Abstract：
BACKGROUND: There is limited data on predicting successful chronic total occlusion crossing using primary antegrade wiring (AW).
OBJECTIVE: The aim of this study was to develop and validate a machine learning (ML) prognostic model for successful chronic total occlusion crossing using primary AW.
METHODS: We used data from 12,136 primary AW cases performed between 2012 and 2023 at 48 centers in the PROGRESS CTO registry (Prospective Global Registry for the Study of Chronic Total Occlusion Intervention; NCT02061436) to develop 5 ML models. Hyperparameter tuning was performed for the model with the best performance, and the SHAP (SHapley Additive exPlanations) explainer was implemented to estimate feature importance.
RESULTS: Primary AW was successful in 6,965 cases (57.4%). Extreme gradient boosting was the best performing ML model with an average area under the receiver-operating characteristic curve of 0.775 (± 0.010). After hyperparameter tuning, the average area under the receiver-operating characteristic curve of the extreme gradient boosting model was 0.782 in the training set and 0.780 in the testing set. Among the factors examined, occlusion length had the most significant impact on predicting successful primary AW crossing followed by blunt/no stump, presence of interventional collaterals, vessel diameter, and proximal cap ambiguity. In contrast, aorto-ostial lesion location had the least impact on the outcome. A web-based application for predicting successful primary AW wiring crossing is available online (PROGRESS-CTO website) (https://www.progresscto.org/predict-aw-success).
CONCLUSIONS: We developed an ML model with 14 features and high predictive capacity for successful primary AW in chronic total occlusion percutaneous coronary intervention.
OBJECTIVE: The aim of this study was to develop and validate a machine learning (ML) prognostic model for successful chronic total occlusion crossing using primary AW.
METHODS: We used data from 12,136 primary AW cases performed between 2012 and 2023 at 48 centers in the PROGRESS CTO registry (Prospective Global Registry for the Study of Chronic Total Occlusion Intervention; NCT02061436) to develop 5 ML models. Hyperparameter tuning was performed for the model with the best performance, and the SHAP (SHapley Additive exPlanations) explainer was implemented to estimate feature importance.
RESULTS: Primary AW was successful in 6,965 cases (57.4%). Extreme gradient boosting was the best performing ML model with an average area under the receiver-operating characteristic curve of 0.775 (± 0.010). After hyperparameter tuning, the average area under the receiver-operating characteristic curve of the extreme gradient boosting model was 0.782 in the training set and 0.780 in the testing set. Among the factors examined, occlusion length had the most significant impact on predicting successful primary AW crossing followed by blunt/no stump, presence of interventional collaterals, vessel diameter, and proximal cap ambiguity. In contrast, aorto-ostial lesion location had the least impact on the outcome. A web-based application for predicting successful primary AW wiring crossing is available online (PROGRESS-CTO website) (https://www.progresscto.org/predict-aw-success).
CONCLUSIONS: We developed an ML model with 14 features and high predictive capacity for successful primary AW in chronic total occlusion percutaneous coronary intervention.
摘要：
背景：关于使用主要顺行布线（AW）预测成功的慢性完全闭塞穿越的数据有限。
目的：本研究的目的是开发和验证一种机器学习（ML）预后模型，用于使用原发性AW成功进行慢性完全闭塞穿越。
方法：我们使用了2012年至2023年间在PROGRESSCTO注册（前瞻性全球注册慢性完全闭塞干预研究；NCT02061436）的48个中心进行的12,136例原发性AW病例的数据来开发5个ML模型。对性能最佳的模型进行了超参数调整，并实施SHAP（SHapley加法扩张）解释器来估计特征重要性。
结果：6,965例（57.4%）初级AW成功。极端梯度增强是表现最好的ML模型，接收器工作特性曲线下的平均面积为0.775（±0.010）。超参数调整后，极端梯度增强模型的接收器-工作特征曲线下的平均面积在训练集中为0.782，在测试集中为0.780。在检查的因素中，闭塞长度对预测成功的主要AW穿越最显著的影响，其次是钝/无残端，存在介入性络脉，血管直径,和近端帽模糊。相比之下,主动脉口病变位置对结局的影响最小.用于预测成功的主要AW布线交叉的基于Web的应用程序可在线获得（PROGRESS-CTO网站）（https://www。progresscto.org/prediction-aw-success)。
结论：我们开发了一种具有14个特征和高预测能力的ML模型，用于慢性完全闭塞经皮冠状动脉介入治疗中成功的原发性AW。
目的：本研究的目的是开发和验证一种机器学习（ML）预后模型，用于使用原发性AW成功进行慢性完全闭塞穿越。
方法：我们使用了2012年至2023年间在PROGRESSCTO注册（前瞻性全球注册慢性完全闭塞干预研究；NCT02061436）的48个中心进行的12,136例原发性AW病例的数据来开发5个ML模型。对性能最佳的模型进行了超参数调整，并实施SHAP（SHapley加法扩张）解释器来估计特征重要性。
结果：6,965例（57.4%）初级AW成功。极端梯度增强是表现最好的ML模型，接收器工作特性曲线下的平均面积为0.775（±0.010）。超参数调整后，极端梯度增强模型的接收器-工作特征曲线下的平均面积在训练集中为0.782，在测试集中为0.780。在检查的因素中，闭塞长度对预测成功的主要AW穿越最显著的影响，其次是钝/无残端，存在介入性络脉，血管直径,和近端帽模糊。相比之下,主动脉口病变位置对结局的影响最小.用于预测成功的主要AW布线交叉的基于Web的应用程序可在线获得（PROGRESS-CTO网站）（https://www。progresscto.org/prediction-aw-success)。
结论：我们开发了一种具有14个特征和高预测能力的ML模型，用于慢性完全闭塞经皮冠状动脉介入治疗中成功的原发性AW。
