Predicting coma and other low responsive patients outcome using event-related brain potentials: a meta-analysis.

Area of Science:

• Neuroscience
• Clinical Neurology
• Signal Processing

Background:

• Low-responsive patients, including those with stroke, trauma, or metabolic encephalopathy, present diagnostic challenges.
• Auditory event-related potentials (ERPs) are electrophysiological measures reflecting brain processing of auditory stimuli.
• Predicting awakening in these patients is crucial for guiding treatment and prognosis.

Purpose of the Study:

• To conduct a meta-analysis estimating the predictive power of auditory ERP components for awakening in low-responsive patients.
• To compare the predictive value of N100, MMN, and P300 components across different etiologies of encephalopathy.

Main Methods:

• A systematic review of MEDLINE and citation analysis was performed.
• Logistic regression analysis was applied to patient data (Glasgow Coma Scale <12) across and within specific etiologies.
• Data from multiple studies on N100, MMN, and P300 components were aggregated.

Main Results:

• The P300 component demonstrated the highest odds ratio (OR) for predicting awakening across all etiologies (OR=8.79), followed by MMN (OR=6.53) and N100 (OR=2.85).
• MMN and P300 showed significantly better predictive power than N100 for awakening.
• Specific ORs varied by etiology, with MMN and P300 consistently showing strong predictive values.

Conclusions:

• The MMN (Mismatch Negativity) and P300 components are reliable predictors of awakening in low-responsive patients.
• Prognostic assessments for these patients should incorporate MMN and P300 findings from auditory ERPs.