Predictors of Morbidity and Mortality After Fall-related Traumatic Brain Injury.

Abstract

Objective: The authors investigate predictors of morbidity and mortality in patients after fall-related Traumatic Brain Injury (TBI) in a retrospective cohort study of patients presenting to a single emergency department.

Methods: This study analyzed the predictors of a subset of patients who come to the emergency department (ED) of a Level 1 trauma center who sustained a TBI after a fall. The study also examines the utility of head Computed Tomography (CT) scan as a predictor in determining outcomes such as hospital admission, in-hospital death, and Intensive Care Unit (ICU) admission. Demographic variables such as age, sex, race, and marital status, as well as symptoms associated with the TBI injury such as seizures, vomiting, loss of consciousness (LOC), post-trauma amnesia (PTA), alteration of consciousness (AOC), were all variables included in the multivariate model. Statistical analysis was conducted in JMP Pro 17 for the Macintosh.

Results: The cohort was composed of 1439 patients, of which 833 (57%) patients were male. The median Glasgow Coma Scale (GCS) score for the cohort was 15, and 87% of the patients experienced mild TBI. Statistically significant predictors of in-hospital death, both in the presence and absence of abnormal head CT in the multivariate model, were age in years, loss of consciousness, and diastolic blood pressure. Predictors that were statistically significant for hospital admission both in the presence and absence of abnormal head CT in the multivariate model were loss of consciousness, age in years, and patient diastolic blood pressure. Finally, predictors for ICU admission that were significant in the presence and absence of abnormal head CT in the multivariate model were GCS score and loss of consciousness.

Conclusion: The presence of an abnormal head CT increased the R2 value in all 3 of the outcomes of in-hospital death, hospital admission, and ICU admission. This suggests that a head CT of the patient plays an important role in predicting various health outcomes, emphasizing the importance of early interventions.