Machine learning analysis of factors influencing ischemic stroke after coronary artery bypass grafting

Ischemic Stroke (IS) is a devastating complication of coronary bypass (CABG) surgery, significantly increasing mortality, morbidity, cost, and the need for long-term care, and reducing the quality of life. With the aim of a learning-machine method, we investigated factors influencing the occurrence of IS after CABG. Methods and results We employed a statistical learning method – random forests – to examine which of various variables had the greatest impact on postoperative IS. A dataset including 16,255 consecutive patients undergoing isolated CABG between 1997 and 2017 In one Institution was examined. Along with demographic and clinical variables technical-related factors were included, encompassing surgical technique, number of touches on the aorta, cardiopulmonary bypass, total aortic clamping, use of the side-biting clamp, or number of proximal anastomoses. A ranking score based on the average percent increase in mean squared error and obtained for all variables was employed to quantify the importance of any variable in predicting IS. A total of 641 strokes occurred (3.9%). Total aortic clamp showed the highest incidence of stroke (n=570, 88.95% of total). Total aortic clamp had the highest score among all variables followed by previous stroke and carotid artery disease >50%. In contrast, other variables related to surgical technique showed low ranking scores in predicting IS. In patients undergoing CABG with the use of total aortic, a previous brain ischemic insult as well as age >75 years increase the risk of IS (fIGURE) Conclusions The use of total aortic clamp is the strongest predictor of stroke. A clamp-less technique is recommended whenever possible especially in older patients and those who experienced preoperative stroke or who had significant carotid stenosis.