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.
Length of stay in ICU ≥24h;
Age ≥18 years old;
Conscious when leaving ICU, communicating with investigators without barriers;
Informed consent. Family members：
Have been in ICU for more than 24h within 3 months before this admission;
Transferred to another ICU;
Cognitive impairment existed before ICU admission (BDRS > 4 points);
Severe hearing impairment, dysarthria, etc., which cannot be followed up;
Stage one involved studying patients discharged from the ICU and their families to investigate the occurrence and factors influencing post-intensive care syndrome (PICS), identifying various developmental patterns of PICS. A minimum sample size of 840 patients and 420 family members was established. Stage two focused on developing and evaluating a machine learning-based early warning model for PICS. The model construction utilized data from stage one, while the evaluation included 525 patients and 393 family members who met the exclusion criteria.
Cohort
Prospective
Not listed
Correlation scale score
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;
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The Affiliated Hospital Of Guizhou Medical University
Lead sponsor
Chinese nursing association
Collaborator
