Department of Surgery University of Toronto Canada.
Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto University of Toronto Canada.
J Am Heart Assoc. 2023 Oct 17;12(20):e030508. doi: 10.1161/JAHA.123.030508. Epub 2023 Oct 7.
Background Carotid endarterectomy (CEA) is a major vascular operation for stroke prevention that carries significant perioperative risks; however, outcome prediction tools remain limited. The authors developed machine learning algorithms to predict outcomes following CEA. Methods and Results The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent CEA between 2011 and 2021. Input features included 36 preoperative demographic/clinical variables. The primary outcome was 30-day major adverse cardiovascular events (composite of stroke, myocardial infarction, or death). The data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using preoperative features. The primary metric for evaluating model performance was area under the receiver operating characteristic curve. Model robustness was evaluated with calibration plot and Brier score. Overall, 38 853 patients underwent CEA during the study period. Thirty-day major adverse cardiovascular events occurred in 1683 (4.3%) patients. The best performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve of 0.91 (95% CI, 0.90-0.92). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.62 (95% CI, 0.60-0.64), and existing tools in the literature demonstrate area under the receiver operating characteristic curve values ranging from 0.58 to 0.74. The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.02. The strongest predictive feature in our algorithm was carotid symptom status. Conclusions The machine learning models accurately predicted 30-day outcomes following CEA using preoperative data and performed better than existing tools. They have potential for important utility in guiding risk-mitigation strategies to improve outcomes for patients being considered for CEA.
颈动脉内膜切除术（CEA）是预防中风的主要血管手术，但围手术期风险较大；然而，预后预测工具仍然有限。作者开发了机器学习算法来预测 CEA 后的结果。
使用国家手术质量改进计划靶向血管数据库，确定 2011 年至 2021 年间接受 CEA 的患者。输入特征包括 36 项术前人口统计学/临床变量。主要结局为 30 天主要不良心血管事件（中风、心肌梗死或死亡的复合结局）。数据分为训练（70%）和测试（30%）集。使用 10 折交叉验证，使用术前特征训练 6 种机器学习模型。评估模型性能的主要指标是接收者操作特征曲线下面积。使用校准图和 Brier 评分评估模型的稳健性。
研究期间共有 38853 例患者接受了 CEA。30 天主要不良心血管事件发生在 1683 例（4.3%）患者中。表现最佳的预测模型是 XGBoost，其接收者操作特征曲线下面积为 0.91（95%置信区间，0.90-0.92）。相比之下，逻辑回归的接收者操作特征曲线下面积为 0.62（95%置信区间，0.60-0.64），文献中现有的工具显示的接收者操作特征曲线下面积值范围为 0.58 至 0.74。校准图显示预测和观察到的事件概率之间有较好的一致性，Brier 得分为 0.02。我们的算法中最强的预测特征是颈动脉症状状态。
机器学习模型使用术前数据准确预测了 CEA 后的 30 天结局，其表现优于现有工具。它们有可能在指导风险缓解策略方面具有重要的实用价值，以改善考虑接受 CEA 的患者的结局。
