Major trauma registry of Navarre (Spain): the accuracy of different survival prediction models.

Objective: To determine which factors predict death among trauma patients who are alive on arrival at hospital.

Methods: Design prospective cohort study method. Data were collected on 378 trauma patients who were initially delivered by the emergency medical services of Navarre (Spain) with multiple injuries with a new injury severity score of 15 or more in 2011-2012. These data related to age, gender, presence of premorbid conditions, abbreviated injury score, injury severity score, new injury severity score (NISS), revised trauma score (RTS), and prehospital and hospital response times. Bivariate analysis was used to show the association between each variable and time until death. Mortality prediction was modeled using logistic regression analysis.

Results: The variables related to the end result were the age of the patient, associated comorbidity, NISS, and hospital RTS. Two models were formulated: in one, the variables used were quantitative, while in the other model these variables were converted into dichotomous qualitative variables. The predictive capability of the two models was compared with the trauma and injury severity score using the area under the curve. The predictive capacities of the three models had areas under the curve of 0.93, 0.88, and 0.87. The response times of the Navarre emergency services system, measured as the sum of the time taken to reach the hospital (median time of 65 min), formulate computed tomography (46 min), and perform crucial surgery (115 min), when required, were not taken into account.

Conclusion: Age, premorbid conditions, hospital RTS, and NISS are significant predictors of death after trauma. The time intervals between the accident and arrival at the hospital, arrival at the hospital and the first computed tomography scan or the first crucial emergency intervention, do not appear to affect the risk of death.