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

TL;DR

This study develops a multidomain prognostic model combining brain functional networks and clinical data to accurately predict one-year outcomes in patients with chronic disorders of consciousness, outperforming single-method approaches.

Contribution

First implementation of a multidomain prognostic model using resting state fMRI and clinical features for chronic disorders of consciousness.

Findings

Achieved around 90% accuracy in predicting patient outcomes

Identified key brain and clinical predictors of prognosis

Model is robust, interpretable, and applicable across multiple datasets

Abstract

Disorders of consciousness are a heterogeneous mixture of different diseases or injuries. Although some indicators and models have been proposed for prognostication, any single method when used alone carries a high risk of false prediction. This study aimed to develop a multidomain prognostic model that combines resting state functional MRI with three clinical characteristics to predict one year outcomes at the single-subject level. The model discriminated between patients who would later recover consciousness and those who would not with an accuracy of around 90% on three datasets from two medical centers. It was also able to identify the prognostic importance of different predictors, including brain functions and clinical characteristics. To our knowledge, this is the first implementation reported of a multidomain prognostic model based on resting state functional MRI and clinical characteristics in chronic disorders of consciousness. We therefore suggest that this novel prognostic model is accurate, robust, and interpretable.