• Mingsheng Chen, Zhihong Li, Zhifeng Yan, Shunnan Ge, Yongbing Zhang, Haigui Yang, Lanfu Zhao, Lingyu Liu, Xingye Zhang, Yaning Cai, and Yan Qu.
• Department of Neurosurgery, Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi, China.
• J. Neurotrauma. 2022 Mar 1; 39 (5-6): 371-378.

AbstractModerate traumatic brain injury (mTBI) is a heterogeneous entity that is poorly defined in the literature. Patients with mTBI have a high rate of neurological deterioration (ND), which is usually accompanied by poor prognosis and no definitive methods to predict. The purpose of this study is to develop and validate a prediction model that estimates the ND risk in patients with mTBI using data collected on admission. Data for 479 patients with mTBI collected retrospectively in our department were analyzed by logistic regression models. Bivariable logistic regression identified variables with a p < 0.05. Multi-variable logistic regression modeling with backward stepwise elimination was used to determine reduced parameters and establish a prediction model. The discrimination efficacy, calibration efficacy, and clinical utility of the prediction model were evaluated. The prediction model was validated using data for 176 patients collected from another hospital. Eight independent prognostic factors were identified: hypertension, Marshall scale (types III and IV), subdural hemorrhage (SDH), location of contusion (frontal and temporal contusions), Injury Severity Score >13, D-dimer level >11.4 mg/L, Glasgow Coma Scale score ≤10, and platelet count ≤152 × 109/L. A prediction model was established and was shown as a nomogram. Using bootstrapping, internal validation showed that the C-statistic of the prediction model was 0.881 (95% confidence interval [CI]: 0.849-0.909). The results of external validation showed that the nomogram could predict ND with an area under the curve of 0.827 (95% CI: 0.763-0.880). The present model, based on simple parameters collected on admission, can predict the risk of ND in patients with mTBI accurately. The high discriminative ability indicates the potential of this model for classifying patients with mTBI according to ND risk.

13 keywords...

hide…