Need for Emergent Intervention within 6 Hours: A Novel Prediction Model for Hospital Trauma Triage. (21st June 2022)

• Record Type: Journal Article
• Authors: Morris, Rachel; Karam, Basil S.; Zolfaghari, Emily J.; Chen, Benjamin; Kirsh, Thomas; Tourani, Roshan; Milia, David J.; Napolitano, Lena; de Moya, Marc; Conterato, Marc; Aliferis, Constantin; Ma, Sisi; Tignanelli, Christopher
• Is Part Of: Prehospital emergency care. Volume 26:Number 4(2022)
• Page Start: 556
• Page End: 565
• Publication Date: 2022-06-21
• Subjects: trauma -- resource allocation -- triage; 362.18
• DOI: 10.1080/10903127.2021.1958961
• Languages: English
• ISSNs: 1090-3127

Abstract

Abstract: Objective: A tiered trauma team activation system allocates resources proportional to patients' needs based upon injury burden. Previous trauma hospital-triage models are limited to predicting Injury Severity Score which is based on > 10% all-cause in-hospital mortality, rather than need for emergent intervention within 6 hours (NEI-6). Our aim was to develop a novel prediction model for hospital-triage that utilizes criteria available to the EMS provider to predict NEI-6 and the need for a trauma team activation. Methods: A regional trauma quality collaborative was used to identify all trauma patients ≥ 16 years from the American College of Surgeons-Committee on Trauma verified Level 1 and 2 trauma centers. Logistic regression and random forest were used to construct two predictive models for NEI-6 based on clinically relevant variables. Restricted cubic splines were used to model nonlinear predictors. The accuracy of the prediction model was assessed in terms of discrimination. Results: Using data from 12, 624 patients for the training dataset (62.6% male; median age 61 years; median ISS 9) and 9, 445 patients for the validation dataset (62.6% male; median age 59 years; median ISS 9), the following significant predictors were selected for the prediction models: age, gender, field GCS, vital signs, intentionality, and mechanism of injury. The final boosted tree model showed an AUC of 0.85 in the validation cohort for predicting NEI-6. Conclusions: The NEI-6 trauma triage prediction model used prehospital metrics to predict need for highest level of trauma activation. Prehospital prediction of major trauma may reduce undertriage mortality and improve resource utilization.