Machine Learning-Enhanced TCAB Score for Predicting Postoperative Ischemic Stroke After CABG.

BACKGROUND: Postoperative acute ischemic stroke remains a critical complication of coronary artery bypass grafting. This study aimed to develop a novel Total Cerebral Atherosclerosis Burden (TCAB) score for predicting the risk of AIS post-coronary artery bypass grafting.

METHODS: A prospective cohort of patients undergoing coronary artery bypass grafting was enrolled. The TCAB score was calculated by summing stenosis severity grades (0: <50%, 1: 50-69%, 2: 70-99%, 3: 100%) across all intracranial and extracranial artery segments. Primary outcome was in-hospital ischemic stroke. Multivariable logistic regression models adjusted for key clinical covariates were used to evaluate the association between TCAB and clinical outcomes.

RESULTS: Among 909 included patients, the mean TCAB score was significantly higher in patients with in-hospital ischemic stroke compared with those without (8 versus 2, P < 0.001). A TCAB score >3 predicted in-hospital ischemic stroke with an area under the curve of 0.756. Across all 3 multivariable models, higher TCAB scores remained independently associated with in-hospital ischemic stroke (Model 3: odds ratio [OR]=1.089, P=0.011), 1-year ischemic stroke (OR=1.093, P=0.011), and 1-year major adverse cardiovascular and cerebrovascular events (OR=1.068, P=0.020). The gradient boosting machine achieved the most stable predictive ability (area under the curve=0.8736 for in-hospital ischemic stroke; 0.8575 for 1-year ischemic stroke; 0.7475 for 1-year major adverse cardiovascular and cerebrovascular events).

CONCLUSIONS: The TCAB score, enhanced by machine learning, effectively predicted in-hospital ischemic stroke, 1-year ischemic stroke, and 1-year major adverse cardiovascular and cerebrovascular events post-coronary artery bypass grafting. It offers a practical tool for guiding preoperative revascularization and intraoperative embolic protection.