Condition: Spinal Anesthesia · Machine Learning · Knee Arthroplasty, Total · Sponsor: Kocaeli City Hospital
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.
This description comes directly from the study's public registry record.
Sıddık Varolgüneş, MD · +905319179657 · varolgunes1235@gmail.com
Ahmet Yüksek, MD · +905326580351 · mdayuksek@hotmail.com
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| Kocaeli City Hospital | Kocaeli, İzmit, Turkey (Türkiye) | Recruiting |
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Source record: clinicaltrials.gov/study/NCT07256548