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Despite high morbidity and mortality associated with peripheral artery disease (PAD), it remains under-diagnosed and under-treated. The objective of this study was to develop a screening metric to identify undiagnosed patients at high risk of developing PAD using administrative data. Commercial claims data from 2010 to 2012 were utilized to develop and internally validate a PAD screening metric. Medicare data were used for external validation. The study population included adults, aged 30 years or older, with new cases of PAD identified using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis/procedure codes or the Healthcare Common Procedure Coding System (HCPCS) codes. Multivariate logistic regression was conducted to determine PAD risk factors used in the development of the screening metric for the identification of at-risk PAD patients. The cumulative incidence of PAD was 6.6%. Sex, age, congestive heart failure, hypertension, chronic renal insufficiency, stroke, diabetes, acute myocardial infarction, transient ischemic attack, hyperlipidemia, and angina were significant risk factors for PAD. A cut-off score of ⩾20 yielded sensitivity, specificity, positive predictive value, negative predictive value, and c-statistics of 83.5%, 60.0%, 12.8%, 98.1%, and 0.78, respectively. By identifying patients at high risk for developing PAD using only administrative data, the use of the current pre-screening metric could reduce the number of diagnostic tests, while still capturing those patients with undiagnosed PAD.

Novel screening metric for the identification of at-risk peripheral artery disease patients using administrative claims data