Assessment of electroencephalography and event-related potentials in unresponsive patients with brain injury.

OBJECTIVE

To investigate the predictors of clinical outcomes in unresponsive patients with acquired brain injuries.

METHODS

Patients with coma or disorders of consciousness were enrolled from August 2019 to March 2021. A retrospective analysis of demographics, etiology, clinical score, diagnosis, electroencephalography (EEG), and event-related potential (ERP) data from 1 week to 2 months after coma onset was conducted. Findings were assessed for predicting favorable outcomes at 6 months post-coma, and functional outcomes were determined using the Glasgow Outcome Scale-Extended (GOS-E).

RESULTS

Of 68 patients, 22 patients had a good neurological outcome at 6 months, while 11 died. Univariate analysis showed that motor response (Motor-R; p < 0.001), EEG pattern (p = 0.015), sleep spindles (p = 0.018), EEG reactivity (EEG-R; p < 0.001), mismatch negativity (MMN) amplitude at electrode Fz (FzMMNA; p = 0.001), P3a latency (p = 0.044), and P3a amplitude at electrode Cz (CzP3aA; p < 0.001) were significantly correlated with patient prognosis. Multivariable logistic regression analysis showed that FzMMNA, CzP3aA, EEG-R, and Motor-R were significant independent predictors of a favorable outcome. The sensitivity and specificity of FzMMNA (dichotomized at 1.16 μV) were 86.4% and 58.5%, and of CzP3aA (cut-off value 2.76 μV) were 90.9% and 70.7%, respectively. ERP amplitude (ERP-A), a combination of FzMMNA and CzP3aA, improved prediction accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.884. A model incorporating Motor-R, EEG-R, and ERP-A yielded an outstanding predictive performance (AUC=0.921) for a favorable outcome.

CONCLUSION

ERP-A and the prognostic model resulted in the efficient prediction of a favorable outcome in unresponsive patients.