Evidence map›Paper›PMID 37283981›Full record

ArticleThe Lancet regional health. Western Pacific2023

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 and 3 more

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in The Lancet regional health. Western Pacific, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.4field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Use of artificial intelligence to support prehospital traumatic injury care: A scoping review.Journal of the American College of Emergency Physicians open · 2024
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors at 7 institutions in 4 countries.

Jae Yong YuDepartment of Digital Health, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea.
Sejin HeoDepartment of Digital Health, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea.
Feng XieProgramme in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore.
Nan LiuProgramme in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore.
Sun Yung YoonDepartment of Digital Health, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea.
Han Sol ChangDepartment of Digital Health, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea.
Taerim KimDepartment of Digital Health, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea.
Se Uk LeeDepartment of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
Marcus Eng Hock OngProgramme in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore.
Yih Yng NgDigital & Smart Health Office, Tan Tock Seng Hospital, Singapore.
Sang Do ShinDepartment of Emergency Medicine, Seoul National University College of Medicine, Seoul, South Korea.
Kentaro KajinoDepartment of Emergency and Critical Care Medicine, Kansai Medical University, Moriguchi, Japan.
Won Chul ChaDepartment of Digital Health, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea.
Samsung (South Korea) · KRNational University of Singapore · SGKansai Medical University · JPSamsung Medical Center · KRSeoul National University · KRSingHealth · SGTan Tock Seng Hospital · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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).

Indexed as

Emergency medical servicesInjury severity scoreMachine learningMortalityTriage

Identifiers

PMID37283981
PMCPMC10240358
OpenAlexW4323074472

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.