Prognostic models for prolonged disorders of consciousness: an integrative review

Disorders of consciousness (DoC) are acquired conditions of severe altered consciousness. During the past decades, some prognostic models for DoC have been explored on the basis of a variety of predictors, including demographics, neurological examinations, clinical diagnosis, neurophysiology and brain images. In this article, a systematic review of pertinent literature was conducted. We identified and evaluated 21 prognostic models involving a total of 1201 DoC patients. In terms of the reported accuracies of predicting the prognosis of DoC, these 21 models vary widely, ranging from 60 to 90%. Using improvement of consciousness level as favorable outcome criteria, we performed a quantitative meta-analysis, and found that the pooled sensitivity and specificity of the hybrid model that combined more than one technique were both superior to those of any single technique, including EEG and fMRI at the tasks and resting state. These results support the view that any single technique has its own advantages and limitations; and the integrations of multiple techniques, including diverse brain images and different paradigms, have the potential to improve predictive accuracy for DoC. Then, we provide methodological points of view and some prospects about future research. Totally, in comparison to a great many diagnostic methods for the DoC, the research of prognostic models is sparse and preliminary, still largely in its infancy with many challenges and opportunities.