Zhang et al. aimed to predict the need for preoperative intra-aortic balloon pump (IABP) use in patients undergoing coronary artery bypass grafting (CABG) using machine learning techniques. Researchers retrospectively analyzed data from 236 patients, applying six machine learning models to clinical and laboratory features. The Gaussian Naïve Bayes model achieved the best predictive performance. Key predictors of preoperative IABP use included inflammatory and cardiac injury markers like the neutrophil-to-lymphocyte ratio and troponin T. Overall, Zhang et al. showed that systemic inflammation and cardiac injury are key factors in predicting preoperative IABP use, as reflected by top model features like neutrophil-to-lymphocyte ratio and troponin T. Zhang et al. suggest that using machine learning to preoperatively assess patients could help physicians make better decisions, but they highlight that the results need to be confirmed in larger studies and at multiple hospitals.