The primary objective of the SEASCAPE project is to design, develop, and to apply a clinical implementation tool of a machine learning (ML) and artificial intelligence (AI)-based co-pilot system for the real-time monitoring and control of nociception during general anesthesia (GA).
The ultimate clinical purpose is to optimize individualized pain management by achieving precise titration of intravenous opioids (specifically remifentanil), thereby minimizing the incidence of over- and under-dosing. This optimization is projected to enhance patient outcomes, reduce opioid-related complications, and improve overall cost-effectiveness of anesthetic procedures.
The main scientific question guiding this work is: Can a novel algorithm be generated and validated to provide superior analytical precision for analgesic management by reliably differentiating genuine nociceptive responses from confounding physiological variables-such as inadequate neuromuscular blockade or changes in depth of anesthesia-thereby significantly improving the clinical decision-making framework for intraoperative nociception control? This project addresses the recognized challenge in anesthesiology: defining an objective measure to quantify nociception and antinociception during GA.
Study Population: Patients scheduled for elective surgical procedures requiring general anesthesia (GA).
Existing Intervention: The standard anesthetic regimen includes continuous intravenous infusion of the remifentanil for intraoperative analgesia, typically governed by a Target Controlled Infusion (TCI) system utilizing a pharmacokinetic/pharmacodynamic (PK/PD) model (Eleveld TCI model).
Project Focus: The research seeks to improve the accuracy and efficacy of this existing analgesic strategy by integrating a multivariate patient data stream with the newly developed SEASCAPE co-pilot AI. This aims to refine the remifentanil dose predictions beyond the current TCI model's capabilities, personalized system.
Patients scheduled for elective surgery with general anesthesia.
Surgeries scheduled to last at least two hours.
Patients undergoing emergency surgery.
Pregnant women.
Presence of a mental or intellectual disability before the hospitalization.
Drug dependence.
Patients scheduled for elective surgery with general anesthesia and anesthesiologist.
Cohort
Prospective
Hemodynamic monitor, BIS, TOF, ANI, anesthesia machine and infusion pumps
Combination productExtraction of data obtained from hemodynamic monitoring, BIS, ANI, anesthesia machine and infusion pumps using Mindray's e-getaway system.
SEASCAPE
DeviceArtificial intelligence-assisted copilot system for nociception management. SEASCAPE First generation.
To develop and implement the SEASCAPE
To develop and implement a machine learning-based copilot system for monitoring nociception in patients under general anesthesia with remifentanil target-controlled infusion (TCI) analgesia using the Eleveld model, to assist clinicians in intraoperative decision-making and optimize nociception management.
Nociceptive patterns
To identify nociceptive patterns from changes in hemodynamic variables, electroencephalogram, ANI index variability, mechanical ventilator parameters, and the estimated dose of remifentanil according to the Eleveld TCI predictive model.
Patterns of anesthetic depth and inadequate muscle relaxation
Identify patterns of anesthetic depth and inadequate muscle relaxation, considering changes in hemodynamic variables, electroencephalogram and mechanical ventilator, quantified by serial neuromuscular monitoring and the dose of muscle relaxant administered.
Degree of usability of the SEASCAPE
Identify the degree of usability of the SEASCAPE system by users (anesthesiologists).
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