Enhancing Perioperative Decision-Making: Utilizing Machine Learning to Predict Postoperative Stroke Following Craniotomy

Objection: Postoperative stroke (PS) represents a significant and grave complication, which often remains challenging to detect until clear clinical symptoms emerge. The early identification of populations at high risk for PS is essential for enabling timely intervention and enhancing postoperative outcomes. This study seeks to employ machine learning (ML) techniques to create a predictive model for PS following elective craniotomy. Methods This study encompassed a total of 1,349 cases that underwent elective craniotomy between January 2013 and August 2021. Perioperative data, encompassing demographics, etiology, laboratory results, comorbidities, and medications, were utilized to construct predictive models. Nine distinct ML models were developed for the prediction of PS and assessed based on the area under the receiver-operating characteristic curve (AUC), along with sensitivity, specificity, and accuracy metrics. Results Among the 1,349 patients included in the study, 137 cases (10.2%) were diagnosed with PS, which was associated with a worse prognosis. Of the nine ML prediction models evaluated, the logistic regression (LR) model exhibited superior performance, as indicated by an AUC value of 0.741 (0.64–0.85), and competitive performance metrics, including an accuracy of 0.668, sensitivity of 0.650, and specificity of 0.670. Notably, feature importance analysis identified "preoperative albumin," "ASA classification," and "preoperative hemoglobin" as the top three factors contributing to the prediction of PS. Conclusion Our study successfully developed a real-time and easily accessible parameter requiring LR-based PS prediction model for post-elective craniotomy patients, which enhanced perioperative decision-making.