Improved long-term prognostic value of coronary CT angiography-derived plaque measures and clinical parameters on adverse cardiac outcome using machine learning

• Published in: European radiology Vol. 31; no. 1; pp. 486 - 493
• Main Authors: Tesche, Christian, Bauer, Maximilian J., Baquet, Moritz, Hedels, Benedikt, Straube, Florian, Hartl, Stefan, Gray, Hunter N., Jochheim, David, Aschauer, Theresia, Rogowski, Sebastian, Schoepf, U. Joseph, Massberg, Steffen, Hoffmann, Ellen, Ebersberger, Ullrich
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 2021; Springer Nature B.V
• Subjects: Algorithms; Angiography; Calcium; Cardiac; Cardiovascular disease; Cardiovascular diseases; Computed Tomography Angiography; Coronary Angiography; Coronary artery; Coronary artery disease; Coronary Artery Disease - diagnostic imaging; Coronary Stenosis - diagnostic imaging; Decision trees; Diagnostic Radiology; Diagnostic systems; Female; Health risks; Heart; Heart diseases; Humans; Imaging; Integration; Internal Medicine; Interventional Radiology; Learning algorithms; Machine Learning; Male; Medical imaging; Medicine; Medicine & Public Health; Neuroradiology; Parameters; Plaque, Atherosclerotic - diagnostic imaging; Predictions; Predictive Value of Tests; Prognosis; Radiology; Regression analysis; Regression models; Retrospective Studies; Risk analysis; Risk Assessment; Risk factors; Segments; Stenosis; Tomography, X-Ray Computed; Ultrasound; Outcome measures; Coronary artery disease; Machine learning; Multidetector computed tomography
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-020-07083-2

Objectives
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).
Methods
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).
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 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
 < 0.05). Similar results were shown for adverse 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 compared with logistic regression analysis (AUC 0.96 vs. 0.92,
p
 = 0.024).
Conclusion
Integration of a ML model improves the long-term prediction of MACE when compared with conventional CT risk scores, adverse plaque measures, and clinical information. ML algorithms may improve the integration of patient’s information to enhance risk stratification.
Key Points
•
A machine learning (ML) model portends high discriminatory power to predict major adverse cardiac events (MACE).
• ML-based risk stratification shows superior diagnostic performance for MACE prediction over coronary CT angiography (cCTA)-derived risk scores or clinical parameters alone.
• A ML model outperforms conventional logistic regression analysis for the prediction of MACE.