Objective To assess with machine learning the predictive value of mechanomics derived from coronary CT angiography (CCTA) for atherosclerotic plaque formation in regions proximal to myocardial bridging (MB) in the left anterior descending coronary artery (LAD).

Methods This retrospective study included a cohort of patients with MB in LAD and no atherosclerotic plaque formation in LAD as confirmed by two CCTA conducted between January 2007 and April 2021 at our hospital. The interval between the two CCTA examinations was more than 3 months. The primary endpoint was the formation of atherosclerotic plaques in regions proximal to the myocardial bridging. Patient demographic characteristics and clinical risk factors were documented. Then, the patients were matched by age and sex in a 1-to-1 ratio and divided into two groups, those with plaque formation and those without plaque formation. Computational fluid dynamics analysis was performed based on CCTA. Key anatomical parameters of MB, including location, length, depth, and systolic compression index, were meticulously measured on the CCTA images. Mechanomic data were extracted from the region proximal to the MB. A multivariate Cox regression analysis was performed to identify significant features. A random forest algorithm was used to select mechanomic features for subsequent modeling and to assign scores for each patient's mechanomic features. The log-rank test and Kaplan-Meier curves were used to investigate the mechanomic model's predictive performance concerning plaque formation. Additionally, the operator characteristic curves were applied to evaluate how well the model could predict plaque formation across various myocardial bridge subgroups.

Results A total of 104 patients with LAD MB were recruited. The mean age of the subjects were (54.56±10.56) years and 75.00% (78/104) of them were male. Among them, 52 developed plaque formation over a median follow-up period of 3.0 years. Apart from a smoking history, which was more prevalent in the group with plaque formation than that in the group without plaque formation (21.15% vs. 5.77%, P=0.04), no significant differences between the groups were observed in terms of the other clinical or anatomical characteristics (all P≤0.05). The participants were divided into a training set (n=74) and a validation set (n=30) at a 7∶3 ratio. With the mechanomics model constructed using the random forest algorithm, the patients were classified into a high-score group (≥0.46) and a low-score group (<0.46) based on a cutoff score of 0.46. The mechanomics model achieved a sensitivity of 0.87 (0.58-0.98) and an accuracy of 0.63 (0.44-0.79) in the validation set. The multivariate Cox regression model revealed a strong positive association between mechanomics and plaque formation (hazards ratio HR: 10.58; 95% confidence interval CI: 3.23-34.64, P<0.001). The log-rank test showed that the high-score group in the mechanomics model was more likely to develop plaques at the proximal regions of the myocardial bridge compared to the low-score group (P<0.001). The area under the curve (AUC) for plaque formation, as predicted by the model, was 0.88 (95% CI: 0.82-0.95) for the entire population, 0.89 (95% CI: 0.82-0.96) for the training set, 0.86 (95% CI: 0.74-0.99) for the validation set, 0.92 (95% CI: 0.86-0.97) for the superficial MB group, 0.86 (95% CI: 0.74-0.98) for the long MB group, and 0.91 (95% CI: 0.83-0.98) for the short MB group.

Conclusion The mechanomic assessment holds substantial potential as a predictive tool for atherosclerotic plaque formation in regions proximal to MB in LAD.