Zunyi Medical University

Introduction：Delirium is a prevalent psychiatric disorder observed in the Intensive Care unit(ICU) hat; often aggravates patients; overall health status, extends the duration of mechanical ventilation, prolongs ICU stays, and causes persistent or long-term cognitive impairment. A plethora of studies have indicated that the frequency, duration, and severity of delirium during hospitalization are significant risk factors for cognitive impairment in patients following transfer from the ICU. However, existing research has predominantly focused on cross-sectional analyses without delving into the influencing factors among patient subgroups. Consequently, the aim of this study is to employ the Latent Growth Curve Model (LGCM) and the Latent Class Growth Model (LCGM) model to analyze the trajectory of cognitive development and influencing factors of patients with delirium in the ICU one year after discharge.
Methods and analysis：This prospective study aims to investigate the trajectory of cognitive level variations of patients with delirium in ICU for one year. It is planned to recruit 240 participants and gather comprehensive data including general demographics, disease-related information, scores from the Minimum Mental State Examination, Hospital Anxiety and Depression Scale, and the Pittsburgh Sleep Quality Index. All data will be followed up using the same schedule on the day of transfer, 1 month after transfer, 3 months after transfer, 6 months after transfer and 1 year after transfer. Ultimately, the investigators will employ Latent Growth Curve Model and Latent Class Growth Model to analyze the trajectory of cognitive changes and identify potential categories, in addition to utilizing logistic regression modeling to explore influencing factors. The results of this study provide a theoretical framework for the clinical implementation of precision nursing interventions within this demographic, so as to prevent and reduce the decline of cognitive function and improve the quality of life of patients.

The goal of this study is to determin the MRI features associated with cerebral palsy and to develop prediction models of pediatric disorders by combining MRI with artificial intelligence.
The main questions it aims to answer are:
* How to achieve features on conventional MRI associated with cerebral palsy?
* How to predict the risk of cerebral palsy in infants aged 6 to 2 years based on conventional MRI and deep learning? Researchers will compare characteristics of periventricular white matter injury with cerebral palsy to those without cerebral palsy.
Participants will be asked to provide MRI data, clinical diagnoses information, and follow-up outcomes.