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Use of an Electronic Medical Record for the Identification of Research Subjects with Diabetes Mellitus

Russell A. Wilke, MD, PhD, Center for Human Genetics, Marshfield Clinic Research Foundation and Department of Internal Medicine, Marshfield Clinic, 1000 North Oak Avenue, Marshfield, Wisconsin 54449
Richard L. Berg, MS, Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, 1000 North Oak Avenue, Marshfield, Wisconsin 54449
Peggy Peissig, MBA, Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, 1000 North Oak Avenue, Marshfield, Wisconsin 54449
Terrie Kitchner, Center for Human Genetics, Marshfield Clinic Research Foundation, 1000 North Oak Avenue, Marshfield, Wisconsin 54449
Catherine A. McCarty, PhD, Center for Human Genetics, Marshfield Clinic Research Foundation, 1000 North Oak Avenue, Marshfield, Wisconsin 54449
Bozana Sijercic, MD, Department of Internal Medicine, Marshfield Clinic, 1000 North Oak Avenue, Marshfield, Wisconsin 54449
Daniel J. McCarty, PhD, Marshfield Epidemiology Research Center, Marshfield Clinic Research Foundation, 1000 North Oak Avenue, Marshfield, Wisconsin 54449

Reprint Requests: Russell A. Wilke, MD, PhD, Center for Human Genetics, Marshfield Clinic Research Foundation, 1000 North Oak Avenue, Marshfield, WI 54449, Tel: 715-389-3885, Fax: 715-389-3808, E-mail: wilke.russell{at}mcrf.mfldclin.edu

Diabetes mellitus is a rapidly increasing and costly public health problem. Large studies are needed to understand the complex gene-environment interactions that lead to diabetes and its complications. The Marshfield Clinic Personalized Medicine Research Project (PMRP) represents one of the largest population-based DNA biobanks in the United States. As part of an effort to begin phenotyping common diseases within the PMRP, we now report on the construction of a diabetes case-finding algorithm using electronic medical record data from adult subjects aged 50 years living in one of the target PMRP ZIP codes. Based upon diabetic diagnostic codes alone, we observed a false positive case rate ranging from 3.0% (in subjects with the highest glycosylated hemoglobin values) to 44.4% (in subjects with the lowest glycosylated hemoglobin values). We therefore developed an improved case finding algorithm that utilizes diabetic diagnostic codes in combination with clinical laboratory data and medication history. This algorithm yielded an estimated prevalence of 24.2% for diabetes mellitus in adult subjects aged 50 years.

Key Words: Metformin • Natural language processing • Pharmacogenetics • Sulfonylurea

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