Machine learning-based model development for predicting risk factors of prolonged intra-aortic balloon pump therapy in patients with coronary artery bypass grafting.

Machine learning algorithms are frequently used to clinical risk prediction. Our study was designed to predict risk factors of prolonged intra-aortic balloon pump (IABP) use in patients with coronary artery bypass grafting (CABG) through developing machine learning-based models. Patients who received perioperative IABP therapy were divided into two groups based on their length of IABP implantation longer than the 75th percentile for the whole cohort: normal (≤ 10 days) and prolonged (> 10 days) groups. Seven machine learning-based models were created and evaluated, and then the Shapley Additive exPlanations (SHAP) method was employed to further illustrate the influence of the features on model. In our study, a total of 143 patients were included, comprising 56 cases (38.16%) in the prolonged group. The logistic regression model was considered the final prediction model according to its most excellent performance. Furthermore, feature important analysis identified left ventricular end-systolic or diastolic diameter, preoperative IABP use, diabetes, and cardiac troponin T as the top five risk variables for prolonged IABP implantation in patients. The SHAP analysis further explained the features attributed to the model. Machine learning models were successfully developed and used to predict risk variables of prolonged IABP implantation in patients with CABG. This may help early identification for prolonged IABP use and initiate clinical interventions.

A case report of acute simultaneous cardiocerebral infarction: possible pathophysiology.

The occurrence of acute myocardial infarction (AMI) accompanied by ischemic cerebrovascular accidents has rarely been reported in previous articles. In this report, we present a 72-year-old female patient with massive cerebral infarction secondary to acute anterior and high lateral wall myocardial infarction, finally resulting in a deep coma. The patient ultimately failed to respond to aggressive resuscitation and succumbed to cardiogenic shock and fatal ventricular arrhythmias. We consider that the co-occurrence of these diseases is more than just a coincidence, and that there may be a connection between them. In this article, we performed an in-depth exploration and discussion of the explanation of this phenomenon. It is essential to recognize these situations in the early stages, which determines the follow-up treatment and prognosis. We suggest that decisions regarding patient management should be based on hemodynamic stability, close cardiac monitoring, and the site of cerebral infarction, and also emphasize that the evaluation of hemodynamic status in these patients is a prerequisite for deciding whether to treat the cerebral or coronary infarction first. The present report is written for the purpose of reminding readers of this rare and severe situation, and to emphasize the necessity for further research on how to deal with it best.