Lucca Lucia Francesca, Lofaro Danilo, Leto Elio, Ursino Maria, Rogano Stefania, Pileggi Antonio, Vulcano Serafino, Conforti Domenico, Tonin Paolo, Cerasa Antonio
S. Anna Institute, Crotone, Italy.
Eng, deHealth Lab-DIMEG, UNICAL, Arcavata di Rende, Italy.
Front Hum Neurosci. 2020 Oct 21;14:570544. doi: 10.3389/fnhum.2020.570544. eCollection 2020.
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.
在本研究中,我们试图评估重度创伤性脑损伤(TBI)后处于意识障碍(DOC)状态的患者在神经康复住院期间预后的预测因素。总共纳入了96例DOC患者(植物状态、微意识状态或从微意识状态苏醒)(69例男性;平均年龄43.6±20.8岁),以残疾评定量表评估的残疾程度改善作为主要结局指标。为了确定最佳预测因素,采用了双重方法对一系列人口统计学和临床因素进行建模:(1)逻辑回归以评估变量之间可能的因果效应;(2)机器学习算法(ML),以定义最佳预测模型。单因素分析表明,DOC患者出院时的残疾程度相对于入院时在统计学上有所下降。泌尿生殖系统是神经康复期间出现的最常见医疗并发症(MC)。逻辑模型显示,MC的总数是功能改善缺乏的一个危险因素。ML表明,最重要的预后因素是呼吸和肝脏并发症以及上消化道合并症的存在。我们的研究为预测重度TBI后DOC患者功能恢复的最不利短期因素提供了新证据。应考虑神经康复住院期间医疗并发症的发生情况以避免不良结局。