Machine Learning-based Longitudinal Study of Post-ICU Syndrome Development Trajectory in Critically Ill Patients and Construction of Clinical Early Warning Models: a Research Protocol for Longitudinal Study
- Conditions
- Sleep DisorderCognitive ImpairmentIntensive Care Unit SyndromePredictionMemory Disorders
- Registration Number
- NCT06427265
- Lead Sponsor
- The Affiliated Hospital Of Guizhou Medical University
- Brief Summary
This project intends to track and evaluate whether post-ICU syndrome will occur 7 days, 1 month, 3 months and 6 months after ICU patients are transferred out of the ICU through a longitudinal study, apply the latent category growth model to identify different trajectory patterns of post-ICU syndrome in critically ill patients, and use modern machine learning models to build an early warning model of the trajectory patterns of post-ICU syndrome.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 840
Not provided
Not provided
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Correlation scale score 2024-2026 Patient:
1. Post-icu syndrome assessment of ICU survivors: Healthy Aging Brain Care monitoring(HABC Monitor) score;
2. ICU related Memory: Intensive Care Unit Memory Tool (ICUMT);
3. Sleep quality: Richards-Campbell Sleep Questionnaire (RCSQ);
4. Perceived Social Support Scale (PSSS).
Family members:
1. Sleep quality of family members: Richards-Campbell Sleep Questionnaire (RCSQ);
2. anxiety and Depression of family members: Hospital anxiety and Depression Scale score;
3. Family fatigue: Scores of fatigue rating Scale;
4. PTSD of family members: Event Impact Scale score;
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
Affiliated Hospital of Guizhou Medical University
🇨🇳GuiYang, Guizhou, China
Affiliated Hospital of Guizhou Medical University🇨🇳GuiYang, Guizhou, ChinaTingrui WANGContact19117899885W19117899885@163.com
