Predicting Neuromuscular Recovery in Surgical Patients Using Machine Learning (PINES)

University Hospital Ulm

Status

Not yet enrolling

Conditions

Postoperative Complications

Residual Paralysis, Post Anesthesia

Neuromuscular Blockade

Study type

Observational

Funder types

Other

Identifiers

NCT05471882

TOF-R Prediction

Details and patient eligibility

About

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 >0.95) and may provide as a decision support in the individual management of timing and dosing of neuromuscular blocking drugs and their reversal agents.

Full description

The objective of the PINES project is to identify a model that can accurately predict 1) time to complete neuromuscular recovery, 2) optimal timing and dose of neuromuscular blocking agents at each time point during surgery, and 3) TOF ratio at the estimated end of surgery to assess residual paralysis. Furthermore, a prospective clinical pilot study will be conducted to compare anesthesiologist-predicted neuromuscular recovery with that of the algorithm.

The project consists of two main objectives:

I. Big data analysis

Establishing a data warehouse: Electronic registry data will be used.
Generation of prediction models: Classification models will first be used to identify and weight the relevant parameters collected during premedication and intraoperatively. These will form the basis for the training cohort, which can then be used to carry out a simulated real-time analysis of the data. To compare the models, the loss functions mean squared error, mean absolute error and Huber Loss will be calculated.

II. Prospective comparison of the prediction: machine-learning model vs. anesthesiologist

Using the validated final prediction model with the best accuracy, we will perform a prospective clinical pilot study. The cohort will include prospectively enrolled surgical patients which will be analysed by the prediction model as well as an experienced anesthesiologist. Surgeries requiring neuromuscular block of any non-depolarising neuromuscular blocking agent will be included. An anesthesiologist will prospectively estimate the time to neuromuscular recovery in 10-minute intervals beginning with the initial dose of neuromuscular blocking agent administration at anesthesia induction. Agreement between predictions and objectively measured time to TOF ratio >0.95 will be assessed using inter-rater correlation coefficients.

Enrollment

240,000 estimated patients

Sex

All

Ages

18+ years old

Volunteers

No Healthy Volunteers

Inclusion criteria

Adult patients (≥18 years) undergoing non-cardiac surgery receiving general anesthesia with intraoperative neuromuscular blocking agent administration and available TOF data.

Exclusion criteria