Machine Learning for Predicting Spinal Anesthesia Duration

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.

NCT Number	NCT07256548
Lead Sponsor	Kocaeli City Hospital
Conditions	Spinal Anesthesia, Machine Learning, Knee Arthroplasty, Total, Spinal Anesthesia Duration, Postoperative Care, Postoperative Acute Pain
Enrollment	145 participants
Start Date	2025-10-31
Primary Completion	2026-05-14 (estimated)
Study Completion	2026-06-01 (estimated)
Updated on ClinicalTrials.gov	2026-08-13