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).

Emergency medical services Injury severity score Machine learning Mortality Triage

Journal

The Lancet regional health. Western Pacific
ISSN: 2666-6065
Titre abrégé: Lancet Reg Health West Pac
Pays: England
ID NLM: 101774968

Informations de publication

Date de publication:
May 2023
Historique:
received: 28 11 2022
revised: 24 01 2023
accepted: 19 02 2023
medline: 7 6 2023
pubmed: 7 6 2023
entrez: 7 6 2023
Statut: epublish

Résumé

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

Sections du résumé

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

Identifiants

pubmed: 37283981
doi: 10.1016/j.lanwpc.2023.100733
pii: S2666-6065(23)00051-2
pmc: PMC10240358
doi:

Types de publication

Journal Article

Langues

eng

Pagination

100733

Informations de copyright

© 2023 The Authors.

Déclaration de conflit d'intérêts

This research was supported by a grant of the Korea Health Technology R&D Project through the 10.13039/501100003710Korea Health Industry Development Institute (KHIDI), funded by the 10.13039/501100003625Ministry of Health & Welfare, Republic of Korea (Grant Number: HI19C1328). This funding was granted to Jae Yong Yu.

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Auteurs

Jae Yong Yu (JY)

Department of Digital Health, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea.
Digital & Smart Health Office, Tan Tock Seng Hospital, Singapore.

Sejin Heo (S)

Department of Digital Health, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea.
Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.

Feng Xie (F)

Programme in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore.
Department of Biomedical Data Science, Stanford University, Stanford, USA.
Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, USA.

Nan Liu (N)

Programme in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore.
Health Service Research Centre, Singapore Health Services, Singapore.
Institute of Data Science, National University of Singapore, Singapore.

Sun Yung Yoon (SY)

Department of Digital Health, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea.

Han Sol Chang (HS)

Department of Digital Health, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea.
Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.

Taerim Kim (T)

Department of Digital Health, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea.
Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.

Se Uk Lee (SU)

Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.

Marcus Eng Hock Ong (ME)

Programme in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore.
Department of Emergency Medicine, Singapore General Hospital, Singapore.

Yih Yng Ng (YY)

Digital & Smart Health Office, Tan Tock Seng Hospital, Singapore.

Sang Do Shin (S)

Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, South Korea.

Kentaro Kajino (K)

Department of Emergency and Critical Care Medicine, Kansai Medical University, Moriguchi, Japan.

Won Chul Cha (WC)

Department of Digital Health, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea.
Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
Digital Innovation Center, Samsung Medical Center, Seoul, South Korea.

Classifications MeSH