0% in MetS (p < 0.0001 for trend; adjusted OR MetS vs No MetS = 6.86 [6.06-7.76]); CHD incidence was 3.2%, 5.3%, and 6.4% respectively (p < 0.0001 for trend; adjusted OR = 1.42 [1.25-1.62]). Costs and resource utilization increased across categories (p < 0.0001 for trends). Results were similar analyzing individuals with all five criteria not missing, or defining MetS as ≥ 2 criteria present. Conclusion: Risk factor clustering in EHR data identifies primary care patients at increased risk for new diabetes, CHD and higher resource utilization.
Published Version: doi:10.1186/1472-6963-9-170
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2753330/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:4621708

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