Establishing a Classification System for Predicting Flow-Limiting Dissection After Balloon Angioplasty Using Explainable Machine-Learning Models: A Multicenter Retrospective Cohort Study

Percutaneous transluminal angioplasty (PTA) is the primary method for treatment in peripheral arterial disease. However, some patients experience flow-limiting dissection (FLD) after PTA. We utilized machine learning and SHapley Additive exPlanations to identify and optimize a classification system to predict FLD after PTA. This was a multi-center, retrospective, cohort study. The cohort comprised 407 patients who underwent treatment of the femoropopliteal (FP) arteries in 3 institutions between January 2021 and June 2023. Preoperative computed tomography angiography images were evaluated to identify FP artery grading, chronic total occlusion (CTO), and vessel calcification (peripheral artery calcium scoring system [PACSS]). After PTA, FLD was identified by angiography. We trained and validated 6 machine-learning models to estimate FLD occurrence after PTA, and the best model was selected. Then, the sum of the Shapley values for each of CTO, FP, and PACSS was calculated for each patient to produce the CTO-FP-PACSS value. The CTO-FP-PACSS classification system was used to classify the patients into classes 1 to 4. Univariate and multivariate analyses were performed to validate the effectiveness of the CTO-FP-PACSS classification system for predicting FLD. Overall, 407 patients were analyzed, comprising 189 patients with FLD and 218 patients without FLD. Differences in sex (71% males vs 54% males, p