Evidence map›Paper›PMID 35514876›Full record

ArticleInternational journal of cardiology. Heart & vasculature2022

Development and validation of a comprehensive early risk prediction model for patients with undifferentiated acute chest pain.

Luke P Dawson, Emily Andrew, Ziad Nehme, Jason Bloom, Danny Liew, Shelley Cox, David Anderson, Michael Stephenson, Jeffrey Lefkovits, Andrew J Taylor and 4 more

Open access · goldAbstract read
In one paragraph

Article in International journal of cardiology. Heart & vasculature, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.9field-weighted citation impact, top 29% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Observational
  2. Article
  3. 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

14 authors at 6 institutions in 1 country.

Luke P DawsonDepartment of Cardiology, The Alfred Hospital, Melbourne, Victoria, Australia.
Emily AndrewDepartment of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Ziad NehmeDepartment of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Jason BloomDepartment of Cardiology, The Alfred Hospital, Melbourne, Victoria, Australia.
Danny LiewDepartment of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Shelley CoxDepartment of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
David AndersonAmbulance Victoria, Melbourne, Victoria, Australia.
Michael StephensonDepartment of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Jeffrey LefkovitsDepartment of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Andrew J TaylorDepartment of Cardiology, The Alfred Hospital, Melbourne, Victoria, Australia.
David KayeDepartment of Cardiology, The Alfred Hospital, Melbourne, Victoria, Australia.
Louise CullenEmergency and Trauma Centre, Royal Brisbane and Women's Hospital, Brisbane, Australia.
Karen SmithDepartment of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Dion StubDepartment of Cardiology, The Alfred Hospital, Melbourne, Victoria, Australia.
Ambulance Victoria · AUBaker Heart and Diabetes Institute · AUThe Alfred Hospital · AUThe Royal Melbourne Hospital · AUMonash University · AURoyal Brisbane and Women's Hospital · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: Existing risk scores for undifferentiated chest pain focus on excluding coronary events and do not represent a comprehensive risk assessment if an alternate serious diagnosis is present. This study aimed to develop and validate an all-inclusive risk prediction model among patients with undifferentiated chest pain. Methods: We developed and validated a multivariable logistic regression model for a composite measure of early all-inclusive risk (defined as hospital admission excluding a discharge diagnosis of non-specific pain, 30-day all-cause mortality, or 30-day myocardial infarction [MI]) among adults assessed by emergency medical services (EMS) for non-traumatic chest pain using a large population-based cohort (January 2015 to June 2019). The cohort was randomly divided into development (146,507 patients [70%]) and validation (62,788 patients [30%]) cohorts. Results: The composite outcome occurred in 28.4%, comprising hospital admission in 27.7%, mortality within 30-days in 1.8%, and MI within 30-days in 0.4%. The Early Chest pain Admission, MI, and Mortality (ECAMM) risk model was developed, demonstrating good discrimination in the development (C-statistic 0.775, 95% CI 0.772-0.777) and validation cohorts (C-statistic 0.765, 95% CI 0.761-0.769) with excellent calibration. Discriminatory performance for the composite outcome and individual components was higher than existing scores commonly used in undifferentiated chest pain risk stratification. Conclusions: The ECAMM risk score model can be used as an all-inclusive risk stratification assessment of patients with non-traumatic chest pain without the limitation of a single diagnostic outcome. This model could be clinically useful to help guide decisions surrounding the need for non-coronary investigations and safety of early discharge.

Indexed as

Chest painCI, confidence intervalEmergency medical servicesMI, myocardial infarctionPrediction modelPre-hospitalRisk scoreVAED, Victorian Admitted Episodes DatasetVDI, Victorian Death IndexVEMD, Victorian Emergency Minimum Dataset

Identifiers

PMID35514876
PMCPMC9062672
OpenAlexW4225012881

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.