Research Paper Volume 14, Issue 2 pp 789—799

A dynamic model to predict long-term outcomes in patients with prolonged disorders of consciousness

Junwei Kang1, , Lianghua Huang1, , Yunliang Tang1, , Gengfa Chen1, , Wen Ye1, , Jun Wang1, , Zhen Feng1, ,

  • 1 Department of Rehabilitation Medicine, First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, P.R. China

Received: September 9, 2021       Accepted: November 22, 2021       Published: January 19, 2022
How to Cite

Copyright: © 2022 Kang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Purpose: It is important to predict the prognosis of patients with prolonged disorders of consciousness (DOC). This study established and validated a nomogram and corresponding web-based calculator to predict outcomes for patients with prolonged DOC.

Methods: All data were obtained from the First Affiliated Hospital of Nanchang University and the Shangrao Hospital of Traditional Chinese Medicine. Predictive variables were identified by univariate and multiple logistic regression analyses. Receiver operating characteristic curves, calibration curves, and a decision curve analysis (DCA) were utilized to assess the predictive accuracy, discriminative ability, and clinical utility of the model, respectively.

Results: Independent prognostic factors, such as age, Glasgow coma scale score, state of consciousness, and brainstem auditory-evoked potential grade were integrated into a nomogram. The model demonstrated good discrimination in the training and validation cohorts, with area-under-the-curve values of 0.815 (95% confidence interval [CI]: 0.748–0.882) and 0.805 (95% CI: 0.727–0.883), respectively. The calibration plots and DCA demonstrated good model performance and clear clinical benefits in both cohorts.

Conclusions: Based on our nomogram, we developed an effective, simple, and accurate model of a web-based calculator that may help individualize healthcare decision-making. Further research is warranted to optimize the system and update the predictors.


DOC: disorders of consciousness; MCS: minimally conscious state; VS: vegetative state; EEG: electroencephalogram; GCS: Glasgow Coma Scale; CRS-R: Coma Recovery Scale; SSEP: somatosensory evoked potentials; BAEP: brainstem auditory evoked potential; GOS: Glasgow Outcome Scale; CI: confidence interval; AUC: area-under-the-curve; DCA: decision curve analysis; ROC: receiver operating characteristic; TBI: traumatic brain injury; APG: anterior-posterior gradient; MiA: mildly abnormal; MoA: moderately abnormal; DS: diffuse slowing (DS); LV: low voltage.