Development and Validation of a Simplified Prehospital Triage Model Using Neural Network to Predict Mortality in Trauma Patients: The Ability to Follow Commands, Age, Pulse Rate, Systolic Blood Pressure and Peripheral Oxygen Saturation (CAPSO) Model

Yun Li; Lu Wang; Yuyan Liu; Yan Zhao; Yong Fan; Mengmeng Yang; Rui Yuan; Feihu Zhou; Zhengbo Zhang; Hongjun Kang

doi:10.3389/fmed.2021.810195

Development and Validation of a Simplified Prehospital Triage Model Using Neural Network to Predict Mortality in Trauma Patients: The Ability to Follow Commands, Age, Pulse Rate, Systolic Blood Pressure and Peripheral Oxygen Saturation (CAPSO) Model

Front Med (Lausanne). 2021 Dec 10:8:810195. doi: 10.3389/fmed.2021.810195. eCollection 2021.

Authors

Yun Li^{1

2}, Lu Wang^{1

2}, Yuyan Liu^{1

2}, Yan Zhao², Yong Fan³, Mengmeng Yang², Rui Yuan², Feihu Zhou², Zhengbo Zhang³, Hongjun Kang²

Affiliations

¹ Medical School of Chinese PLA, Beijing, China.
² Department of Critical Care Medicine, The First Medical Centre, Chinese PLA General Hospital, Beijing, China.
³ Center for Artificial Intelligence in Medicine, Chinese PLA General Hospital, Beijing, China.

Abstract

Objective: Most trauma scoring systems with high accuracy are difficult to use quickly in field triage, especially in the case of mass casualty events. We aimed to develop a machine learning model for trauma mortality prediction using variables easy to obtain in the prehospital setting. Methods: This was a retrospective prognostic study using the National Trauma Data Bank (NTDB). Data from 2013 to 2016 were used for model training and internal testing, and data from 2017 were used for validation. A neural network model (NN-CAPSO) was developed using the ability to follow commands (whether GCS-motor was <6), age, pulse rate, systolic blood pressure (SBP) and peripheral oxygen saturation, and a new score (the CAPSO score) was developed based on logistic regression. To achieve further simplification, a neural network model with the SBP variable removed (NN-CAPO) was also developed. The discrimination ability of different models and scores was compared based on the area under the receiver operating characteristic curve (AUROC). Furthermore, a reclassification table with three defined risk groups was used to compare NN-CAPSO and other models or scores. Results: The NN-CAPSO had an AUROC of 0.911(95% confidence interval 0.909 to 0.913) in the validation set, which was higher than the other trauma scores available for prehospital settings (all p < 0.001). The NN-CAPO and CAPSO score both reached the AUROC of 0.904 (95% confidence interval 0.902 to 0.906), and were no worse than other prehospital trauma scores. Compared with the NN-CAPO, CAPSO score, and the other trauma scores in reclassification tables, NN-CAPSO was found to more accurately classify patients to the right risk groups. Conclusions: The newly developed CAPSO system simplifies the method of consciousness assessment and has the potential to accurately predict trauma patient mortality in the prehospital setting.

Keywords: in-hospital mortality; machine learning; prehospital; scoring system; trauma; triage.