Machine Learning for Predicting Spinal Anesthesia Duration
Comparative Evaluation of Machine Learning Algorithms for Predicting Spinal Anesthesia Termination Time
1 other identifier
observational
140
1 country
1
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P50-P75 for all trials
Started Oct 2025
Shorter than P25 for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
October 31, 2025
CompletedFirst Submitted
Initial submission to the registry
November 20, 2025
CompletedFirst Posted
Study publicly available on registry
December 1, 2025
CompletedPrimary Completion
Last participant's last visit for primary outcome
February 14, 2026
CompletedStudy Completion
Last participant's last visit for all outcomes
March 1, 2026
CompletedDecember 8, 2025
December 1, 2025
4 months
November 20, 2025
December 1, 2025
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Predictive performance of machine learning
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)
From the end of intrathecal injection (T₀) to complete motor recovery (T_end), expected within 6 hours post-injection.
Secondary Outcomes (2)
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.
Study Arms (1)
Knee Arthroplasty Group
The group of patients who will undergo knee replacement surgery under spinal anesthesia
Interventions
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
Eligibility Criteria
This study will include adult patients undergoing elective total knee arthroplasty (TKA) under spinal anesthesia at Kocaeli City Hospital Operating Theaters between November 2025 and March 2026. All participants will receive spinal anesthesia using 0.5% hyperbaric bupivacaine, and intraoperative monitoring will be conducted in accordance with institutional anesthesia standards. The study population represents a homogeneous surgical group in which spinal anesthesia is routinely applied, allowing for standardized anesthesia protocols and reliable measurement of anesthesia duration. Eligible patients will be classified as ASA Physical Status I or II and aged 18 years or older.
You may qualify if:
- 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.
You may not qualify if:
- 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.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Kocaeli City Hospital
Kocaeli, İzmit, 41000, Turkey (Türkiye)
Related Publications (5)
Bellini V, Russo M, Domenichetti T, Panizzi M, Allai S, Bignami EG. Artificial Intelligence in Operating Room Management. J Med Syst. 2024 Feb 14;48(1):19. doi: 10.1007/s10916-024-02038-2.
PMID: 38353755BACKGROUNDCao Y, Wang Y, Liu H, Wu L. Artificial intelligence revolutionizing anesthesia management: advances and prospects in intelligent anesthesia technology. Front Med (Lausanne). 2025 Aug 6;12:1571725. doi: 10.3389/fmed.2025.1571725. eCollection 2025.
PMID: 40842529BACKGROUNDMagdic Turkovic T, Sabo G, Babic S, Sostaric S. SPINAL ANESTHESIA IN DAY SURGERY - EARLY EXPERIENCES. Acta Clin Croat. 2022 Sep;61(Suppl 2):160-164. doi: 10.20471/acc.2022.61.s2.22.
PMID: 36824644BACKGROUNDBoublik J, Gupta R, Bhar S, Atchabahian A. Prilocaine spinal anesthesia for ambulatory surgery: A review of the available studies. Anaesth Crit Care Pain Med. 2016 Dec;35(6):417-421. doi: 10.1016/j.accpm.2016.03.005. Epub 2016 Jun 21.
PMID: 27352633BACKGROUNDSchubert AK, Wiesmann T, Wulf H, Dinges HC. Spinal anesthesia in ambulatory surgery. Best Pract Res Clin Anaesthesiol. 2023 Jun;37(2):109-121. doi: 10.1016/j.bpa.2023.04.002. Epub 2023 Apr 15.
PMID: 37321760BACKGROUND
MeSH Terms
Conditions
Interventions
Condition Hierarchy (Ancestors)
Intervention Hierarchy (Ancestors)
Study Officials
- STUDY DIRECTOR
Ahmet Yüksek, MD
Kocaeli City Hospital
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER GOV
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- MD
Study Record Dates
First Submitted
November 20, 2025
First Posted
December 1, 2025
Study Start
October 31, 2025
Primary Completion
February 14, 2026
Study Completion
March 1, 2026
Last Updated
December 8, 2025
Record last verified: 2025-12