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fying Patients with Diabetes into Clinically Relevant Groups by Combination of Chronic Conditions to Identify Gaps in Quality of Care

Objective: To find clinically relevant combinations of chronic conditions among patients with diabetes and to examine their relationships with six diabetes quality metrics.

Data Sources/Study Setting: Twenty-nine thousand five hundred and sixty-two adult patients with diabetes seen at eight Midwestern U.S. health systems during 2010–2011.

Study Design: We retrospectively evaluated the relationship between six diabetes quality metrics and patients' combinations of chronic conditions. We analyzed 12 conditions that were concordant with diabetes care to define five mutually exclusive combinations of conditions (“classes”) based on condition co-occurrence. We used logistic regression to quantify the relationship between condition classes and quality metrics, adjusted for patient demographics and utilization.

Data Collection: We extracted electronic health record data using a standardized algorithm.

Principal Findings: We found the following condition classes: severe cardiac, cardiac, noncardiac vascular, risk factors, and no concordant comorbidities. Adjusted odds ratios and 95 percent confidence intervals for glycemic control were, respectively, 1.95 (1.7–2.2), 1.6 (1.4–1.9), 1.3 (1.2–1.5), and 1.3 (1.2–1.4) compared to the class with no comorbidities. Results showed similar patterns for other metrics.

Conclusions: Patients had distinct quality metric achievement by condition class, and those in less severe classes were less likely to achieve diabetes metrics.

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