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.
Who can participate
Age range18 Years
SexALL
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Inclusion criteria
✓. Patients scheduled to undergo total knee arthroplasty between November 2025 and March 2026 at the Kocaeli City Hospital Operating Theaters.
✓. Patients who have provided written informed consent to participate in the study.
✓. Patients whose surgery is planned under spinal anesthesia.
✓. Patients for whom complete clinical data can be obtained during the study period.
✓. Adults aged 18 years or older, classified as American Society of Anesthesiologist's (ASA) Physical Status I or II.
Exclusion criteria
✕. Patients who were converted to general anesthesia during surgery or initially operated under general anesthesia.
✕. Patients who required postoperative intensive care unit (ICU) admission following anesthesia.
✕. Patients who developed surgical complications and for whom postoperative mobilization could not be planned.
✕. Patients with cognitive impairment preventing them from completing pain assessment scales in the postoperative period.
What they're measuring
1
Predictive performance of machine learning
Timeframe: From the end of intrathecal injection (Tâ‚€) to complete motor recovery (T_end), expected within 6 hours post-injection.