Scoring of Coronary Artery Disease Characteristics on Coronary CT Angiograms by Using Machine Learning

Background

Coronary CT angiography contains prognostic information but the best method to extract these data remains unknown.

Purpose

To use machine learning to develop a model of vessel features to discriminate between patients with and without subsequent death or cardiovascular events. Performance was compared with that of conventional scores.

Materials and Methods

Coronary CT angiography was analyzed by radiologists into four features for each of 16 coronary segments. Four machine learning model types were explored. Five conventional vessel scores were computed for comparison including the Coronary Artery Disease Reporting and Data System (CAD-RADS) score. The National Death Index was retrospectively queried from January 2004 through December 2015. Outcomes were all-cause mortality, coronary heart disease deaths, and coronary deaths or nonfatal myocardial infarctions. Score performance was assessed by using area under the receiver operating characteristic curve (AUC).

Results

Between February 2004 and November 2009, 6892 patients (4452 men [mean age ± standard deviation, 51 years ± 11] and 2440 women [mean age, 57 years ± 12]) underwent coronary CT angiography (median follow-up, 9.0 years; interquartile range, 8.2–9.8 years). There were 380 deaths of all causes, 70 patients died of coronary artery disease, and 43 patients reported nonfatal myocardial infarctions. For all-cause mortality, the AUC was 0.77 (95% confidence interval: 0.76, 0.77) for machine learning (k-nearest neighbors) versus 0.72 (95% confidence interval: 0.72, 0.72) for CAD-RADS (P < .001). For coronary artery heart disease deaths, AUC was 0.85 (95% confidence interval: 0.84, 0.85) for machine learning versus 0.79 (95% confidence interval: 0.78, 0.80) for CAD-RADS (P < .001). When deciding whether to start statins, if the choice is made to tolerate treating 45 patients to be sure to include one patient who will later die of coronary disease, the use of the machine learning score ensures that 93% of patients with events will be administered the drug; if CAD-RADS is used, only 69% will be treated.

Conclusion

Compared with Coronary Artery Disease Reporting and Data System and other scores, machine learning methods better discriminated patients who subsequently experienced an adverse event from those who did not.