Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry
Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry
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DOI：
10.1093/eurheartj/ehz565
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发表时间：
2020-01-14
影响因子：
39.3
通讯作者：
Shaw, Leslee J.
中科院分区：
文献类型：
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作者：
Al'Arefilb, Subhi J.;Maliakal, Gabriel;Shaw, Leslee J.
目的：基于症状的预试概率评分评估稳定性胸痛患者阻塞性冠状动脉疾病（CAD）的可能性具有中等准确性。我们试图开发一种机器学习（ML）模型，利用临床因素和冠状动脉钙化评分（CACS），预测冠状动脉计算机断层扫描血管造影术（CCTA）上阻塞性CAD的存在。方法和结果该研究筛选了35281名参加CONFIRM登记的参与者，这些参与者由于怀疑或先前确定的CAD而接受了>= 64探测器行CCTA评估。使用增强集成算法（XGBoost），将数据分成训练集（80%）和测试集（20%），在训练集上进行10倍交叉验证。评估了（1）ML模型（使用25个临床和人口统计学特征）、（2）ML + CACS、（3）CAD联盟临床评分、（4）CAD联盟临床评分+ CACS和（5）更新的Diamond-Forrester（UDF）评分的性能。研究人群包括13054例患者，其中2380例（18.2%）患有阻塞性CAD（狭窄>= 50%）。与单独ML（AUC为0.773）、CAD联盟临床评分（AUC为0.734）以及CACS（AUC为0.866）和UDF（AUC为0.682）相比，使用CACS的机器学习产生了最佳性能[曲线下面积（AUC）为0.881]，所有比较P < 0.05。CACS、年龄和性别是最高等级的特征。结论ML模型结合临床特征，除了CACS可以准确地估计阻塞性CAD对CCTA的预测试的可能性。在临床实践中，利用这种方法可以改善风险分层，并有助于指导下游管理。
Aims Symptom-based pretest probability scores that estimate the likelihood of obstructive coronary artery disease (CAD) in stable chest pain have moderate accuracy. We sought to develop a machine [earning (ML) model,utilizing clinical factors and the coronary artery calcium score (CACS), to predict the presence of obstructive CAD on coronary computed tomography angiography (CCTA).Methods and results The study screened 35 281 participants enrolled in the CONFIRM registry, who underwent >= 64 detector row CCTA evaluation because of either suspected or previously established CAD. A boosted ensemble algorithm (XGBoost) was used, with data split into a training set (80%) on which 10-fold cross-validation was done and a test set (20%). Performance was assessed of the (1) ML model (using 25 clinical and demographic features), (2) ML + CACS, (3) CAD consortium clinical score, (4) CAD consortium clinical score + CACS, and (5) updated Diamond-Forrester (UDF) score. The study population comprised of 13 054 patients, of whom 2380 (18.2%) had obstructive CAD (>= 50% stenosis). Machine learning with CACS produced the best performance [area under the curve (AUC) of 0.881] compared with ML alone (AUC of 0.773), CAD consortium clinical score (AUC of 0.734), and with CACS (AUC of 0.866) and UDF (AUC of 0.682), P < 0.05 for all comparisons. CACS, age, and gender were the highest ranking features.Conclusion A ML model incorporating clinical features in addition to CACS can accurately estimate the pretest likelihood of obstructive CAD on CCTA. In clinical practice, the utilization of such an approach could improve risk stratification and help guide downstream management.