Machine learning–based coronary artery calcium score predicted from clinical variables as a prognostic indicator in patients referred for invasive coronary angiography

• Published in: European radiology Vol. 34; no. 9; pp. 5633 - 5643
• Main Authors: Jian, Wen, Dong, Zhujun, Shen, Xueqian, Zheng, Ze, Wu, Zheng, Shi, Yuchen, Han, Yingchun, Du, Jie, Liu, Jinghua
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2024; Springer Nature B.V
• Subjects: Aged; Algorithms; Angiography; Arteriosclerosis; Calcification; Calcification (ectopic); Calcium; Cardiac; Cerebral infarction; Computed Tomography Angiography - methods; Coronary Angiography - methods; Coronary artery; Coronary Artery Disease - diagnostic imaging; Coronary vessels; Diagnostic Radiology; Female; Humans; Imaging; Internal Medicine; Interventional Radiology; Learning algorithms; Machine Learning; Male; Medical imaging; Medical prognosis; Medicine; Medicine & Public Health; Middle Aged; Myocardial infarction; Neuroradiology; Predictive Value of Tests; Prognosis; Radiology; Retrospective Studies; Support vector machines; Test sets; Ultrasound; Vascular Calcification - diagnostic imaging; Vascular calcification; Coronary angiography; Prognosis; X-ray computed tomography; Machine learning
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-024-10629-3

Objectives 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. Methods 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. Results 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). Conclusion ML-CAC score predicted from clinical variables can serve as a novel prognostic indicator in patients referred for invasive coronary angiography. Clinical relevance statement 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. Key Points • 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.