Identifying primary care patients at risk for future diabetes and cardiovascular disease using electronic health records

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 (>or= 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 >or= 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.