Machine Learning for Predicting Spinal Anesthesia Duration

Kocaeli City Hospital

Status: Enrolling

Conditions: Postoperative Acute Pain; Postoperative Care; Spinal Anesthesia; Knee Arthroplasty, Total; Machine Learning; Spinal Anesthesia Duration

Treatments: Procedure: Spinal Anesthesia (bupivacaine)

Study type: Observational

Funder types: Other

Identifiers

NCT07256548

KSH_SVG_1

Details and patient eligibility

About

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.

Full 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.

Enrollment: 140 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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.

Trial design

140 participants in 1 patient group

Knee Arthroplasty Group

Description:

The group of patients who will undergo knee replacement surgery under spinal anesthesia

Treatment:

Procedure: Spinal Anesthesia (bupivacaine)