AI-Assisted Analgesia Copilot System

NCT07253012 | Recruiting DMC

• 1 other identifier: 250310009
• Study Type: observational
• Target: 150
• Locations: 1 country
• Sites: 2

Brief 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

Keywords

Nociception; Target Controlled Infusion; Remifentanil; Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

• 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.

  From the beginning of the anesthetic process to the end of the anesthesia

Secondary Outcomes (3)

• Nociceptive patterns

  From the beginning of the anesthetic process to the end of the anesthesia

• Patterns of anesthetic depth and inadequate muscle relaxation

  From the beginning of the anesthetic process to the end of the anesthesia

• Degree of usability of the SEASCAPE

  From the beginning of the anesthetic process to the end of the anesthesia

Study Arms (2)

Patients

Patients from 0 to 99 years of age from whom records of the received GA will be extracted.

Combination Product: Hemodynamic monitor, BIS, TOF, ANI, anesthesia machine and infusion pumps

Anaesthesiologist

Anesthesiologists who will use the Seascape in its pilot mode

Device: SEASCAPE

Interventions

SEASCAPE DEVICE

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

Anaesthesiologist

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

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

Also known as: SEASCAPE first generation

Patients

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients scheduled for elective surgery with general anesthesia and anesthesiologist.

You may qualify if:

• Patients scheduled for elective surgery with general anesthesia.
• Surgeries scheduled to last at least two hours.

You may not qualify if:

• Patients undergoing emergency surgery.
• Pregnant women.
• Presence of a mental or intellectual disability before the hospitalization.
• Drug dependence.
• Surgeries scheduled for more than 4 hours.
• Intraoperative complications requiring changes in routine behavior.

Sponsors & Collaborators

• Pontificia Universidad Catolica de Chile: lead

Study Sites (2)

Division de Anestesiologia

Santiago, Chile

NOT YET RECRUITING

Hospital Clinico UC Christus

Santiago, Chile

RECRUITING

Related Publications (3)

• Wingert T, Lee C, Cannesson M. Machine Learning, Deep Learning, and Closed Loop Devices-Anesthesia Delivery. Anesthesiol Clin. 2021 Sep;39(3):565-581. doi: 10.1016/j.anclin.2021.03.012. Epub 2021 Jul 12.

  PMID: 34392886 BACKGROUND

• Connor CW. Artificial Intelligence and Machine Learning in Anesthesiology. Anesthesiology. 2019 Dec;131(6):1346-1359. doi: 10.1097/ALN.0000000000002694.

  PMID: 30973516 BACKGROUND

• Eleveld DJ, Colin P, Absalom AR, Struys MMRF. Target-controlled-infusion models for remifentanil dosing consistent with approved recommendations. Br J Anaesth. 2020 Oct;125(4):483-491. doi: 10.1016/j.bja.2020.05.051. Epub 2020 Jul 9.

  PMID: 32654750 BACKGROUND

MeSH Terms

Interventions

Infusion Pumps

Intervention Hierarchy (Ancestors)

Equipment and Supplies; Artificial Organs; Surgical Equipment

Study Officials

• Victor Contreras, RN, MSN

  Pontificia Universidad Catolica de Chile

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Victor Contreras, RN, MSN

CONTACT

+56955049217; vecontre@uc.cl

Karen Azagra, RA

CONTACT

+56955049217; karen.azagra@uc.cl

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 5 Weeks
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator, Associate Researcher

Study Record Dates

• First Submitted: November 17, 2025
• First Posted: November 28, 2025
• Study Start: January 29, 2026
• Primary Completion (Estimated): October 27, 2026
• Study Completion (Estimated): October 27, 2027
• Last Updated: March 9, 2026

Record last verified: 2026-02

Locations

CL (2)