Center for Coronary Artery Disease, Beijing Anzhen Hospital of Capital Medical University, Beijing, China.
Beijing Anzhen Hospital of Capital Medical University and Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China.
Eur Radiol. 2024 Sep;34(9):5633-5643. doi: 10.1007/s00330-024-10629-3. Epub 2024 Feb 10.
Utilising readily available clinical variables, we aimed to develop and validate a novel machine learning (ML) model to predict severe coronary calcification, and further assessed its prognostic significance.
This retrospective study enrolled patients who underwent coronary CT angiography and subsequent invasive coronary angiography. Multiple ML algorithms were used to train the models for predicting severe coronary calcification (cardiac CT-measured coronary artery calcium [CT-CAC] score ≥ 400). The ML-based CAC (ML-CAC) score derived from the ML predictive probability was stratified into quartiles for prognostic analysis. The primary endpoint was a composite of all-cause death, nonfatal myocardial infarction, or nonfatal stroke.
Overall, 5785 patients were divided into training (80%) and test sets (20%). For clinical practicability, we selected the nine-feature support vector machine model with good and satisfactory performance regarding both discrimination and calibration based on five repetitions of the 10-fold cross-validation in the training set (mean AUC = 0.715, Brier score = 0.202), and based on the test in the test set (AUC = 0.753, Brier score = 0.191). In the test set cohort (n = 1137), the primary endpoint was observed in 50 (4.4%) patients during a median 2.8 years' follow-up. The ML-CAC system was significantly associated with an increased risk of the primary endpoint (adjusted hazard ratio for trend 2.26, 95% CI 1.35-3.79, p = 0.002). There was no significant difference in the prognostic value between the ML-CAC and CT-CAC systems (C-index, 0.67 vs. 0.69; p = 0.618).
ML-CAC score predicted from clinical variables can serve as a novel prognostic indicator in patients referred for invasive coronary angiography.
In patients referred for invasive coronary angiography who have not undergone preoperative CT-measured coronary artery calcium scoring, machine learning-based coronary artery calcium score assessment can serve as an alternative for predicting the prognosis.
• The coronary artery calcium (CAC) score, a solid prognostic indicator, can be predicted using non-CT methods. • We developed a machine learning (ML)-CAC model utilising nine clinical variables to predict severe coronary calcification. • The ML-CAC system offers significant prognostic value in patients referred for invasive coronary angiography.
利用现有临床变量，我们旨在开发和验证一种新的机器学习（ML）模型来预测严重冠状动脉钙化，并进一步评估其预后意义。
本回顾性研究纳入了接受冠状动脉 CT 血管造影和随后的冠状动脉造影的患者。使用多种 ML 算法来训练预测严重冠状动脉钙化（心脏 CT 测量的冠状动脉钙 [CT-CAC] 评分≥400）的模型。从 ML 预测概率中得出的基于 ML 的 CAC（ML-CAC）评分分为四分位数进行预后分析。主要终点是全因死亡、非致死性心肌梗死或非致死性卒中的复合终点。
总体而言，5785 例患者分为训练集（80%）和测试集（20%）。为了临床实用性，我们根据 10 次交叉验证的五次重复，在训练集中选择了具有良好和令人满意的判别和校准性能的九特征支持向量机模型（平均 AUC=0.715，Brier 评分=0.202），并根据测试集进行了选择（AUC=0.753，Brier 评分=0.191）。在测试集队列（n=1137）中，中位随访 2.8 年后有 50 例（4.4%）患者发生主要终点。ML-CAC 系统与主要终点的风险增加显著相关（趋势调整后的危险比 2.26，95%CI 1.35-3.79，p=0.002）。ML-CAC 系统和 CT-CAC 系统的预后价值无显著差异（C 指数，0.67 与 0.69；p=0.618）。
从临床变量预测的 ML-CAC 评分可作为接受有创性冠状动脉造影患者的新型预后指标。
在未行术前 CT 测量冠状动脉钙评分的接受有创性冠状动脉造影的患者中，基于机器学习的冠状动脉钙评分评估可作为预测预后的替代方法。
• CAC 评分是一种可靠的预后指标，可通过非 CT 方法进行预测。• 我们开发了一种利用九个临床变量预测严重冠状动脉钙化的基于机器学习（ML）的 CAC 模型。• ML-CAC 系统在接受有创性冠状动脉造影的患者中具有显著的预后价值。
