Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, Chaoyang District, Beijing, China.
Department of Cardiology, 1st Affiliated Hospital of Dalian Medical University, Dalian, China.
BMC Cardiovasc Disord. 2022 Dec 26;22(1):569. doi: 10.1186/s12872-022-03022-9.
We investigated the predictive value of clinical factors combined with coronary artery calcium (CAC) score based on a machine learning method for obstructive coronary heart disease (CAD) on coronary computed tomography angiography (CCTA) in individuals with atypical chest pain.
The study included data from 1,906 individuals undergoing CCTA and CAC scanning because of atypical chest pain and without evidence for the previous CAD. A total of 63 variables including traditional cardiovascular risk factors, CAC score, laboratory results, and imaging parameters were used to build the Random forests (RF) model. Among all the participants, 70% were randomly selected to train the models on which fivefold cross-validation was done and the remaining 30% were regarded as a validation set. The prediction performance of the RF model was compared with two traditional logistic regression (LR) models.
The incidence of obstructive CAD was 16.4%. The area under the receiver operator characteristic (ROC) for obstructive CAD of the RF model was 0.841 (95% CI 0.820-0.860), the CACS model was 0.746 (95% CI 0.722-0.769), and the clinical model was 0.810 (95% CI 0.788-0.831). The RF model was significantly superior to the other two models (p < 0.05). Furthermore, the calibration curve and Hosmer-Lemeshow test showed that the RF model had good classification performance (p = 0.556). CAC score, age, glucose, homocysteine, and neutrophil were the top five important variables in the RF model.
RF model was superior to the traditional models in the prediction of obstructive CAD. In clinical practice, the RF model may improve risk stratification and optimize individual management.
我们研究了基于机器学习的临床因素联合冠状动脉钙（CAC）评分对因非典型胸痛且无既往冠心病证据而行冠状动脉计算机断层扫描血管造影（CCTA）的个体中阻塞性冠心病（CAD）的预测价值。
本研究纳入了 1906 名因非典型胸痛且无既往 CAD 证据而行 CCTA 和 CAC 扫描的患者。共使用了 63 个变量，包括传统心血管危险因素、CAC 评分、实验室结果和影像学参数，以建立随机森林（RF）模型。在所有参与者中，70%被随机选择用于模型训练，并进行了五重交叉验证，其余 30%作为验证集。比较了 RF 模型与两种传统的逻辑回归（LR）模型的预测性能。
阻塞性 CAD 的发生率为 16.4%。RF 模型对阻塞性 CAD 的受试者工作特征（ROC）曲线下面积为 0.841（95%CI 0.820-0.860），CACS 模型为 0.746（95%CI 0.722-0.769），临床模型为 0.810（95%CI 0.788-0.831）。RF 模型明显优于其他两种模型（p<0.05）。此外，校准曲线和 Hosmer-Lemeshow 检验表明 RF 模型具有良好的分类性能（p=0.556）。CAC 评分、年龄、血糖、同型半胱氨酸和中性粒细胞是 RF 模型中最重要的前五个变量。
RF 模型在预测阻塞性 CAD 方面优于传统模型。在临床实践中，RF 模型可能会改善风险分层并优化个体管理。
