Spinal Anesthesia Duration

Spinal anesthesia is the most widely employed procedure for lower limb orthopedic operations, as it is very cost-effective and simple to apply. These advantages could be restricted because presently existing local anesthetic drugs had relatively short length of action. (1) Spinal anesthesia with 0.5% heavy bupivacaine (hyperbaric) is a common technique, still there was burden of its short duration of action. To overcome this issue, there was a continuous search for an ideal adjuvant.(2) Adjuvants were mostly added to local anesthetic drugs to increase their effectiveness, speedy onset, increase the period of the block, and reduce the local anesthetics dosage, thus reduction their adverse effects.(3) Such adjuvants had been beneficial in extension of analgesia along with initiation of movement though their related side effects.(4)

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.