A prediction model of clinical outcomes in prolonged disorders of consciousness: A prospective cohort study - Xiong, Qi; Le, Kai; Wang, Yong; Tang, Yunliang; Dong, Xiaoyang; Zhong, Yuan; Zhou, Yao; Feng, Zhen (Frontiers in Neuroscience, 2023)
Objective:To establish and validate a prediction model on clinical outcome in patients with prolonged disorder of consciousness (pDOC). Methods:A total of 170 patients with pDOC enrolled in our rehabilitation unit were included and divided into a training set(n=119) and validation set(n=51).Independent predictors on the improved clinical outcome were identified by univariate and multivariate logistic regression analysis,and a nomogram model was established.The nomogram performance was quantified using receiver operating curve(ROC) and calibration curves in the training set and validated set.A decision curve analysis (DCA) was performed to evaluate the clinical usefulness of this nomogram model. Results:Univariate and multivariate logistic regression analysis indicated that age,diagnosis at entry,serum albumin(g/L),and pupillary reflex were the independent prognostic factors,which were used to construct the nomogram.The area under the curve in the training and validation set were 0.845 and 0.801,respectively.This nomogram model showed a good calibration with a good consistency between actual and predicted probabilities of improved outcome.DCA demonstrated a higher net benefit in clinical decision-making compared to treat all or none. Conclusions:Several feasible, cost effective prognostic variables which are widely available in the hospitals can provide an,efficient and accurate prediction model on improved clinical outcome and support clinicians to offer suitable clinical care and decision-making to patients with pDOC and their family members.