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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

Recruiting
Conditions
Sleep Disorder
Cognitive Impairment
Intensive Care Unit Syndrome
Prediction
Memory 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
Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Primary Outcome Measures
NameTimeMethod
Correlation scale score2024-2026

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
NameTimeMethod

Trial Locations

Locations (1)

Affiliated Hospital of Guizhou Medical University

🇨🇳

GuiYang, Guizhou, China

Affiliated Hospital of Guizhou Medical University
🇨🇳GuiYang, Guizhou, China
Tingrui WANG
Contact
19117899885
W19117899885@163.com

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