Key result
Machine learning outperforms conventional CCTA scores for major cardiovascular events prediction with ~0.92 AUC.
Why the study?
The study aimed to compare the prognostic performance of conventional scoring systems with machine learning models on CCTA for predicting MACE and identifying key contributing factors.
Do machine learning models improve the prediction of major adverse cardiovascular events in patients undergoing coronary computed tomography angiography compared to conventional scoring systems?
Observational (n=416)
No
Do machine learning models improve the prediction of major adverse cardiovascular events in patients undergoing coronary computed tomography angiography compared to conventional scoring systems?
Machine learning models integrating clinical and anatomical features from CCTA can significantly improve risk stratification for major adverse cardiovascular events compared to conventional scoring systems.
Answers are grounded in this paper and cite their sources.
May enhance MACE prediction after CCTA; hypothesis-generating and requires prospective validation before practice change.
Background: The study aims to compare the prognostic performance of conventional scoring systems to a machine learning (ML) model on coronary computed tomography angiography (CCTA) to discriminate between the patients with and without major adverse cardiovascular events (MACEs) and to find the most important contributing factor of MACE. Materials and methods: From November to December 2019, 500 of 1586 CCTA scans were included and analyzed, then six conventional scores were calculated for each participant, and seven ML models were designed. Our study endpoints were all-cause mortality, non-fatal myocardial infarction, late coronary revascularization, and hospitalization for unstable angina or heart failure. Score performance was assessed by area under the curve (AUC) analysis. Results: Of 500 patients (mean age: 60 ± 10; 53.8% male subjects) referred for CCTA, 416 patients have met inclusion criteria, 46 patients with early (<90 days) cardiac evaluation (due to the inability to clarify the reason for the assessment, deterioration of the symptoms vs. the CCTA result), and 38 patients because of missed follow-up were not enrolled in the final analysis. Forty-six patients (11.0%) developed MACE within 20.5 ± 7.9 months of follow-up. Compared to conventional scores, ML models showed better performance, except only one model which is eXtreme Gradient Boosting had lower performance than conventional scoring systems (AUC:0.824, 95% confidence interval (CI): 0.701-0.947). Between ML models, random forest, ensemble with generalized linear, and ensemble with naive Bayes were shown to have higher prognostic performance (AUC: 0.92, 95% CI: 0.85-0.99, AUC: 0.90, 95% CI: 0.81-0.98, and AUC: 0.89, 95% CI: 0.82-0.97), respectively. Coronary artery calcium score (CACS) had the highest correlation with MACE. Conclusion: Compared to the conventional scoring system, ML models using CCTA scans show improved prognostic prediction for MACE. Anatomical features were more important than clinical characteristics.
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Ghorashi et al. (2022) conducted an observational in Patients referred for coronary computed tomography angiography (CCTA) (n=416). Machine learning models on CCTA vs. Conventional scoring systems was evaluated on Prognostic performance for predicting major adverse cardiovascular events (MACE) (AUC 0.92, 95% CI 0.85-0.99). Machine learning models, particularly random forest, improved the prognostic prediction of major adverse cardiovascular events compared to conventional scoring systems.
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