ArticleJournal of multidisciplinary healthcare2025
Clinical Characteristics of Auditory Mismatch Negativity in Patients with Chronic Disorders of Consciousness Following Intracerebral Haemorrhage: A Prognostic Prediction Study.
Article in Journal of multidisciplinary healthcare, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: To examine the clinical features of auditory mismatch negativity (MMN) and its potential utility in prognostic prediction in patients with chronic disorders of consciousness (DOC) following intracerebral haemorrhage (ICH). Methods: Between September 2023 and July 2024, 24 patients with chronic DOC following ICH were recruited. The Coma Recovery Scale-Revised (CRS-R) was used to assess the patients' level of consciousness, dividing them into vegetative state, minimally conscious state and escaped minimally conscious state groups. Auditory MMN data were collected from each group using an oddball paradigm, and differences in MMN amplitude and latency were compared. Follow-up was conducted 3 months later. Patients were categorised into favourable and unfavourable outcome groups based on the Glasgow Outcome Scale scores. The receiver operating characteristic curve method was utilised to evaluate the prognostic predictive power of significant indicators, establishing cut-off values. Results: The amplitude of auditory MMN was different in patients with varying levels of consciousness. The MMN amplitude at electrode Cz was associated with prognosis (P < 0.05), and the area under the curve was 0.850 (95% CI: 0.79-0.91). Moreover, MMN amplitudes at Cz were dichotomised at a value of 1.19 μV, with a sensitivity and specificity of 87.5% and 80.0%, respectively, for the prognosis prediction. The prediction value improved when combined with the CRS-R and the Glasgow Coma Scale. Conclusion: Auditory MMN amplitude, particularly at electrode Cz, serves as a reliable prognostic indicator for patients with chronic DOC following ICH. Its integration with clinical scales enhances predictive accuracy, offering valuable insights for clinical decision-making and patient management.
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