Combining quantified EEG with clinical measures to better predict outcomes of acute disorders of consciousness.

Abstract

Objective: To explore the application of the neuronal recovery model (i.e., the ABCD model derived from EEG power spectral analysis) in forecasting outcomes for patients with acute disorders of consciousness (DOC).

Methods: Patients with acute DOC were enrolled, and clinical assessments, including the Glasgow Coma Scale (GCS), Full Outline of UnResponsiveness (FOUR), and Coma Recovery Scale-Revised (CRS-R) scores, along with electroencephalography (EEG), were documented on the first day post-enrollment. The ABCD model, derived from EEG power spectral data reflecting frequency bands, categorized brain activity into four distinct groups (A, B, C, D). Outcome prognoses were evaluated using the Glasgow Outcome Scale-Extended (GOSE) six months after enrollment. Statistical analyses were performed to assess the correlation between the ABCD model and clinical assessments, and to investigate the predictive value of EEG and clinical assessments for the long-term prognosis.

Results: A total of 93 patients with acute DOC were included; the median age was 64 years (interquartile range 52, 72), of which 52 patients had favorable outcomes. Significant correlations were observed between the ABCD model and both the FOUR and CRS-R scores. The CRS-R and ABCD model demonstrated relatively good predictive value for six-month prognoses, with Area Under the Curve (AUC) values of 0.695 and 0.678, respectively (P < 0.05). Furthermore, the combination of the CRS-R score and ABCD model exhibited the highest predictive value with an AUC of 0.746.

Conclusions: The ABCD model effectively predicted the prognosis of patients with acute DOC in combination with CRS-R.