Deep Learning Framework for Continuous Depth of Anesthesia Forecasting

NCT07536230 | Not Yet Recruiting DMC

• 1 other identifier: AIBIS
• Study Type: observational
• Target: 115
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

BIS; BIS-EEG; Artifical Intelligence; Intraoperative; Machine Learning; Anesthesia; Anesthesia Awareness; Predictive Model

Outcome Measures

Primary Outcomes (3)

• Calibration error of the predictive uncertainty cone

  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.

  Continuous - Perioperative

• Mean Absolute Error (MAE)

  Mean Absolute Error (MAE)

  Continuous - perioperative

• Trend accuracy

  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.

  Continuous - perioperative

Secondary Outcomes (1)

• Root Mean Square Error (RMSE)

  Continuous - perioperative

Study Arms (2)

Prospective

Prospective Cohort

Restrospective

Retrospective Cohort

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Patients undergoing general anesthesia under continuous depth of anesthesia monitoring.

You may qualify if:

• Patients scheduled for elective surgery requiring general anesthesia.
• Procedures requiring continuous depth of anesthesia monitoring (BIS).

You may not qualify if:

• \- Procedures where the primary anesthetic plan does not involve continuous electronic data capture.

Sponsors & Collaborators

• Universitair Ziekenhuis Brussel: lead
• AZ Sint-Jan AV: collaborator

Study Sites (1)

AZ Sint-Jan AV

Bruges, 8000, Belgium

Location

MeSH Terms

Conditions

Intraoperative Awareness

Condition Hierarchy (Ancestors)

Intraoperative Complications; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Hugo Carvalho, MD, PhD

CONTACT

+32 50 45 24 19; hugo.nogueiracarvalho@azsintjan.be

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Target Duration: 1 Day
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 2, 2026
• First Posted: April 17, 2026
• Study Start (Estimated): June 1, 2026
• Primary Completion (Estimated): August 1, 2026
• Study Completion (Estimated): September 1, 2026
• Last Updated: April 17, 2026

Record last verified: 2026-03

Locations

BE (1)