Enhancing post-induction hypotension prediction based on exemplar learning with crossover restart strategy driven feature selection.

Abstract

Background and objective: Post-induction hypotension (PIH), primarily resulting from the vasodilatory effects and reduced cardiac output induced by anesthetic agents, is widespread among patients with pre-existing cardiovascular conditions or those who have experienced suboptimal fluid management. This condition can lead to inadequate perfusion of critical organs such as the brain and heart, increasing the risk of lengthy postoperative recovery, complications, and mortality. Therefore, early identification and prediction of PIH are crucial for improving postoperative management and patient outcomes.

Methods: This study utilized data from 440 elderly patients experiencing elective surgery under general anesthesia at the Ningbo University Affiliated People's Hospital. Patients were categorized into PIH and non-PIH groups based on their mean arterial pressure at the time of induction. To predict PIH, the study developed a machine-learning model named bECRIME-SVM. The model employed an exemplar learning strategy enhanced by a crossover restart strategy within the rime optimization algorithm (ECRIME) to select optimal feature subsets. These subsets were then evaluated using a support vector machine (SVM) to assess their predictive efficacy for PIH.

Results: The bECRIME-SVM model demonstrated strong performance on the PIH dataset, achieving a prediction accuracy of 84.100 % and a specificity of 85.287 %. Comparative analysis with other models from the CEC 2017 benchmark functions confirmed the superior optimization capability and convergence accuracy of the ECRIME algorithm. The model also identified several key predictive features, including diabetes, drinking history, atropine, β-blockers, total cholesterol, and pre-induction systolic blood pressure.

Conclusions: The bECRIME-SVM model provides a valuable tool for the clinical prediction of PIH, with high accuracy and specificity. Identifying significant predictive features offers essential insights for the early detection and management of PIH, ultimately contributing to improved postoperative outcomes for patients undergoing general anesthesia.