Ignite Creation Date:
2025-12-25 @ 4:17 AM
Ignite Modification Date:
2025-12-26 @ 3:18 AM
Study NCT ID:
NCT07141420
Status:
ENROLLING_BY_INVITATION
Last Update Posted:
2025-08-26
First Post:
2025-08-19
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Application of Large Language Models Techniques to Post-ICU Syndrome Management in Critically Ill Patients: A Fully Longitudinal Mixed Study
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'C000657744', 'term': 'postintensive care syndrome'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'QUADRUPLE', 'whoMasked': ['PARTICIPANT', 'CARE_PROVIDER', 'INVESTIGATOR', 'OUTCOMES_ASSESSOR']}, 'primaryPurpose': 'SUPPORTIVE_CARE', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 90}}, 'statusModule': {'overallStatus': 'ENROLLING_BY_INVITATION', 'startDateStruct': {'date': '2025-06-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-08', 'completionDateStruct': {'date': '2026-02-20', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-08-19', 'studyFirstSubmitDate': '2025-08-19', 'studyFirstSubmitQcDate': '2025-08-19', 'lastUpdatePostDateStruct': {'date': '2025-08-26', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-08-26', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2026-01-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Change in Post-Intensive Care Syndrome (PICS) Symptom Severity', 'timeFrame': 'Measured at baseline (pre-discharge), 1 month, 3 months, and 6 months post-discharge.', 'description': '\\- Total score of the Chinese Version of the Post-Intensive Care Syndrome Questionnaire (PICSQ).\n\nDomains: Physical function (6 items), cognitive impairment (6 items), psychological symptoms (6 items).\n\nScoring: 18 items × 0-3 points = 0-54 total; higher scores = worse symptoms.\n\n* Total score of the Pittsburgh Sleep Quality Index (PSQI). Scoring: 7 components × 0-3 points = 0-21 total; higher scores = poorer sleep.\n* Recall experiences measured by the Chinese ICU Memory Tool (ICUMT). Format: 14-item mixed open/closed questions about ICU admission, treatment, and discharge memories.\n* Anxiety: GAD-7 score (0-21; higher = worse anxiety). Depression: PHQ-9 score (0-27; higher = worse depression).'}], 'secondaryOutcomes': [{'measure': 'Self-Management Ability', 'timeFrame': '1m, 3m, 6m post-discharge.', 'description': '\\- Total score of the Adults Health Self-Management Ability Rating Scale (AHSMSRS).\n\nScoring: 38 items × 1-3 points = 38-114 total; higher scores = poorer self-management.'}, {'measure': 'Patient Experience with LLMs', 'timeFrame': '3 months and 6 months post-discharge (Group C only).', 'description': 'Qualitative insights from semi-structured interviews based on the Technology Acceptance Model (TAM).'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Post-Intensive Care Syndrome']}, 'descriptionModule': {'briefSummary': 'The goal of this clinical trial is to evaluate whether Large Language Models (LLMs) combined with an optimized care program can effectively manage Post-Intensive Care Syndrome (PICS) in adult ICU survivors (aged ≥18 years) discharged from a tertiary hospital in China. The main questions it aims to answer are:\n\n* Does the intervention (optimized program + LLMs) improve physical, psychological, cognitive, and social function recovery compared to standard care or the optimized program alone?\n* How do patients experience and perceive the utility of LLMs in PICS self-management during recovery?\n\nResearchers will compare three groups:\n\n1. Group A (routine care)\n2. Group B (optimized program without LLMs)\n3. Group C (optimized program + LLMs) to see if adding LLMs significantly enhances PICS symptom management, patient self-efficacy, and quality of life over 6 months post-discharge.\n\nParticipants will:\n\n* Install and use the Kimi Smart Assistant LLM (Group C only) for health queries under nurse supervision.\n* Complete standardized questionnaires at discharge (baseline), 7 days, 1 month, 3 months, and 6 months post-discharge:\n\n * PICS Symptom Questionnaire (PICSQ)\n * Pittsburgh Sleep Quality Index (PSQI)\n * Anxiety (GAD-7) and Depression (PHQ-9) scales\n * Self-Management Ability Scale (AHSMSRS)\n* Attend semi-structured interviews (Group C only) at 3 and 6 months to share experiences with LLM use.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '100 Years', 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* ICU hospitalization duration \\> 24 hours.\n* Age ≥ 18 years.\n* Conscious at ICU discharge, able to communicate without barriers.\n* Provide informed consent to participate.\n* Regular access to and usage of smart electronic devices.\n\nExclusion Criteria:\n\n* Previous ICU admission (≥24h) within 3 months before the current hospitalization.\n* Transferred to another ICU during the current hospitalization.\n* Pre-existing cognitive impairment (Blessed Dementia Rating Scale \\[BDRS\\] score \\>4 before ICU admission).\n* Severe communication barriers:\n\nHearing impairment Dysarthria Other conditions preventing follow-up assessments.\n\n* Critically unstable condition preventing questionnaire completion.\n* Infrequent/no experience using smart electronic devices (e.g., smartphones, tablets).'}, 'identificationModule': {'nctId': 'NCT07141420', 'briefTitle': 'Application of Large Language Models Techniques to Post-ICU Syndrome Management in Critically Ill Patients: A Fully Longitudinal Mixed Study', 'organization': {'class': 'OTHER', 'fullName': 'The Affiliated Hospital Of Guizhou Medical University'}, 'officialTitle': 'Application of Large Language Models Techniques to Post-ICU Syndrome Management in Critically Ill Patients: A Fully Longitudinal Mixed Study', 'orgStudyIdInfo': {'id': '2024-171'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'OTHER', 'label': 'Routine Care Group', 'description': 'Participants receive standard post-ICU follow-up care according to hospital protocols . This includes routine health assessments and general rehabilitation guidance at designated intervals (discharge, 7 days, 1/3/6 months post-discharge). No structured PICS management program or AI technology is provided.', 'interventionNames': ['Behavioral: Routine Care']}, {'type': 'OTHER', 'label': 'Optimized Program Group', 'description': 'Participants receive an evidence-based, optimized PICS management program developed using the Health Promotion Model (HPM). This includes personalized rehabilitation plans, psychological support, and education tailored to PICS symptoms. Interventions are delivered by clinical staff at discharge, 7 days, and 1/3/6 months post-discharge. No AI/LLM technology is used.', 'interventionNames': ['Behavioral: Health Promotion Model-Based Optimized Program', 'Behavioral: LLM-Enhanced Optimized Program']}, {'type': 'OTHER', 'label': 'Optimized Program + LLMs Group', 'description': 'Participants receive the same optimized PICS program as Group B, enhanced with Large Language Models (LLMs). Key components:\n\nPersonalized AI-generated plans: ChatGPT-4 synthesizes patient data (baseline + follow-ups) to create monthly rehabilitation plans, reviewed by a multidisciplinary expert team.\n\nLLM access: Installation of "Kimi Smart Assistant" for daily health queries. Safety protocols: Patients must validate LLM advice with nurses via WeChat before use .\n\nPhased intervention:\n\nPre-discharge: LLM training + baseline plan generation.\n\n1 month: Plan updates based on new data. 3/6 months: Plan updates + semi-structured interviews about LLM experience.', 'interventionNames': ['Behavioral: LLM-Enhanced Optimized Program']}], 'interventions': [{'name': 'Routine Care', 'type': 'BEHAVIORAL', 'description': 'Participants receive standard post-ICU follow-up care according to hospital protocols . This includes routine health assessments and general rehabilitation guidance at designated intervals (discharge, 1/3/6 months post-discharge). No structured PICS management program or AI technology is provided.', 'armGroupLabels': ['Routine Care Group']}, {'name': 'Health Promotion Model-Based Optimized Program', 'type': 'BEHAVIORAL', 'description': 'An evidence-based, multidisciplinary rehabilitation protocol for Post-Intensive Care Syndrome (PICS) management, developed using the Health Promotion Model (HPM). It includes:\n\nPersonalized rehabilitation plans addressing physical, cognitive, and psychological recovery.\n\nStructured follow-up at discharge, 1/3/6 months post-discharge. Components: Physical function training, cognitive exercises, anxiety/depression coping strategies, and sleep hygiene education.\n\nDelivery: Clinician-guided (no AI/technology involved). Developed via literature review and validated by ICU physicians and nursing experts .', 'armGroupLabels': ['Optimized Program Group']}, {'name': 'LLM-Enhanced Optimized Program', 'type': 'BEHAVIORAL', 'description': 'Combines the HPM-Based Optimized Program with Large Language Model (LLM) technology for dynamic personalization:\n\nAI-generated rehabilitation plans: ChatGPT-4 synthesizes patient data (baseline + follow-ups) to create/update monthly plans, reviewed by a multidisciplinary expert team.\n\nPatient-facing LLM tool: "Kimi Smart Assistant" installed for daily health queries under strict safety protocols (all outputs validated by nurses via WeChat).\n\nPhased implementation:\n\nPre-discharge: LLM training + baseline plan generation. 1/3/6 months: Plan updates + outcome tracking. 3/6 months: Semi-structured interviews on LLM experience. Includes LLM usage guidelines and expert validation safeguards .', 'armGroupLabels': ['Optimized Program + LLMs Group', 'Optimized Program Group']}]}, 'contactsLocationsModule': {'locations': [{'zip': '550004', 'city': 'Guiyang', 'state': 'Guizhou', 'country': 'China', 'facility': 'The Affiliated Hospital of Guizhou Medical University', 'geoPoint': {'lat': 26.58333, 'lon': 106.71667}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'The Affiliated Hospital Of Guizhou Medical University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}