Validation of a Deep Learning Framework for Continuous Forecasting of Pharmacodynamic Responses and Physiological Trajectories During General Anesthesia
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 115
- 试验地点
- 1
- 主要终点
- Calibration error of the predictive uncertainty cone
研究概览
简要总结
The integration of Artificial Intelligence (AI) in anesthesiology offers the potential to shift patient monitoring from reactive to predictive. Deep learning architectures, specifically Long Short-Term Memory (LSTM) networks, excel at processing complex, time-series data to forecast future clinical states.
While standard PK/PD models (such as the state of the art Eleveld model for Propofol and Remifentanil) estimate target-site drug concentrations (Ce), they do not account for real-time, patient-specific dynamic responses. This study aims to deploy an AI framework designed to predict future physiological states.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
入排标准
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Patients scheduled for elective surgery requiring general anesthesia.
- •Procedures requiring continuous depth of anesthesia monitoring (BIS).
排除标准
- •- Procedures where the primary anesthetic plan does not involve continuous electronic data capture.
研究组 & 干预措施
Prospective
Prospective Cohort
Restrospective
Retrospective Cohort
结局指标
主要结局
Calibration error of the predictive uncertainty cone
时间窗: Continuous - Perioperative
Calibration error of the predictive uncertainty cone - Calibration error of the predictive uncertainty cone is the discrepancy between a model's stated confidence level (e.g., predicting that 95% of future values will fall within a specific range) and the actual frequency with which the true values actually land inside that predicted boundary.
Mean Absolute Error (MAE)
时间窗: Continuous - perioperative
Mean Absolute Error (MAE)
Trend accuracy
时间窗: Continuous - perioperative
Trend accuracy measures a predictive model's ability to correctly forecast the future direction and rate of change of a variable (such as whether a patient's anesthesia depth is actively lightening or deepening), independent of the absolute numerical error at any single point in time.
次要结局
- Root Mean Square Error (RMSE)(Continuous - perioperative)