Comparative Evaluation of Machine Learning Algorithms for Predicting Spinal Anesthesia Termination Time
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 145
- 试验地点
- 1
- 主要终点
- Predictive performance of machine learning
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- •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.
- •Patients with neuropathic pain, multiple sclerosis, or other neuromotor disorders will be excluded from the study.
研究组 & 干预措施
Knee Arthroplasty Group
The group of patients who will undergo knee replacement surgery under spinal anesthesia
干预措施: Spinal Anesthesia (bupivacaine) (Procedure)
结局指标
主要结局
Predictive performance of machine learning
时间窗: From the end of intrathecal injection (T₀) to complete motor recovery (T_end), expected within 6 hours post-injection.
The primary outcome of this study is the predictive performance of machine learning (ML) algorithms in estimating the duration of spinal anesthesia (in minutes) based on preoperative and intraoperative variables. in: R² (Coefficient of Determination). Dimensionless (no unit)
次要结局
- spinal anesthesia termination time(From the end of intrathecal injection (T₀) to complete motor recovery (T_end), expected within 6 hours post-injection.)
- Visual Analogue Scale(From the end of intrathecal injection (T₀) to complete motor recovery (T_end), expected within 6 hours post-injection.)
研究者
Sıddık Varolgunes
MD
Kocaeli City Hospital