Williams Michelle Claire, Guimaraes Alan R M, Jiang Muchen, Kwieciński Jacek, Weir-McCall Jonathan R, Adamson Philip D, Mills Nicholas L, Roditi Giles H, van Beek Edwin J R, Nicol Edward, Berman Daniel S, Slomka Piotr J, Dweck Marc R, Newby David E, Dey Damini
British Heart Foundation Centre for Research Excellence, The University of Edinburgh, Edinburgh, Scotland, UK
British Heart Foundation Centre for Research Excellence, The University of Edinburgh, Edinburgh, Scotland, UK.
Open Heart. 2025 Sep 1;12(2):e003162. doi: 10.1136/openhrt-2025-003162.
Machine learning based on clinical characteristics has the potential to predict coronary CT angiography (CCTA) findings and help guide resource utilisation.
From the SCOT-HEART (Scottish Computed Tomography of the HEART) trial, data from 1769 patients was used to train and to test machine learning models (XGBoost, 10-fold cross validation, grid search hyperparameter selection). Two models were separately generated to predict the presence of coronary artery disease (CAD) and an increased burden of low-attenuation coronary artery plaque (LAP) using symptoms, demographic and clinical characteristics, electrocardiography and exercise tolerance testing (ETT).
Machine learning predicted the presence of CAD on CCTA (area under the curve (AUC) 0.80, 95% CI 0.74 to 0.85) better than the 10-year cardiovascular risk score alone (AUC 0.75, 95% CI 0.70, 0.81, p=0.004). The most important features in this model were the 10-year cardiovascular risk score, age, sex, total cholesterol and an abnormal ETT. In contrast, the second model used to predict an increased LAP burden performed similarly to the 10-year cardiovascular risk score (AUC 0.75, 95% CI 0.70 to 0.80 vs AUC 0.72, 95% CI 0.66 to 0.77, p=0.08) with the most important features being the 10-year cardiovascular risk score, age, body mass index and total and high-density lipoprotein cholesterol concentrations.
Machine learning models can improve prediction of the presence of CAD on CCTA, over the standard cardiovascular risk score. However, it was not possible to improve the prediction of an increased LAP burden based on clinical factors alone.
基于临床特征的机器学习有潜力预测冠状动脉CT血管造影（CCTA）结果并有助于指导资源利用。
从SCOT-HEART（苏格兰心脏计算机断层扫描）试验中，使用1769名患者的数据来训练和测试机器学习模型（XGBoost，10折交叉验证，网格搜索超参数选择）。分别生成两个模型，使用症状、人口统计学和临床特征、心电图和运动耐量测试（ETT）来预测冠状动脉疾病（CAD）的存在以及低衰减冠状动脉斑块（LAP）负担的增加。
机器学习预测CCTA上CAD的存在（曲线下面积（AUC）0.80，95%CI 0.74至0.85）优于单独的10年心血管风险评分（AUC 0.75，95%CI 0.70，0.81，p=0.004）。该模型中最重要的特征是10年心血管风险评分、年龄、性别、总胆固醇和异常的ETT。相比之下，用于预测LAP负担增加的第二个模型的表现与10年心血管风险评分相似（AUC 0.75，95%CI 0.70至0.80 vs AUC 0.72，95%CI 0.66至0.77，p=0.08），最重要的特征是10年心血管风险评分、年龄、体重指数以及总胆固醇和高密度脂蛋白胆固醇浓度。
机器学习模型在预测CCTA上CAD的存在方面优于标准心血管风险评分。然而，仅基于临床因素无法改善对LAP负担增加的预测。
