ESCR Abstracts
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MEETING ABSTRACTS
ESCR Abstracts
Ó Springer Nature B.V. 2020
Improved prognostic value of coronary CT angiography-derived plaque information and clinical parameter on adverse cardiac outcome using machine learning
Conclusion Integration of a ML model improves the prediction of MACE when compared to conventional CT risk scores, plaque measures and clinical information. ML algorithms may improve the integration of patient’s information to improve risk stratification. References
Christian Tesche, Stefan Hartl, Joe Schoepf, Sebastian Rogowski, Theresia Aschauer, Moritz Baquet, Maximilian J. Bauer, Ullrich Ebersberger, Florian Straube, Ellen Hoffmann Purpose Coronary CT angiography (cCTA) is an accepted method to rule out obstructive coronary artery disease (CAD) and enables direct non-invasive atherosclerotic plaque evaluation (1, 2). Recently, machine learning (ML) as a field of computer science has been introduced into cardiovascular imaging for risk stratification, outcome prediction and decision-making in a more time-efficient manner with improved diagnostic accuracy. Thus, we sought to evaluate the prognostic value of cCTA-derived plaque information and clinical parameters on major adverse cardiac events (MACE) using a ML model. Methods and Materials Datasets of 361 patients (61.9 ± 10.3 years, 65% male) with suspected coronary artery disease (CAD) who underwent cCTA were retrospectively analyzed. Major adverse cardiac events (MACE) more than 90 days after cCTA were recorded. Several cCTA-derived plaque measures and conventional CT risk scores together with cardiovascular risk factors were provided to a ML model to predict MACE. A boosted ensemble algorithm (RUSBoost) utilizing decision trees as weak learners with repeated nested cross-validation to train and validate the model was used. Performance of the ML model was calculated using the area under the receiver operating characteristic curve (AUC). Results MACE was observed in 31 patients (8.6%) after a median follow-up of 5.4 years. Discriminatory power was significantly higher for the ML model (AUC 0.96[95% CI 0.93–0.98]) compared to conventional CT risk scores including Agatston calcium score (AUC 0.84 95% CI 0.80–0.87), segment involvement score (AUC 0.88 95% CI 0.84–0.91), and segment stenosis score (AUC 0.89 95% CI 0.86–0.92, all p \ 0.05). Similar results were shown for plaque measures (AUCs 0.72–0.82, all p \ 0.05) and clinical parameters including the Framingham risk score (AUCs 0.71–0.76, all p \ 0.05). The ML model yielded significantly higher diagnostic performance when compared to logistic regression analysis (AUC 0.96 vs. 0.92, p = 0.024).
1. Min JK, Shaw LJ, Devereux RB, et al. Prognostic value of multidetector coronary computed tomographic angiography for prediction of all-cause mortality. J Am Coll Cardiol. 2007;50:1161–1170. 2. Tesche C, Plank F, De Cecco CN, et al. Prognostic implications of coronary CT angiography-derived quantitative markers for the prediction of major adverse cardiac events. J Cardiovasc Comput Tomogr. 2016;10:458–465
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