Spinal AnesthesiaNCT07256548
Machine Learning for Predicting Spinal Anesthesia Duration
An observational study of Spinal Anesthesia and Machine Learning, run by Kocaeli City Hospital at 1 site in Turkey (Türkiye).
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Spinal Anesthesia, Machine Learning, Knee Arthroplasty, Total
- What is being tested?
- Spinal Anesthesia (bupivacaine)
- Type of treatment
- Injection or infusion, Surgery or procedure
- How many people
- 145 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 31 Oct 2025to 1 Jun 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: 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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Comparative Evaluation of Machine Learning Algorithms for Predicting Spinal Anesthesia Termination Time
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.
Read the 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
- Being tested
- Spinal Anesthesia (bupivacaine)
- Keywords
- spinal anesthesia, machine learning, Knee arthroplasty, spinal anesthesia duration, Acute postoperative pain
- Medical subject headings
- Pain, Postoperative
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Spinal Anesthesia (bupivacaine)Procedure or surgery
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
| Study group | Role | What they receive |
|---|---|---|
| Knee Arthroplasty Group | The group of patients who will undergo knee replacement surgery under spinal anesthesia Receives: Procedure: Spinal Anesthesia (bupivacaine) |
What is being measured
The primary outcome is the main question the study is designed to answer.
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)
Measured: 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
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.
Measured: From the end of intrathecal injection (T₀) to complete motor recovery (T_end), expected within 6 hours post-injection.
Visual Analogue Scale
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.
Measured: From the end of intrathecal injection (T₀) to complete motor recovery (T_end), expected within 6 hours post-injection.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Dec 2025
- Study start31 Oct 2025
- Primary completion14 May 2026
- Study completion1 Jun 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for Spinal AnesthesiaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 145 participants (actual)
Sponsor and investigators
- Lead sponsor
- Kocaeli City Hospital
- Sponsor type
- Other government
- Responsible party
- Principal investigator, Sıddık Varolgunes, Kocaeli City Hospital
- Investigators
- Ahmet Yüksek, MD, Study director, Kocaeli City Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07256548
- Sponsor's protocol ID
- KSH_SVG_1
- Status last verified
- Aug 2026
- First submitted
- 20 Nov 2025
- Last updated
- 13 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- Individual data sharing
- Undecided
Publications
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.
PubMed 38353755Cao 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.
PubMed 40842529Magdic 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.
PubMed 36824644Boublik 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.
PubMed 27352633Schubert 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.
PubMed 37321760
Study at a glance
From the registry record, last updated by the sponsor on 13 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Comparative Evaluation of Machine Learning Algorithms for Predicting Spinal Anesthesia Termination Time
- ClinicalTrials.gov ID
- NCT07256548
- Acronym / sponsor ID
- KSH_SVG_1
- Conditions
- Spinal Anesthesia, Machine Learning, Knee Arthroplasty, Total, Spinal Anesthesia Duration, Postoperative Care, Postoperative Acute Pain
- Keywords
- spinal anesthesia, machine learning, Knee arthroplasty, spinal anesthesia duration, Acute postoperative pain
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 145 participants (actual)
- Lead sponsor
- Kocaeli City HospitalOther government
- Study start
- 31 Oct 2025 (actual)
- Primary completion
- 14 May 2026 (actual)
- Study completion
- 1 Jun 2026 (actual)
- First posted
- 1 Dec 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Knee Arthroplasty Group | – | Procedure: Spinal Anesthesia (bupivacaine)The group of patients who will undergo knee replacement surgery under spinal anesthesia |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- 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.
Key inclusion criteria (5 listed)
- 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.
Key exclusion criteria (5 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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 | Procedure or surgery | Knee Arthroplasty Group |
Primary outcome
Predictive performance of machine learning
Time frame: 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)
2 secondary outcomes
spinal anesthesia termination time
Time frame: From the end of intrathecal injection (T₀) to complete motor recovery (T_end), expected within 6 hours post-injection.
Visual Analogue Scale
Time frame: From the end of intrathecal injection (T₀) to complete motor recovery (T_end), expected within 6 hours post-injection.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Aug 2026.
- Background: PubMed 38353755Bellini 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.
- Background: PubMed 40842529Cao 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.
- Background: PubMed 36824644Magdic 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.
- Background: PubMed 27352633Boublik 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.
- Background: PubMed 37321760Schubert 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.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Ahmet Yüksek, MD study director
Kocaeli City Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Spinal Anesthesia and Machine Learning, run by Kocaeli City Hospital at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 13 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Predictive performance of machine learning (time frame: From the end of intrathecal injection (T₀) to complete motor recovery (T_end), expected within 6 hours post-injection.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07256548, which also lists 5 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Aug 2026; AzTrials last refreshed its copy on 27 Oct 2026.