Improved long-term prognostic value of coronary CT angiography-derived plaque measures and clinical parameters on adverse cardiac outcome using machine learning
| Autor: | David Jochheim, Sebastian Rogowski, Christian Tesche, Ellen Hoffmann, Theresia Aschauer, Steffen Massberg, U. Joseph Schoepf, Hunter N Gray, Stefan Hartl, Maximilian J. Bauer, Florian Straube, Ullrich Ebersberger, Moritz Baquet, Benedikt Hedels |
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| Rok vydání: | 2020 |
| Předmět: |
Male
medicine.medical_specialty Computed Tomography Angiography Coronary Artery Disease Logistic regression Machine learning computer.software_genre Coronary Angiography Risk Assessment 030218 nuclear medicine & medical imaging Coronary artery disease Machine Learning 03 medical and health sciences 0302 clinical medicine Predictive Value of Tests medicine Humans Radiology Nuclear Medicine and imaging Neuroradiology Retrospective Studies Framingham Risk Score medicine.diagnostic_test business.industry Area under the curve Coronary Stenosis Interventional radiology General Medicine medicine.disease Prognosis Plaque Atherosclerotic Stenosis 030220 oncology & carcinogenesis Female Radiology Artificial intelligence business Tomography X-Ray Computed computer Mace |
| Zdroj: | European radiology. 31(1) |
| ISSN: | 1432-1084 |
| Popis: | To evaluate the long-term prognostic value of coronary CT angiography (cCTA)-derived plaque measures and clinical parameters on major adverse cardiac events (MACE) using machine learning (ML). Datasets of 361 patients (61.9 ± 10.3 years, 65% male) with suspected coronary artery disease (CAD) who underwent cCTA were retrospectively analyzed. MACE was recorded. cCTA-derived adverse plaque features 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 curve (AUC). 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 with 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 |
| Databáze: | OpenAIRE |
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