Assessing consciousness results in one of the most complex neurological diagnosis. Even more complex and uncertain is prognosticating on consciousness recovery. Currently, consciousness is assessed by using a six-items scale, the Coma Recovery Scale-Revised. Namely, scores on the sub-items can individually assign or not a specific level of consciousness to a patient. In our work, by using solely the six sub-items of the CRS-R, we implemented a clustering algorithm labeling patients with the Consciousness-Domains Index (CDI) starting from a dataset of 190 patients with a Disorder of Consciousness (DoC). Then, the CDI is compared with the clinical state at admission and at six months via univariate analysis. The number of clusters best dividing the groups resulted equal to two and the most influencing sub-items resulted the visual and motor one. The CDI closely resembles the clinical state at admission (CSA) (Cohen's k=0.85). On the other hand, when comparing CDI and CSA, a net improvement was found in the prognostic power of the neurological outcome at six months, targeted as presence/absence of a DoC ( ). Data-driven techniques pave the way for automated and model-based search of prognostic factors, together with the use of such prognostic factors in multivariate prognostic models. Future works will address the external validation of the CDI, together with the inclusion of the CDI in a multivariate supervised model, in order to assess the true potential of such novel index. Clinical Relevance- A completely data-driven index was derived from a clustering of CRS-R sub-items. It correlates with the neurological outcome at six months better than the state of consciousness at admission.