Clinical signs and early prognosis in vegetative state: A decisional tree, data-mining study

Abstract

Background and purpose: Residual brain function has been documented in vegetative state patients, yet early prognosis remains difficult. The purpose of this study was to identify by artificial intelligence procedures (classification and regression trees, data-mining) the significant neurological signs correlated to and predictive of outcome.

Methods: Three hundred and thirty-three patients in vegetative state of traumatic or non-traumatic aetiology referred to the S.Anna Institute were retrospectively studied. Twenty-two neurological signs were assessed according to criteria included in the UNI ENI ISO 9001 : 2000 quality standards at admission (Time₀) and after 50, 100 or 180 days and entered into a CART (classification and regression tree) data-mining procedure with a decisional tree j48 (Weka software and 10-fold cross-validation). Outcome was conventionally rated by the Glasgow outcome scale.

Results and conclusions: Re-appearance with proper timing of spontaneous motility, eye tracking and oculo-cephalic reflex and disappearance of oral automatisms proved highly correlated to outcome and allowed early and reliable prognosis. These findings are consistent with the brain functional organization thought to sustain consciousness and warrant systematic investigation. Classification and regression trees and data-mining procedures proved applicable in neurology to sort out significant clinical signs also in clinical conditions characterized by paucity of signs such as the vegetative state. Extended application in clinical medicine is conceivable based on the approach peculiarities.

• Vegetative state
• early prognosis
• data mining
• classification and regression tree