The Consciousness Domain Index: External Validation and Prognostic Relevance of a Data-Driven Assessment.

Abstract

The prognosis of neurological outcomes in patients with prolonged Disorders of Consciousness (pDoC) has improved in the last decades. Currently, the level of consciousness at admission to post-acute rehabilitation is diagnosed by the Coma Recovery Scale-Revised (CRS-R) and this assessment is also part of the used prognostic markers. The consciousness disorder diagnosis is based on scores of single CRS-R sub-scales, each of which can independently assign or not a specific level of consciousness to a patient in a univariate fashion. In this work, a multidomain indicator of consciousness based on CRS-R sub-scales, the Consciousness-Domain-Index (CDI), was derived by unsupervised learning techniques. The CDI was computed and internally validated on one dataset (N=190) and then externally validated on another dataset (N=86). Then, the CDI effectiveness as a short-term prognostic marker was assessed by supervised Elastic-Net logistic regression. The prediction accuracy of the neurological prognosis was compared with models trained on the level of consciousness at admission based on clinical state assessments. CDI-based prediction of emergence from a pDoC improved the clinical assessment-based one by 5.3% and 3.7%, respectively for the two datasets. This result confirms that the data-driven assessment of consciousness levels based on multidimensional scoring of the CRS-R sub-scales improve short-term neurological prognosis with respect to the classical univariately-derived level of consciousness at admission.