[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100615287":3},{"organization":4,"armGroups":7,"interventions":8,"overallOfficials":7,"centralContacts":15,"locations":21,"responsibleParty":38,"collaborators":7,"id":42,"slug":43,"hasResults":44,"nctId":45,"briefTitle":46,"officialTitle":46,"acronym":7,"eligibilityCriteria":47,"healthyVolunteers":44,"sex":48,"minAge":49,"maxAge":7,"enrollmentInfo":50,"targetDuration":7,"studyType":53,"phases":7,"briefSummary":54,"conditions":55,"keywords":57,"overallStatus":64,"whyStopped":7,"lastUpdateSubmitDate":65,"lastUpdatePostDateStruct":66,"startDateStruct":69,"completionDateStruct":71,"leadSponsor":73,"locationsCount":74},{"fullName":5,"class":6},"Hospital Nossa Senhora da Conceicao","OTHER",null,[9,12],{"type":6,"name":10,"description":11,"armGroupLabels":7,"otherNames":7},"AI-based pre-anesthetic assessment","Patients use an artificial intelligence (AI) tool based on a Large Language Model (LLM) in Portuguese to complete a pre-anesthetic self-assessment. The tool collects patient information and generates two outputs: Generic Orientations: General instructions sent directly to the patient to aid in surgical preparation.\n\nSpecific Assessment: A detailed evaluation, including recommendations and warning signs for medical use, which is not shared with the anesthesiologist performing the traditional evaluation.",{"type":6,"name":13,"description":14,"armGroupLabels":7,"otherNames":7},"Anesthesiologist-led pre-anesthetic evaluation","Each patient undergoes a standard pre-anesthetic evaluation conducted by an anesthesiologist, following routine clinical practice at both institutions. This evaluation is performed without access to the AI tool's results to ensure blinding.\n\nPurpose: To serve as the comparator for the AI-based assessment, allowing evaluation of concordance in risk assessment, quality of information collected, and clinical judgment.\n\nDetails: The anesthesiologist conducts a clinical interview and review of medical records, assessing factors such as the American Society of Anesthesiologists (ASA) classification, perioperative risk models (for instance, Ex-Care model), and potential complications.",[16],{"name":17,"role":18,"phone":19,"phoneExt":7,"email":20},"Andre P. Schmidt, MD, PhD","CONTACT","+5551996412212","aschmidt@ghc.com.br",[22],{"facility":23,"status":7,"city":24,"state":25,"zip":26,"country":27,"countryCode":28,"cosmosGeoPoint":29,"geoPoint":34,"contacts":35},"Hospital Nossa Senhora da Conceição (Grupo Hospitalar Conceição)","Porto Alegre","Rio Grande do Sul","91787-400","Brazil","BR",{"type":30,"coordinates":31},"Point",[32,33],-51.23019,-30.03283,{"lat":33,"lon":32},[36],{"name":17,"role":18,"phone":37,"phoneExt":7,"email":20},"+555133572419",{"type":39,"investigatorFullName":40,"investigatorTitle":41,"investigatorAffiliation":5,"oldNameTitle":7,"oldOrganization":7},"PRINCIPAL_INVESTIGATOR","Andre Prato Schmidt","MD, PhD (Anesthesiologist - Department Chair).","100615287","prospective-validation-of-an-artificial-intelligence-tool-for-pre-anesthetic-assessment-100615287",false,"NCT07290647","Prospective Validation of an Artificial Intelligence Tool for Pre-Anesthetic Assessment","Inclusion Criteria:\n\n* Patients aged 18 years or older\n* Patients scheduled for elective non-cardiac surgeries\n\nExclusion Criteria:\n\n* Patients undergoing diagnostic procedures with isolated sedation or local anesthesia\n* If a patient undergoes more than one surgical intervention during the same hospitalization, only the major procedure will be considered (i.e., additional procedures during the same admission are not eligible for separate inclusion).","ALL","18 Years",{"count":51,"type":52},270,"ESTIMATED","OBSERVATIONAL","This prospective observational cohort study aims to validate an artificial intelligence (AI) tool designed for pre-anesthetic assessment in Portuguese, tailored to the Brazilian healthcare context. Conducted at a single tertiary hospital, the study will enroll 270 adult patients (aged \\>18 years) scheduled for elective non-cardiac surgeries. Participants will use the AI tool to complete a self-assessment, generating general patient guidance and a detailed medical evaluation (the latter withheld from the anesthesiologist). A standard pre-anesthetic evaluation will then be performed by an anesthesiologist blinded to the AI results. A third blinded anesthesiologist will compare the assessments for accuracy, consistency, and risk identification (e.g., ASA classification and perioperative risk models). Primary outcome is concordance between AI and human assessments using Cohen's Kappa. Secondary outcomes include anesthesiologist perceptions of the tool's utility, impact on assessment quality, and patient usability challenges. The study poses minimal risks, with data collected over 24 months, and aims to enhance perioperative safety and efficiency in Brazil.",[56],"Preoperative Care",[58,59,60,61,62,63],"Artificial Intelligence","Large Language Model","Preoperative Evaluation","Perioperative Care","Risk Assessment","Observational Study","NOT_YET_RECRUITING","2025-12-05",{"date":67,"type":68},"2025-12-18","ACTUAL",{"date":70,"type":52},"2026-03-01",{"date":72,"type":52},"2028-06-30",{"name":5,"class":6},1]