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Идёт набор NCT07256548

Machine Learning for Predicting Spinal Anesthesia Duration

— Наблюдательное Spinal Anesthesia Machine Learning Knee Arthroplasty, Total Spinal Anesthesia Duration

Ориентир для пациента и семьи

Простыми словами

Автоматическая сводка по структурированным данным реестра. Она помогает сориентироваться, но не заменяет официальный протокол или оценку врача.

Что изучают
В протоколе указаны: Spinal Anesthesia (bupivacaine).
Кому может быть актуально
Состояния в реестре: Spinal Anesthesia, Machine Learning, Knee Arthroplasty, Total, Spinal Anesthesia Duration. Базовые параметры: от 18 лет · Все.
Что важно проверить
Возраст, диагноз и пол — только базовые ориентиры. Предыдущее лечение, анализы и другие обязательные условия указаны ниже в критериях участия.
Где проводится
Turkey (Türkiye)
Следующий шаг
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Официальное название

Comparative Evaluation of Machine Learning Algorithms for Predicting Spinal Anesthesia Termination Time

Обзор

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.

Подробное описание

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.

Вмешательства

  • Процедура 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 admin

Первичные конечные точки

  • Predictive performance of machine learning [Срок оценки: From the end of intrathecal injection (T₀) to complete motor recovery (T_end), expected within 6 hours post-injection.]
Вторичные конечные точки (2)
  • spinal anesthesia termination time [Срок оценки: From the end of intrathecal injection (T₀) to complete motor recovery (T_end), expected within 6 hours post-injection.]
  • Visual Analogue Scale [Срок оценки: From the end of intrathecal injection (T₀) to complete motor recovery (T_end), expected within 6 hours post-injection.]

Критерии участия

Критерии включения

  • 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.

Критерии исключения

  • 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.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения
Когортное

Центры проведения

Turkey (Türkiye) · 1 центр
  • Kocaeli City Hospital — Kocaeli

Публикации

  • 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 38353755
  • Cao 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 40842529
  • Magdic 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 36824644
  • Boublik 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 27352633
  • Schubert 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 37321760

Идентификаторы

NCT: NCT07256548 · KSH_SVG_1

Первоисточники (государственные реестры)

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