TY - JOUR
T1 - Need for Emergent Intervention within 6 Hours
T2 - A Novel Prediction Model for Hospital Trauma Triage
AU - Morris, Rachel
AU - Karam, Basil S.
AU - Zolfaghari, Emily J.
AU - Chen, Benjamin
AU - Kirsh, Thomas
AU - Tourani, Roshan
AU - Milia, David J.
AU - Napolitano, Lena
AU - de Moya, Marc
AU - Conterato, Marc
AU - Aliferis, Constantin
AU - Ma, Sisi
AU - Tignanelli, Christopher
N1 - Publisher Copyright:
© 2021 National Association of EMS Physicians.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - resource allocation
KW - trauma
KW - triage
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UR - http://www.scopus.com/inward/citedby.url?scp=85113138038&partnerID=8YFLogxK
U2 - 10.1080/10903127.2021.1958961
DO - 10.1080/10903127.2021.1958961
M3 - Article
C2 - 34313534
AN - SCOPUS:85113138038
SN - 1090-3127
VL - 26
SP - 556
EP - 565
JO - Prehospital Emergency Care
JF - Prehospital Emergency Care
IS - 4
ER -