Article

Establishing prognosis in patients in a persistent vegetative state
(VS) is still challenging. Neural networks underlying consciousness may be
regarded as complex systems whose outputs show a degree of unpredictability
experimentally quantifiable by means of nonlinear parameters such as approximate
entropy (ApEn). OBJECTIVE: The authors propose that the VS might be the result of
derangement of the above neural networks, with an ensuing decrease in complexity
and mutual interconnectivity: this might lead to a functional isolation within
the cerebral cortex and to a reduction in the chaotic behavior of its outputs,
with monotony taking the place of unpredictability. To test this hypothesis, the
authors investigated whether nonlinear dynamics methods applied to
electroencephalography (EEG) recordings may be able to predict outcomes. METHODS:
A total of 38 vegetative patients and 40 matched healthy controls were
investigated. At admission, all patients were assessed by means of the Extended
Glasgow Outcomes Coma Scale (E-GOS) and the Coma Recovery Scale-Revised (CRS-R).
At the same time an EEG recording was performed and used for time series analysis
and ApEn computation. Patients were clinically reassessed at 6 months from the
first evaluation. RESULTS: Mean ApEn values (0.73, standard deviation [SD] = 0.12
vs 0.97, SD = 0.02; P < .001) were lower in patients than in controls. Patients
with the lowest ApEn values either died (n = 14) or remained in a VS (n = 12),
whereas patients with the highest ApEn values became minimally conscious (n = 5)
or showed partial (n = 4) or full recovery (n = 3). CONCLUSIONS: These findings
suggest that dynamic correlates of neural residual complexity might help in
predicting outcomes in vegetative patients.

Langue : ANGLAIS