Spinal anesthesia provides significant advantages over general anesthesia in knee arthroplasty, including reduced blood loss, faster recovery, and fewer complications. However, predicting its duration is critical for patient safety and effective postoperative management. This study evaluates the usability of machine learning (ML) algorithms to predict the termination time of spinal anesthesia and the patient's readiness for mobilization. Using demographic, surgical, and anesthetic variables, ML models were trained to estimate anesthesia duration. Accurate predictions may improve intraoperative planning, optimize postoperative care, and enhance patient outcomes. Integrating ML-based predictive systems into anesthesia practice can contribute to safer, more efficient, and personalized perioperative management.

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.

Inclusion Criteria:

1. Patients scheduled to undergo total knee arthroplasty between November 2025 and March 2026 at the Kocaeli City Hospital Operating Theaters.
2. Patients who have provided written informed consent to participate in the study.
3. Patients whose surgery is planned under spinal anesthesia.
4. Patients for whom complete clinical data can be obtained during the study period.
5. Adults aged 18 years or older, classified as American Society of Anesthesiologist's (ASA) Physical Status I or II.

Exclusion Criteria:

1. Patients who were converted to general anesthesia during surgery or initially operated under general anesthesia.
2. Patients who required postoperative intensive care unit (ICU) admission following anesthesia.
3. Patients who developed surgical complications and for whom postoperative mobilization could not be planned.
4. Patients with cognitive impairment preventing them from completing pain assessment scales in the postoperative period.
5. Patients with neuropathic pain, multiple sclerosis, or other neuromotor disorders will be excluded from the study.

This study will include adult patients undergoing elective total knee arthroplasty (TKA) under spinal anesthesia at Kocaeli City Hospital Operating Theaters between November 2025 and March 2026.

All participants will receive spinal anesthesia using 0.5% hyperbaric bupivacaine, and intraoperative monitoring will be conducted in accordance with institutional anesthesia standards. The study population represents a homogeneous surgical group in which spinal anesthesia is routinely applied, allowing for standardized anesthesia protocols and reliable measurement of anesthesia duration.

Eligible patients will be classified as ASA Physical Status I or II and aged 18 years or older.