Division of Cardiovascular Medicine, Department of Medicine, Faculty of Medicine, Center of Excellence in Arrhythmia Research, Chulalongkorn University, Bangkok 10330, Thailand.
Cardiac Center, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok 10330, Thailand.
Med Sci (Basel). 2023 Dec 29;12(1):3. doi: 10.3390/medsci12010003.
The current recommendation for bioprosthetic valve replacement in severe aortic stenosis (AS) is either surgical aortic valve replacement (SAVR) or transcatheter aortic valve replacement (TAVR). We evaluated the performance of a machine learning-based predictive model using existing periprocedural variables for valve replacement modality selection. We analyzed 415 patients in a retrospective longitudinal cohort of adult patients undergoing aortic valve replacement for aortic stenosis. A total of 72 clinical variables including demographic data, patient comorbidities, and preoperative investigation characteristics were collected on each patient. We fit models using LASSO (least absolute shrinkage and selection operator) and decision tree techniques. The accuracy of the prediction on confusion matrix was used to assess model performance. The most predictive independent variable for valve selection by LASSO regression was frailty score. Variables that predict SAVR consisted of low frailty score (value at or below 2) and complex coronary artery diseases (DVD/TVD). Variables that predicted TAVR consisted of high frailty score (at or greater than 6), history of coronary artery bypass surgery (CABG), calcified aorta, and chronic kidney disease (CKD). The LASSO-generated predictive model achieved 98% accuracy on valve replacement modality selection from testing data. The decision tree model consisted of fewer important parameters, namely frailty score, CKD, STS score, age, and history of PCI. The most predictive factor for valve replacement selection was frailty score. The predictive models using different statistical learning methods achieved an excellent concordance predictive accuracy rate of between 93% and 98%.
目前，严重主动脉瓣狭窄（AS）的生物瓣置换推荐方案为外科主动脉瓣置换（SAVR）或经导管主动脉瓣置换（TAVR）。我们评估了一种基于机器学习的预测模型，该模型使用现有的围手术期变量来选择瓣膜置换方式。我们分析了 415 例接受主动脉瓣置换术治疗主动脉瓣狭窄的成年患者的回顾性纵向队列。共收集了 72 例临床变量，包括人口统计学数据、患者合并症和术前检查特征。我们使用 LASSO（最小绝对收缩和选择算子）和决策树技术对模型进行拟合。使用混淆矩阵的预测准确性来评估模型性能。LASSO 回归中瓣膜选择的最具预测性的独立变量是虚弱评分。预测 SAVR 的变量包括低虚弱评分（等于或低于 2）和复杂冠状动脉疾病（DVD/TVD）。预测 TAVR 的变量包括高虚弱评分（等于或大于 6）、冠状动脉旁路移植术（CABG）史、钙化主动脉和慢性肾脏病（CKD）。LASSO 生成的预测模型在测试数据中对瓣膜置换方式的选择达到了 98%的准确率。决策树模型由较少的重要参数组成，即虚弱评分、CKD、STS 评分、年龄和 PCI 史。瓣膜置换选择的最具预测性因素是虚弱评分。使用不同统计学习方法的预测模型的一致性预测准确率在 93%到 98%之间。
