Spinal Anesthesia, Machine Learning, Knee Arthroplasty, Total, Spinal Anesthesia Duration, Postoperative Care, Postoperative Acute Pain
Conditions
Keywords
spinal anesthesia, machine learning, Knee arthroplasty, spinal anesthesia duration, Acute postoperative pain
Brief 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.
Interventions
PROCEDURESpinal 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
Sponsors
Kocaeli City Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Predictive performance of machine learning | 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) |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| spinal anesthesia termination time | From the end of intrathecal injection (T₀) to complete motor recovery (T_end), expected within 6 hours post-injection. | It is the period of time from the moment of completion of spinal anesthesia until the complete resolution of motor blockade in the patient's lower extremities. |
| Visual Analogue Scale | From the end of intrathecal injection (T₀) to complete motor recovery (T_end), expected within 6 hours post-injection. | A tool used to help a person rate the intensity of certain sensations and feelings, such as pain. The visual analog scale for pain is a straight line with one end meaning no pain and the other end meaning the worst pain imaginable. A patient marks a point on the line that matches the amount of pain he or she feels. It may be used to help choose the right dose of pain medicine. |
Countries
Turkey (Türkiye)
Contacts
STUDY_DIRECTORAhmet Yüksek, MD
Kocaeli City Hospital
Outcome results
None listed