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

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

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.

Inclusion Criteria patients :

• Length of stay in ICU ≥24h;
• Age ≥18 years old;
• Conscious when leaving ICU, communicating with investigators without barriers;
• Informed consent. Family members：
• One family member ≥18 years of age was selected for each patient;
• Assume the main role of caring for patients and medical decision-making;
• No history of mental illness or other serious organic diseases;
• Informed consent and voluntary participation in this study.

Exclusion Criteria patients :

• 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;
• Unable to complete the questionnaire survey due to serious illness.

Family members:

• Family members refuse to participate in the study due to their own reasons;
• Severe hearing and language impairment, unable to cooperate with researchers.