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

NCT ID: NCT07198490

Last Updated: 2025-09-30

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Total Enrollment: 50 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-10-01
• Study Completion Date: 2026-12-31

Brief 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

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Eligibility Criteria

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Xingui Dai

OTHER

Sponsor Role: lead

Responsible Party

Xingui Dai

The department of Critical Care Medicine

Responsibility Role: SPONSOR_INVESTIGATOR

Central Contacts

Xingui Dai

Role: CONTACT

20685562@qq.com

18175708210

References

Jones S, Schwartzbauer G, Jia X. Brain Monitoring in Critically Neurologically Impaired Patients. Int J Mol Sci. 2016 Dec 27;18(1):43. doi: 10.3390/ijms18010043.

Reference Type: BACKGROUND

PMID: 28035993 (View on PubMed)

Miller C, Armonda R; Participants in the International Multi-disciplinary Consensus Conference on Multimodality Monitoring. Monitoring of cerebral blood flow and ischemia in the critically ill. Neurocrit Care. 2014 Dec;21 Suppl 2:S121-8. doi: 10.1007/s12028-014-0021-9.

Reference Type: BACKGROUND

PMID: 25208667 (View on PubMed)

Gandee R, Miller C. Multimodality Monitoring: Toward Improved Outcomes. Semin Respir Crit Care Med. 2017 Dec;38(6):785-792. doi: 10.1055/s-0037-1608774. Epub 2017 Dec 20.

Reference Type: RESULT

PMID: 29262436 (View on PubMed)

Other Identifiers

Chenzhou People's Hospital

Identifier Type: -

Identifier Source: org_study_id