Development and Validation of a Machine Learning Algorithm for Prediction of Complete Neuromuscular Recovery in Adult Surgical Patients
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 发起方
- 入组人数
- 240,000
- 试验地点
- 4
- 主要终点
- complete neuromuscular recovery
研究概览
简要总结
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.
详细描述
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
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Adult patients (≥18 years) undergoing non-cardiac surgery receiving general anesthesia with intraoperative neuromuscular blocking agent administration and available TOF data.
排除标准
- 未提供
结局指标
主要结局
complete neuromuscular recovery
时间窗: intraoperative
predicting the time to complete neuromuscular recovery (defined as TOF ratio \>0.9) from any time point of surgery
complete neuromuscular recovery
时间窗: intraoperative
predicting the time to complete neuromuscular recovery (defined as TOF ratio \>0.9) from any time point of surgery
次要结局
未报告次要终点
研究者
Flora Scheffenbichler
Clinician Scientist
University Hospital Ulm