Construction of a Prognostic Model for Severe Brain-Injured Patients Based on Integrated Metabolic-Neurological Monitoring (CPM-Brain)

Xingui Dai

Status: Not yet enrolling

Conditions: Brain Injuries

Study type: Observational

Funder types: Other

Identifiers

NCT07198490

Chenzhou People's Hospital

Details and patient eligibility

About

This is a prospective, observational cohort study aimed at constructing a machine learning-based prognostic model for severe brain-injured patients. The study will synchronously collect continuous glucose monitoring (CGM), electroencephalography (EEG), near-infrared spectroscopy (fNIRS), transcranial Doppler (TCD), and serum neuronal injury biomarkers (NSE, S100β) within 72 hours post-injury. The goal is to investigate the correlation between glycemic variability (GV) and neurological function and to develop an integrated model for early prediction of 3-6 month neurological outcomes (GOSE score).

Full description

This study intends to enroll 50 adult patients with brain injury admitted to the ICU. Multimodal monitoring data will be collected prospectively. Machine learning algorithms will be used to integrate the data and build a predictive model. The study will test whether integrated metabolic-neurological monitoring outperforms traditional single-parameter prognostic methods.

Enrollment: 50 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Diagnosis of severe TBI, large-volume stroke, or HIE
• Expected ICU stay >72 hours
• Informed consent from legal surrogate

Exclusion criteria

• Terminal organ failure
• Pre-existing severe neurological disease
• Skull defect preventing monitoring
• Pregnancy or lactation