Citation

Lucca LF, Lofaro D, Leto E, Ursino M, Rogano S, Pileggi A, Vulcano S, Conforti D, Tonin P, Cerasa A. Front. Hum. Neurosci. 2020; 14: e570544.

Copyright

(Copyright © 2020, Frontiers Research Foundation)

DOI

10.3389/fnhum.2020.570544

PMID

33192402 PMCID

Abstract

In this study, we sought to assess the predictors of outcome in patients with disorders of consciousness (DOC) after severe traumatic brain injury (TBI) during neurorehabilitation stay. In total, 96 patients with DOC (vegetative state, minimally conscious state, or emergence from minimally conscious state) were enrolled (69 males; mean age 43.6 ± 20.8 years) and the improvement of the degree of disability, as assessed by the Disability Rating Scale, was considered the main outcome measure. To define the best predictor, a series of demographical and clinical factors were modeled using a twofold approach: (1) logistic regression to evaluate a possible causal effect among variables; and (2) machine learning algorithms (ML), to define the best predictive model. Univariate analysis demonstrated that disability in DOC patients statistically decreased at the discharge with respect to admission. Genitourinary was the most frequent medical complication (MC) emerging during the neurorehabilitation period. The logistic model revealed that the total amount of MCs is a risk factor for lack of functional improvement. ML discloses that the most important prognostic factors are the respiratory and hepatic complications together with the presence of the upper gastrointestinal comorbidities. Our study provides new evidence on the most adverse short-term factors predicting a functional recovery in DOC patients after severe TBI. The occurrence of medical complications during neurorehabilitation stay should be considered to avoid poor outcomes.

Language: en

Keywords

machine learning; medical complications; neurorehabiliation; predictor factors; severe traumatic brain injury (sTBI)