Machine Learning for Predicting Spinal Anesthesia Duration

Abstract Spinal anesthesia offers several advantages over general anesthesia in total knee arthroplasty, including reduced intraoperative blood loss, less postoperative pain, faster recovery, and shorter hospital stays. It also minimizes anesthesia-related complications and facilitates early mobilization, making it a preferred technique for many orthopedic procedures. However, predicting the exact duration of spinal anesthesia remains challenging and is clinically significant for ensuring patient safety, optimizing postoperative pain control, and preventing anesthesia-related complications. Accurate estimation of anesthesia duration allows for more effective surgical planning, timely analgesia administration, and improved patient satisfaction. Unexpectedly prolonged anesthesia may increase the risk of adverse effects, whereas premature termination can result in inadequate pain management. Machine learning (ML) technologies offer promising tools for predicting clinical outcomes in anesthesia practice by analyzing complex, multidimensional datasets. Previous research has demonstrated the potential of ML algorithms to predict perioperative events such as hypotension, blood transfusion requirements, and postoperative complications. In this study, the usability and effectiveness of ML models in predicting the time of termination of spinal anesthesia and the patient's readiness for mobilization were investigated. By incorporating multiple clinical variables-such as patient demographics, anesthetic drug dosages, and surgical factors-our model aims to provide accurate, data-driven predictions. These predictive insights can support anesthesiologists in tailoring perioperative management, reducing complication risks, and improving overall patient outcomes. Ultimately, integrating ML-based prediction systems into anesthesia practice may enhance the safety, efficiency, and personalization of perioperative care.