A tree of life? Multivariate logistic outcome-prediction in disorders of consciousness

Background: Clinical outcome of patients with disorders of consciousness (DOC) is seen as generally very poor. Here, we specify individual outcome chances for patients with DOC on the basis of clinical and event-related-potentials (ERPs) data and identify subgroups, who vary substantially regarding their outcome chances.

Methods: We employed data from 102 patients and used standard clinical protocol data (age, etiology, diagnosis, gender), sensory (N100, Mismatch-Negativity) and cognitive (P300, N400) ERPs to predict patients’ recovery rates.

Results: Two significant prediction models emerged: In both, subgroups of patients with good (51%, tree 1) to very good recovery chances (97%, tree 2) could be identified. The first model was obtained from standard clinical data. The second model included cognitive ERPs and resulted in considerably better patient classification. Moreover, when taking cognitive ERPs into account, the standard protocol data did not add further significant information, neither did sensory ERPs.

Conclusion: The presented information about outcome chances of individual patients with DOC will be vital for these patients and critical for clinical professionals who have to direct specialized treatments and council relatives. Legal guardians and families, in turn, need to know what to expect in the future in order to prepare for the challenges ahead.

KEYWORDS: Disorders of consciousness, unresponsive wakefulness syndrome, minimal consciousness state, logistic outcome-prediction