Prognostic Value of Machine Learning–based Time-to-Event Analysis Using Coronary CT Angiography in Patients with Suspected Coronary Artery Disease
Maximilian J. Bauer, Nejva Nano, Rafael Adolf, Albrecht Will, Eva Hendrich, Stefan Martinoff, Martin Hadamitzky
Abstract
Purpose To assess the long-term prognostic value of a machine learning (ML) approach in time-to-event analyses incorporating coronary CT angiography (CCTA)–derived and clinical parameters in patients with suspected coronary artery disease. Materials and Methods The retrospective analysis included patients with suspected coronary artery disease who underwent CCTA between October 2004 and December 2017. Major adverse cardiovascular events were defined as the composite of all-cause death, myocardial infarction, unstable angina, or late revascularization (>90 days after index scan). Clinical and CCTA-derived parameters were assessed as predictors of major adverse cardiovascular events and incorporated into two models: a Cox proportional hazards model with recursive feature elimination and an ML model based on random survival forests. Both models were trained and validated by employing repeated nested cross-validation. Harrell concordance index (C-index) was used to assess the predictive power. Results A total of 5457 patients (mean age, 61 years ± 11 [SD]; 3648 male patients) were evaluated. The predictive power of the ML model (C-index, 0.74; 95% CI: 0.71, 0.76) was significantly higher than the Cox model (C-index, 0.71; 95% CI: 0.68, 0.74; P = .02). The ML model also outperformed the segment stenosis score (C-index, 0.69; 95% CI: 0.66, 0.72; P < .001), which was the best performing CCTA-derived parameter, and patient age (C-index, 0.66; 95% CI: 0.63, 0.69; P < .001), the best performing clinical parameter. Conclusion An ML model for time-to-event analysis based on random survival forests had higher performance in predicting major adverse cardiovascular events compared with established clinical or CCTA-derived metrics and a conventional Cox model. Keywords: Machine Learning, CT Angiography, Cardiac, Arteries, Heart, Arteriosclerosis, Coronary Artery Disease Supplemental material is available for this article. © RSNA, 2023