NCT07198490

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

Construction of a Prognostic Model for Severe Brain-Injured Patients Based on Integrated Metabolic-Neurological Monitoring: A Prospective Observational Study

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 50 (estimated)
• Sponsor: Xingui Dai · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: —

Summary

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).

Detailed 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.

Conditions

• Brain Injuries

Timeline

• Start date: 2025-10-01
• Primary completion: 2026-06-30
• Completion: 2026-12-31
• First posted: 2025-09-30
• Last updated: 2025-09-30