LAUSR

Moderate traumatic brain injury (mTBI) is a heterogeneous entity that poorly defined in the literature. mTBI patients suffer from a high rate of neurological deterioration (ND), which is usually accompanied with 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 mTBI patients using data collected on admission. Retrospectively collected 479 mTBI patients' data in our department were analyzed by logistic regression models. Bivariable logistic regression identified variables with a p-value<0.05. Multivariable 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 176 patients' data collected from another hospital. Eight independent prognostic factors were identified: hypertension, Marshall's scale (types III and IV), subdural hemorrhage (SDH), location of contusion (LOC) (frontal and temporal contusions), Injury Severity Score (ISS) >13, D-dimer level >11.4 mg/L, Glasgow Coma Scale (GCS) score ≤10, and platelet (PLT) 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 (AUC) of 0.827 (95% CI: 0763.-0.880). The present model, based on simple parameters collected on admission, can predict the risk of ND in mTBI patients accurately. The high discriminative ability indicates the potential of this model for classifying mTBI patients according to ND risk.