Application of Large Language Models Techniques to Post-ICU Syndrome Management in Critically Ill Patients: A Fully Longitudinal Mixed Study
- Conditions
- Post-Intensive Care Syndrome
- Registration Number
- NCT07141420
- Lead Sponsor
- The Affiliated Hospital Of Guizhou Medical University
- Brief Summary
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:
* Does the intervention (optimized program + LLMs) improve physical, psychological, cognitive, and social function recovery compared to standard care or the optimized program alone?
* How do patients experience and perceive the utility of LLMs in PICS self-management during recovery?
Researchers will compare three groups:
1. Group A (routine care)
2. Group B (optimized program without LLMs)
3. 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.
Participants will:
* Install and use the Kimi Smart Assistant LLM (Group C only) for health queries under nurse supervision.
* Complete standardized questionnaires at discharge (baseline), 7 days, 1 month, 3 months, and 6 months post-discharge:
* PICS Symptom Questionnaire (PICSQ)
* Pittsburgh Sleep Quality Index (PSQI)
* Anxiety (GAD-7) and Depression (PHQ-9) scales
* Self-Management Ability Scale (AHSMSRS)
* Attend semi-structured interviews (Group C only) at 3 and 6 months to share experiences with LLM use.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- ENROLLING_BY_INVITATION
- Sex
- All
- Target Recruitment
- 90
- ICU hospitalization duration > 24 hours.
- Age ≥ 18 years.
- Conscious at ICU discharge, able to communicate without barriers.
- Provide informed consent to participate.
- Regular access to and usage of smart electronic devices.
- Previous ICU admission (≥24h) within 3 months before the current hospitalization.
- Transferred to another ICU during the current hospitalization.
- Pre-existing cognitive impairment (Blessed Dementia Rating Scale [BDRS] score >4 before ICU admission).
- Severe communication barriers:
Hearing impairment Dysarthria Other conditions preventing follow-up assessments.
- Critically unstable condition preventing questionnaire completion.
- Infrequent/no experience using smart electronic devices (e.g., smartphones, tablets).
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Primary Outcome Measures
Name Time Method Change in Post-Intensive Care Syndrome (PICS) Symptom Severity Measured at baseline (pre-discharge), 1 month, 3 months, and 6 months post-discharge. \- Total score of the Chinese Version of the Post-Intensive Care Syndrome Questionnaire (PICSQ).
Domains: Physical function (6 items), cognitive impairment (6 items), psychological symptoms (6 items).
Scoring: 18 items × 0-3 points = 0-54 total; higher scores = worse symptoms.
* Total score of the Pittsburgh Sleep Quality Index (PSQI). Scoring: 7 components × 0-3 points = 0-21 total; higher scores = poorer sleep.
* Recall experiences measured by the Chinese ICU Memory Tool (ICUMT). Format: 14-item mixed open/closed questions about ICU admission, treatment, and discharge memories.
* Anxiety: GAD-7 score (0-21; higher = worse anxiety). Depression: PHQ-9 score (0-27; higher = worse depression).
- Secondary Outcome Measures
Name Time Method Self-Management Ability 1m, 3m, 6m post-discharge. \- Total score of the Adults Health Self-Management Ability Rating Scale (AHSMSRS).
Scoring: 38 items × 1-3 points = 38-114 total; higher scores = poorer self-management.Patient Experience with LLMs 3 months and 6 months post-discharge (Group C only). Qualitative insights from semi-structured interviews based on the Technology Acceptance Model (TAM).
Trial Locations
- Locations (1)
The Affiliated Hospital of Guizhou Medical University
🇨🇳Guiyang, Guizhou, China
The Affiliated Hospital of Guizhou Medical University🇨🇳Guiyang, Guizhou, China