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

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...

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Veröffentlicht in:Journal of endovascular therapy 2024-08, p.15266028241268653
Hauptverfasser: Hou, Xinhuang, Xu, Shuguo, Lin, Tong, Liu, Liang, Guo, Pingfan, Cai, Fanggang, Zhang, Jinchi, Lin, Jun, Lai, Xiaoling, Li, Wanglong, Dai, Yiquan
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creator Hou, Xinhuang
Xu, Shuguo
Lin, Tong
Liu, Liang
Guo, Pingfan
Cai, Fanggang
Zhang, Jinchi
Lin, Jun
Lai, Xiaoling
Li, Wanglong
Dai, Yiquan
description 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
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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&lt;0.001), CTO (72% vs 43%, p&lt;0.001), FP (3.26±0.94 vs 2.66±1.06, p&lt;0.001), and PACSS (2.39±1.40 vs 1.74±1.35, p&lt;0.001) were observed between patients with and without FLD, respectively. The random forest model demonstrated the best performance (validation set area under the curve: 0.82). SHapley Additive exPlanations revealed CTO, PACSS, and FP as the 3 most influential FLD predictors, and the univariate and multivariate analyses confirmed CTO-FP-PACSS classification as an independent FLD predictor (multivariate hazard ratio 4.13; p&lt;0.001). The CTO-FP-PACSS classification system accurately predicted FLD after PTA. This user-friendly system may guide surgical decision-making, helping choose between PTA and additional devices to reduce FLD in FP artery treatment. We utilised machine-learning techniques in conjunction with SHapley Additive exPlanations to develop a clinical classification system that predicts the probability of flow-limiting dissection (FLD) after plain old balloon angioplasty. This classification system categorises lesions into Classes 1-4 based on three factors: chronic total occlusion, femoropopliteal grading, and peripheral artery calcium scoring. Each class demonstrated a different probability of developing FLD. 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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&lt;0.001), CTO (72% vs 43%, p&lt;0.001), FP (3.26±0.94 vs 2.66±1.06, p&lt;0.001), and PACSS (2.39±1.40 vs 1.74±1.35, p&lt;0.001) were observed between patients with and without FLD, respectively. The random forest model demonstrated the best performance (validation set area under the curve: 0.82). SHapley Additive exPlanations revealed CTO, PACSS, and FP as the 3 most influential FLD predictors, and the univariate and multivariate analyses confirmed CTO-FP-PACSS classification as an independent FLD predictor (multivariate hazard ratio 4.13; p&lt;0.001). The CTO-FP-PACSS classification system accurately predicted FLD after PTA. This user-friendly system may guide surgical decision-making, helping choose between PTA and additional devices to reduce FLD in FP artery treatment. We utilised machine-learning techniques in conjunction with SHapley Additive exPlanations to develop a clinical classification system that predicts the probability of flow-limiting dissection (FLD) after plain old balloon angioplasty. This classification system categorises lesions into Classes 1-4 based on three factors: chronic total occlusion, femoropopliteal grading, and peripheral artery calcium scoring. Each class demonstrated a different probability of developing FLD. 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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&lt;0.001), CTO (72% vs 43%, p&lt;0.001), FP (3.26±0.94 vs 2.66±1.06, p&lt;0.001), and PACSS (2.39±1.40 vs 1.74±1.35, p&lt;0.001) were observed between patients with and without FLD, respectively. The random forest model demonstrated the best performance (validation set area under the curve: 0.82). SHapley Additive exPlanations revealed CTO, PACSS, and FP as the 3 most influential FLD predictors, and the univariate and multivariate analyses confirmed CTO-FP-PACSS classification as an independent FLD predictor (multivariate hazard ratio 4.13; p&lt;0.001). The CTO-FP-PACSS classification system accurately predicted FLD after PTA. This user-friendly system may guide surgical decision-making, helping choose between PTA and additional devices to reduce FLD in FP artery treatment. We utilised machine-learning techniques in conjunction with SHapley Additive exPlanations to develop a clinical classification system that predicts the probability of flow-limiting dissection (FLD) after plain old balloon angioplasty. This classification system categorises lesions into Classes 1-4 based on three factors: chronic total occlusion, femoropopliteal grading, and peripheral artery calcium scoring. Each class demonstrated a different probability of developing FLD. This classification system may be valuable for surgeons in their clinical practice, as well as serving as a source of inspiration for other researchers.</abstract><cop>United States</cop><pmid>39108044</pmid><doi>10.1177/15266028241268653</doi><orcidid>https://orcid.org/0009-0005-3738-2473</orcidid></addata></record>
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title Establishing a Classification System for Predicting Flow-Limiting Dissection After Balloon Angioplasty Using Explainable Machine-Learning Models: A Multicenter Retrospective Cohort Study
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