Predicting Neuromuscular Recovery in Surgical Patients Using Machine Learning

Despite emerging efforts to decrease residual paralysis and postoperative complications with the use of quantitative neuromuscular monitoring and reversal agents their incidences remain high. In an optimal setting, neuromuscular blocking agents are dosed in a way that there is no residual block at the end of surgery. The effect of neuromuscular blocking agents, however, is highly variable and is not only influenced by their dose, but also by several patient-related factors such as muscle status, metabolic activity, and anesthesia management. Accordingly, the duration of action is difficult to predict. The PINES project will use artificial intelligence methods to develop a model that can accurately predict the course of action of neuromuscular blocking agents. It will be used to predict time to complete neuromuscular recovery (train-of-four \[TOF\] ratio \>0.9) and may provide as a decision support in the individual management of timing and dosing of neuromuscular blocking drugs and their reversal agents. In a secondary analysis, the association between the choice of neuromuscular blocking agent and postoperative pulmonary complications will be evaluated.

NCT Number	NCT05471882
Lead Sponsor	University Hospital Ulm Collaborators: Technical University of Munich
Conditions	Residual Paralysis, Post Anesthesia, Postoperative Complications, Neuromuscular Blockade, Quantitative Neuromuscular Monitoring
Enrollment	240,000 participants
Start Date	2024-03-01
Primary Completion	2026-09-01 (estimated)
Study Completion	2027-01-01 (estimated)
Updated on ClinicalTrials.gov	2026-05-07