Validation of ICD-10 Codes to Distinguish Between Claudication and Chronic Limb-Threatening Ischemia in Patients Undergoing Peripheral Vascular Intervention Using Medicare-Matched Registry Data.
Academic Article
Overview
abstract
BACKGROUND: The accuracy of contemporary administrative claims codes to discriminate between different phenotypes of peripheral artery disease is not well defined. We aimed to validate a predefined set of International Classification of Diseases, Tenth Revision, codes used to distinguish between claudication and chronic limb-threatening ischemia (CLTI) and to optimize their diagnostic accuracy using a supervised machine-learning approach. METHODS: We included all patients who underwent a peripheral vascular intervention for claudication or CLTI in the US Medicare-matched VQI-VISION (Vascular Quality Initiative Vascular Implant Surveillance and Interventional Outcomes Network) registry database between January 2016 and December 2019. Gold standard claudication and CLTI diagnoses were determined using VQI (Vascular Quality Initiative) registry data. These diagnoses were compared with a predetermined set of International Classification of Diseases, Tenth Revision, codes in the Medicare-matched data set. We used traditional logistic regression modeling and 6 machine-learning models to distinguish claudication from CLTI. We evaluated the sensitivity, specificity, total agreement, and area under the curve for all models, implementing grid search cross-validation to boost machine-learning model performance. RESULTS: Of 54 180 patients who underwent a peripheral vascular intervention (mean age, 71.9±10.0 years; 41.0% female; 74.2 non-Hispanic White), 20 769 (38.3%) had claudication and 33 411 (61.7%) had CLTI per gold standard registry definitions. The predefined set of International Classification of Diseases, Tenth Revision, codes had high sensitivity (80.9%), specificity (81.9%), and total agreement (81.3%) for distinguishing claudication versus CLTI. Traditional logistic regression improved sensitivity to 96.2%, but with a substantial drop in specificity (41.8%) and an area under the curve of 0.785. Of the machine-learning models, gradient boosting classifier performed the best (area under the curve, 0.892), improving sensitivity to 88.6% and total agreement to 84.2% with minimal drop in specificity (77.1%). CONCLUSIONS: International Classification of Diseases, Tenth Revision, codes can be used to discriminate between claudication and CLTI in claims data. Our defined set of claims codes can be used by investigators to accurately distinguish between these 2 peripheral artery disease phenotypes.
