BACKGROUND: We sought to develop a novel Prehospital Injury Mortality Score (PIMS) to predict blunt trauma mortality using only prehospital variables. STUDY DESIGN: The 2017 Trauma Quality Improvement Program database was queried and divided into two equal sized sets at random (derivation and validation sets). Multiple logistic regression models were created to determine the risk of mortality using age, sex, mechanism, and trauma activation criterion. The PIMS was derived using the weighted average of each independent predictor. The discriminative power of the scoring tool was assessed by calculating the area under the receiver operating characteristics (AUROC) curve. The PIMS ability to predict mortality was then assessed by using the validation cohort. The score was compared to the Revised Trauma Score (RTS) using the AUROC curve, including a subgroup of patients with normal vital signs. RESULTS: The derivation and validation groups each consisted of 163 694 patients. Seven independent predictors of mortality were identified, and the PIMS was derived with scores ranging from 0 to 20. The mortality rate increased from 1.4% to 43.9% and then 100% at scores of 1, 10, and 19, respectively. The model had very good discrimination with an AUROC of .79 in both the derivation and validation groups. When compared to the RTS, the AUROC were similar (.79 vs. .78). On subgroup analysis of patients with normal prehospital vital signs, the PIMS was superior to the RTS (.73 vs. .56). CONCLUSION: The PIMS is a novel scoring tool to predict mortality in blunt trauma patients using prehospital variables. It had improved discriminatory power in blunt trauma patients with normal vital signs compared to the RTS.
BACKGROUND: We sought to develop a novel Prehospital Injury Mortality Score (PIMS) to predict blunt trauma mortality using only prehospital variables. STUDY DESIGN: The 2017 Trauma Quality Improvement Program database was queried and divided into two equal sized sets at random (derivation and validation sets). Multiple logistic regression models were created to determine the risk of mortality using age, sex, mechanism, and trauma activation criterion. The PIMS was derived using the weighted average of each independent predictor. The discriminative power of the scoring tool was assessed by calculating the area under the receiver operating characteristics (AUROC) curve. The PIMS ability to predict mortality was then assessed by using the validation cohort. The score was compared to the Revised Trauma Score (RTS) using the AUROC curve, including a subgroup of patients with normal vital signs. RESULTS: The derivation and validation groups each consisted of 163 694 patients. Seven independent predictors of mortality were identified, and the PIMS was derived with scores ranging from 0 to 20. The mortality rate increased from 1.4% to 43.9% and then 100% at scores of 1, 10, and 19, respectively. The model had very good discrimination with an AUROC of .79 in both the derivation and validation groups. When compared to the RTS, the AUROC were similar (.79 vs. .78). On subgroup analysis of patients with normal prehospital vital signs, the PIMS was superior to the RTS (.73 vs. .56). CONCLUSION: The PIMS is a novel scoring tool to predict mortality in blunt trauma patients using prehospital variables. It had improved discriminatory power in blunt trauma patients with normal vital signs compared to the RTS.