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Disorders of consciousness are a heterogeneous mixture of different diseasesor injuries. Although some indicators and models have been proposed forprognostication, any single method when used alone carries a high risk of falseprediction. This study aimed to develop a multidomain prognostic model thatcombines resting state functional MRI with three clinical characteristics topredict one year outcomes at the single-subject level. The model discriminatedbetween patients who would later recover consciousness and those who would notwith an accuracy of around 90% on three datasets from two medical centers. Itwas also able to identify the prognostic importance of different predictors,including brain functions and clinical characteristics. To our knowledge, thisis the first implementation reported of a multidomain prognostic model based onresting state functional MRI and clinical characteristics in chronic disordersof consciousness. We therefore suggest that this novel prognostic model isaccurate, robust, and interpretable.

Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics