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AI-Assisted Analgesia Copilot System

NCT07253012 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 150

Last updated 2026-03-09

No results posted yet for this study

Summary

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.

Conditions

  • Nociception
  • Artificial Intelligence (AI)
  • Target Controlled Infusion (TCI)
  • Remifentanil Consumption

Interventions

COMBINATION_PRODUCT

Hemodynamic monitor, BIS, TOF, ANI, anesthesia machine and infusion pumps

Extraction of data obtained from hemodynamic monitoring, BIS, ANI, anesthesia machine and infusion pumps using Mindray's e-getaway system.

DEVICE

SEASCAPE

Artificial intelligence-assisted copilot system for nociception management. SEASCAPE First generation.

Sponsors & Collaborators

  • Pontificia Universidad Catolica de Chile

    lead OTHER

Principal Investigators

  • Victor Contreras, RN, MSN · Pontificia Universidad Catolica de Chile

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-01-29
Primary Completion
2026-10-27
Completion
2027-10-27

Countries

  • Chile

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07253012 on ClinicalTrials.gov