Evidence map›Paper›PMID 39849353›Full record

Observational studyBMC emergency medicine2025

Construction and evaluation of a triage assessment model for patients with acute non-traumatic chest pain: mixed retrospective and prospective observational study.

Xuan Zhou, Gangren Jian, Yuefang He, Yating Huang, Jie Zhang, Shengfang Wang, Yunxian Wang, Ruofei Zheng

Abstract readObservational Study
In one paragraph

Observational study in BMC emergency medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Xuan ZhouThe School of Nursing, Fujian Medical University, Fuzhou, Fujian, China.
Gangren JianShengli Clinical Medical College of Fujian Medical University, Department of Emergency, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Key Laboratory of Emergency Medicine, Fuzhou, Fujian, China.
Yuefang HeThe School of Nursing, Fujian Medical University, Fuzhou, Fujian, China.
Yating HuangThe School of Nursing, Fujian Medical University, Fuzhou, Fujian, China.
Jie ZhangThe School of Nursing, Fujian Medical University, Fuzhou, Fujian, China.
Shengfang WangShengli Clinical Medical College of Fujian Medical University, Department of Emergency, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Key Laboratory of Emergency Medicine, Fuzhou, Fujian, China.
Yunxian WangDepartment Institute of Nursing, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China.
Ruofei ZhengShengli Clinical Medical College of Fujian Medical University, Department of Emergency, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Key Laboratory of Emergency Medicine, Fuzhou, Fujian, China. lpz505@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAcute non-traumatic chest pain is one of the common complaints in the emergency department and is closely associated with fatal disease. Triage assessment urgently requires the use of simple, rapid tools to screen patients with chest pain for high-risk condition to improve patient outcomes.

methodsAfter data preprocessing and feature selection, univariate and multiple logistic regression analyses were performed to identify potential predictors associated with acute non-traumatic chest pain. A nomogram was built based on the predictors, and an internal evaluation was performed using bootstrap resampling methods. The model was also externally validated in this center. Furthermore, the model results were risk-stratified using the decision tree analysis to explore the corresponding triage level. Subsequently, we developed an online visualization tool based on the model to assess the risk of high risk in patients with chest pain.

resultsMultiple logistic regression analysis showed that age, smoking, coronary heart disease, hypertension, diabetes, hyperlipidemia, pain site, concomitant symptoms, and electrocardiograph, all of which are independent predictors of high-risk chest pain patients. The AUC of our model in the development and validation groups was 0.919 (95%CI: 0.891 ~ 0.974) and 0.904 (95%CI: 0.855 ~ 0.952). Moreover, our model demonstrated better outcomes in terms of accuracy/sensitivity in both cohorts (81.9%/85.2% and 94.8%/78.5%). The calibration curve shows a high degree of agreement between the predicted and actual probabilities. Decision curve analysis clarified that our model had higher net gains across the entire range of clinical thresholds. Afterward, we developed an online tool, which is used in the triage link to facilitate nurses to screen people with high-risk chest pain.

conclusionWe proposed an accurate model to predict the high-risk populations with chest pain, based on which a simple and rapid online tool was developed and provided substantial support for its application as a decision-making tool for the emergency department. REGISTRATION: The study protocol was approved by the Ethics Committee Board of Fujian Provincial Hospital. CLINICAL TRIAL REGISTRATION NUMBER: ChiCTR2200061918.

Indexed as

Chest PainTriageAdultAgedDecision TreesEmergency Service, HospitalFemaleHumansLogistic ModelsMaleMiddle AgedNomogramsProspective StudiesRetrospective StudiesRisk AssessmentChest painRisk prediction modelRisk stratificationTriage

Identifiers

PMID39849353
PMCPMC11756052

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.