title: Novel screening metric for the identification of at-risk peripheral artery disease patients using administrative claims data.
abstract: 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.
authors.affiliation: Health Advocate, Inc., Westlake Village, CA, USA.
authors.forename: Vishal
authors.initials: V
authors.lastname: Bali
authors.affiliation: Health Advocate, Inc., Westlake Village, CA, USA.
authors.forename: Irina
authors.initials: I
authors.lastname: Yermilov
authors.affiliation: Health Advocate, Inc., Westlake Village, CA, USA.
authors.forename: Kayla
authors.initials: K
authors.lastname: Coutts
authors.affiliation: Health Advocate, Inc., Westlake Village, CA, USA University of California, Los Angeles, School of Public Health, Los Angeles, CA, USA legorreta@ucla.edu.
authors.forename: Antonio P
authors.initials: AP
authors.lastname: Legorreta
mesh_terms: D000067575:Administrative Claims, Healthcare; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D016009:Chi-Square Distribution; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008403:Mass Screening / Q000379:methods*; D006278:Medicare; D008875:Middle Aged; D015999:Multivariate Analysis; D016017:Odds Ratio; D058729:Peripheral Arterial Disease / Q000175:diagnosis / Q000453:epidemiology*; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D014481:United States / Q000453:epidemiology; D057188:Workflow
keywords: claims data; peripheral artery disease (PAD); prediction; risk factors for PAD; screening metric; screening model for PAD; vascular