[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100612394":3},{"organization":4,"armGroups":7,"interventions":19,"overallOfficials":32,"centralContacts":36,"locations":44,"responsibleParty":66,"collaborators":10,"id":69,"slug":70,"hasResults":71,"nctId":72,"briefTitle":73,"officialTitle":74,"acronym":29,"eligibilityCriteria":75,"healthyVolunteers":71,"sex":76,"minAge":10,"maxAge":10,"enrollmentInfo":77,"targetDuration":80,"studyType":81,"phases":10,"briefSummary":82,"conditions":83,"keywords":88,"overallStatus":62,"whyStopped":10,"lastUpdateSubmitDate":92,"lastUpdatePostDateStruct":93,"startDateStruct":96,"completionDateStruct":98,"leadSponsor":100,"locationsCount":101},{"fullName":5,"class":6},"Pontificia Universidad Catolica de Chile","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"Patients",null,"Patients from 0 to 99 years of age from whom records of the received GA will be extracted.",[13],"Combination Product: Hemodynamic monitor, BIS, TOF, ANI, anesthesia machine and infusion pumps",{"label":15,"type":10,"description":16,"interventionNames":17},"Anaesthesiologist","Anesthesiologists who will use the Seascape in its pilot mode",[18],"Device: SEASCAPE",[20,27],{"type":21,"name":22,"description":23,"armGroupLabels":24,"otherNames":25},"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.",[9],[26],"SEASCAPE first generation",{"type":28,"name":29,"description":30,"armGroupLabels":31,"otherNames":10},"DEVICE","SEASCAPE","Artificial intelligence-assisted copilot system for nociception management. SEASCAPE First generation.",[15],[33],{"name":34,"affiliation":5,"role":35},"Victor Contreras, RN, MSN","PRINCIPAL_INVESTIGATOR",[37,41],{"name":34,"role":38,"phone":39,"phoneExt":10,"email":40},"CONTACT","+56955049217","vecontre@uc.cl",{"name":42,"role":38,"phone":39,"phoneExt":10,"email":43},"Karen Azagra, RA","karen.azagra@uc.cl",[45,60],{"facility":46,"status":47,"city":48,"state":10,"zip":10,"country":49,"countryCode":50,"cosmosGeoPoint":51,"geoPoint":56,"contacts":57},"Division de Anestesiologia","NOT_YET_RECRUITING","Santiago","Chile","CL",{"type":52,"coordinates":53},"Point",[54,55],-70.64827,-33.45694,{"lat":55,"lon":54},[58],{"name":59,"role":38,"phone":39,"phoneExt":10,"email":40},"Victor Contreras, MSN, RN",{"facility":61,"status":62,"city":48,"state":10,"zip":10,"country":49,"countryCode":50,"cosmosGeoPoint":63,"geoPoint":65,"contacts":10},"Hospital Clinico UC Christus","RECRUITING",{"type":52,"coordinates":64},[54,55],{"lat":55,"lon":54},{"type":35,"investigatorFullName":67,"investigatorTitle":68,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"Victor Contreras, MSN","Principal Investigator, Associate Researcher","100612394","ai-assisted-analgesia-copilot-system-100612394",false,"NCT07253012","AI-Assisted Analgesia Copilot System","AI-assisted Analgesia Copilot System for Proper Management of Nociception","Inclusion Criteria:\n\n* Patients scheduled for elective surgery with general anesthesia.\n* Surgeries scheduled to last at least two hours.\n\nExclusion Criteria:\n\n* Patients undergoing emergency surgery.\n* Pregnant women.\n* Presence of a mental or intellectual disability before the hospitalization.\n* Drug dependence.\n* Surgeries scheduled for more than 4 hours.\n* Intraoperative complications requiring changes in routine behavior.","ALL",{"count":78,"type":79},150,"ESTIMATED","5 Weeks","OBSERVATIONAL","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).\n\nThe 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.\n\nThe 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.\n\nStudy Population: Patients scheduled for elective surgical procedures requiring general anesthesia (GA).\n\nExisting 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\u002Fpharmacodynamic (PK\u002FPD) model (Eleveld TCI model).\n\nProject 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.",[84,85,86,87],"Nociception","Artificial Intelligence (AI)","Target Controlled Infusion (TCI)","Remifentanil Consumption",[84,89,90,91],"Target Controlled Infusion","Remifentanil","Artificial Intelligence","2026-03-06",{"date":94,"type":95},"2026-03-09","ACTUAL",{"date":97,"type":95},"2026-01-29",{"date":99,"type":79},"2027-10-27",{"name":5,"class":6},2]