Impact of thoracic injury on traumatic brain injury outcome

Abstract

Background: To assessed the significance of thoracic injury on the 30-day mortality and outcome of traumatic brain injury (TBI).

Methods: TBI patients admitted to our department were retrospectively evaluated. We developed two prognostic models based on admission predictors with logistic regression analysis to assess the significance of thoracic injuries in determining the 30-day mortality and outcome. The internal validity of the models was evaluated with the bootstrap re-sampling technique. We also validated the models in an external series of 165 patients that collected from our center. Discriminative ability was evaluated with C statistic. Calibrative ability was assessed with the Hosmer-Lemeshow test (H-L test).

Results: Among 505 TBI patients admitted, 102 (20.2%) had thoracic injuries. Patients with a PCS ≥ 6 had a 3.142 and 8.065 times higher odds of mortality and poor outcome compared with patients with a PCS <6, respectively. Any one-score increase of the TTS had a 1.193 times higher odds of a poor outcome (p = 0.017). The predictive model for mortality and 30-day functional outcome both had good accuracy (AUC: 0.875; 95% confidence interval [CI], 0.841-0.910 and AUC: 0.888; 95%CI, 0.860-0.916, respectively). Internal validation showed no over optimism in any of the two models' predictive C statistics (C statistic 0.872 for 30-day mortality and C statistic 0.884 for the 30-day neurological outcome). The external validation confirmed the discriminatory ability of these models (C statistic 0.949 (95%CI: 0.919-0.980) for 30-day mortality and C statistic 0.915 (95%CI: 0.868-0.963) for the 30-day neurological outcome). The calibration was also good for patients from the validation population (H-L test p>0.05).

Conclusion: Thoracic injury diagnosed by CT has a negative impact on the 30-day mortality and functional outcome of TBI patients. The extent of PC and the TTS are the predictors for TBI outcome.

Figure 1. Receiver operating characteristic (ROC) curve for prediction of mortality estimated using multiple logistic regression.

Predictive variables for this equation are age, injury severity score ≥25, pulmonary contusion score (PCS) ≥6, GCS (Glasgow Coma Scale) group, and pupillary size and light response with an AUC of 0.875, indicating good accuracy.

Figure 2. ROC curve for prediction of poor outcome estimated using multiple logistic regression.

Predictive variables for this equation are age, head AIS >3, PCS ≥6, GCS group, and TTS score with an AUC of 0.888, indicating good accuracy.

Figure 3. Validation of the prognostic models for 30-day mortality in validation patients (n = 165).

The smooth dash curves reflect the relation between observed probability of mortality and predicted probability of mortality. The triangles indicate the observed frequencies by deciles of predicted probability.

Figure 4. Validation of the prognostic models for 30-day outcome in validation patients (n = 165).

The smooth dash curves reflect the relation between observed probability of outcome and predicted probability of outcome. The triangles indicate the observed frequencies by deciles of predicted probability.