Outcome Prediction in Patients With Acute Disorders of Consciousness

Status Recruiting

Clinical Trial Summary

Prior research has indicated a link between outcomes in patients with disorders of consciousness (DOC) and their responses to emotional auditory stimuli. Given the high degree of uncertainty in acute situations, this study plans to record EEG responses and observe microexpressions in DOC patients during exposure to auditory stimuli, with the aim to identify more reliable prognostic indicators. The goal is to uncover potential patterns or signals that can contribute to a more comprehensive understanding of a patient's condition, and thereby provide more accurate prognostication.

Clinical Trial Description

This study intends to probe the potential of electroencephalography (EEG) and microexpression analysis for predicting the outcomes of acutely unresponsive patients within the intensive care unit (ICU). Previous research has illuminated a relationship between patients' emotional responses to auditory stimuli and their prognosis. However, the predictive utility of these responses in the context of acute conditions remains largely unexplored. At the onset of the study, participants will undergo EEG monitoring while exposed to a pre-determined set of emotional auditory stimuli. Additionally, the patients' facial expressions will be captured via video for subsequent microexpression analysis. Follow-up assessments of the patients' status will be conducted at three, six, and twelve-month intervals. Using cutting-edge analytical techniques, the goal is to decode these EEG and microexpression responses. This study aims to identify possible correlations between these findings, the patients' diagnoses, and their respective outcomes over time. ;

Study Design

Related Conditions & MeSH terms

Clinical Trial Details

NCT number	NCT04411719
Study type	Observational
Source	First Affiliated Hospital of Zhejiang University
Contact	Benyan Luo, Prof
Phone	0571-87236537
Email	luobenyan@zju.edu.cn
Status	Recruiting
Phase
Start date	January 1, 2022
Completion date	December 1, 2024

View Details

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