Abstract
BackgroundPrevention of diabetes and coronary heart disease (CHD) is possible but identification of at-risk patients for targeting interventions is a challenge in primary care.MethodsWe 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.ResultsAfter 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.ConclusionRisk factor clustering in EHR data identifies primary care patients at increased risk for new diabetes, CHD and higher resource utilization.
Highlights
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
Data from the NHANES III estimated that 24% of the US population over the age of 20 fulfilled the criteria of the metabolic syndrome according to the NCEP-ATPIII definition [3]
Data Source and Study Patients We identified all people receiving regular care from an identified primary care physician in a network of 12 outpatient practices in eastern Massachusetts affiliated with Massachusetts General Hospital (MGH) and the Partners Healthcare System (PHS): the MGH Primary Care Practice-Based Research Network (PBRN)
Summary
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. Metabolic syndrome is a diagnosis that has been proposed to identify patients in whom the clustering of risk factors is associated with increased risk of diabetes and cardiovascular disease [1]. National surveys and large population-based studies have shown that metabolic syndrome is common [3,4] and is associated with substantial health care costs [5]. The concept of risk factor clustering has high potential for identification of at-risk patients, but data from real-world clinical care is needed to understand the actual usefulness of the metabolic syndrome concept as a marker of risk factor clustering and a target for prevention of its adverse consequences
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