Development and Asian-wide validation of the Grade for Interpretable Field Triage (GIFT) for predicting mortality in pre-hospital patients using the Pan-Asian Trauma Outcomes Study (PATOS)

Jae Yong Yu; Sejin Heo; Feng Xie; Nan Liu; Sun Yung Yoon; Han Sol Chang; Taerim Kim; Se Uk Lee; Marcus Eng Hock Ong; Yih Yng Ng; Sang Do Shin; Kentaro Kajino; Won Chul Cha

doi:10.1016/j.lanwpc.2023.100733

Development and Asian-wide validation of the Grade for Interpretable Field Triage (GIFT) for predicting mortality in pre-hospital patients using the Pan-Asian Trauma Outcomes Study (PATOS)

Lancet Reg Health West Pac. 2023 Mar 4:34:100733. doi: 10.1016/j.lanwpc.2023.100733. eCollection 2023 May.

Authors

Jae Yong Yu^{1

2}, Sejin Heo^{1

3}, Feng Xie^{4

5

6}, Nan Liu^{4

7

8}, Sun Yung Yoon¹, Han Sol Chang^{1

3}, Taerim Kim^{1

3}, Se Uk Lee³, Marcus Eng Hock Ong^{4

9}, Yih Yng Ng², Sang Do Shin¹⁰, Kentaro Kajino¹¹, Won Chul Cha^{1

3

12}

Affiliations

¹ Department of Digital Health, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea.
² Digital & Smart Health Office, Tan Tock Seng Hospital, Singapore.
³ Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
⁴ Programme in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore.
⁵ Department of Biomedical Data Science, Stanford University, Stanford, USA.
⁶ Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, USA.
⁷ Health Service Research Centre, Singapore Health Services, Singapore.
⁸ Institute of Data Science, National University of Singapore, Singapore.
⁹ Department of Emergency Medicine, Singapore General Hospital, Singapore.
¹⁰ Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, South Korea.
¹¹ Department of Emergency and Critical Care Medicine, Kansai Medical University, Moriguchi, Japan.
¹² Digital Innovation Center, Samsung Medical Center, Seoul, South Korea.

Abstract

Background: Field triage is critical in injury patients as the appropriate transport of patients to trauma centers is directly associated with clinical outcomes. Several prehospital triage scores have been developed in Western and European cohorts; however, their validity and applicability in Asia remains unclear. Therefore, we aimed to develop and validate an interpretable field triage scoring systems based on a multinational trauma registry in Asia.

Methods: This retrospective and multinational cohort study included all adult transferred injury patients from Korea, Malaysia, Vietnam, and Taiwan between 2016 and 2018. The outcome of interest was a death in the emergency department (ED) after the patients' ED visit. Using these results, we developed the interpretable field triage score with the Korea registry using an interpretable machine learning framework and validated the score externally. The performance of each country's score was assessed using the area under the receiver operating characteristic curve (AUROC). Furthermore, a website for real-world application was developed using R Shiny.

Findings: The study population included 26,294, 9404, 673 and 826 transferred injury patients between 2016 and 2018 from Korea, Malaysia, Vietnam, and Taiwan, respectively. The corresponding rates of a death in the ED were 0.30%, 0.60%, 4.0%, and 4.6% respectively. Age and vital sign were found to be the significant variables for predicting mortality. External validation showed the accuracy of the model with an AUROC of 0.756-0.850.

Interpretation: The Grade for Interpretable Field Triage (GIFT) score is an interpretable and practical tool to predict mortality in field triage for trauma.

Funding: This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (Grant Number: HI19C1328).

Keywords: Emergency medical services; Injury severity score; Machine learning; Mortality; Triage.