NCT07256548

Machine Learning for Predicting Spinal Anesthesia Duration

Comparative Evaluation of Machine Learning Algorithms for Predicting Spinal Anesthesia Termination Time

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 140 (estimated)
• Sponsor: Kocaeli City Hospital · Other Government
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

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.

Detailed description

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.

Conditions

• Spinal Anesthesia
• Machine Learning
• Knee Arthroplasty, Total
• Spinal Anesthesia Duration
• Postoperative Care
• Postoperative Acute Pain

Interventions

Type	Name	Description
PROCEDURE	Spinal Anesthesia (bupivacaine)	Before being placed on the operating table, the patient is positioned comfortably and prepared for the procedure. Standardized monitoring is initiated, including five-lead electrocardiography (ECG), non-invasive blood pressure (NIBP), and pulse oximetry (SpO₂). Baseline measurements of heart rate, systolic and diastolic blood pressure, mean arterial pressure (MAP), and oxygen saturation are recorded. An 18- or 20-gauge intravenous line is inserted, and an appropriate crystalloid preload is administered. After ensuring aseptic conditions, the patient is positioned in the sitting posture, and spinal puncture is performed at the L3-L4 or L4-L5 intervertebral space using a 25 Gauge Whitacre needle. Following free flow of cerebrospinal fluid, 0.5% hyperbaric bupivacaine (10-15 mg) is slowly injected. The completion of the injection is

Timeline

• Start date: 2025-10-31
• Primary completion: 2026-02-14
• Completion: 2026-03-01
• First posted: 2025-12-01
• Last updated: 2025-12-08

Locations

1 site across 1 country: Turkey (Türkiye)