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Updated: Feb 1, 2026

Assessment and Communication for People with Disorders of Consciousness
Published on: August 1, 2017
Looking toward predicting functional recovery in disorders of consciousness: can sensorimotor integration help us?
Luana Billeri1, Antonino Naro1, Antonino Leo1
1a Behavioral and Robotic Neurorehabilitation Laboratory , IRCCS Centro Neurolesi "Bonino-Pulejo" , Messina , Italy.
Primary Objective:
Only a few objective prognostic markers are available for patients with disorders of consciousness (DoC). We assessed whether the magnitude of short-latency afferent inhibition (SAI) might be a useful predictor of responsiveness recovery and functional outcome in patients with DoC.
Research Design:
We enrolled 40 patients with prolonged Minimally Conscious State (MCS) and Unresponsive Wakefulness Syndrome (UWS) in a longitudinal, observational study.
Methods And Procedures:
Clinical features (including Coma Recovery Scale-Revised, CRS-R, and Glasgow Outcome Scale, GOS) and SAI were collected at the study entry and after 18 months from study inclusion, to assess a correlation between SAI and the clinical outcome.
Main Outcomes And Results:
At the follow-up, 19 patients remained in their baseline condition, whereas 7 UWS evolved into MCS or emerged-from-MCS (EMCS), eight MCS evolved into EMCS, and two MCS- evolved into MCS+. Two UWS and one MCS+ died for cardiopulmonary complications. The patients who showed the highest GOS, the highest CRS-R and the lowest SAI strength at study entry, improved at the follow-up.
Conclusions:
Our findings suggest that an objective and simple neurophysiologic measure as SAI strength could provide useful information to predict the outcome and the behavioral responsiveness of patients with DoC.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

