Evidence map›Paper›PMID 35196652›Full record

Observational studyCardiology2022

Development and Internal Validation of a Practical Model to Identify Observe Patients of the European Society of Cardiology 0/1-h Algorithm at Low Risk of a Coronary Diagnosis.

Murat Arslan, Eric Boersma, Admir Dedic, Eric A Dubois

Abstract readObservational Study
In one paragraph

Observational study in Cardiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Murat ArslanDepartment of Cardiology, Erasmus Medical Centre, Rotterdam, The Netherlands.
Eric BoersmaDepartment of Cardiology, Erasmus Medical Centre, Rotterdam, The Netherlands.
Admir DedicDepartment of Cardiology, Erasmus Medical Centre, Rotterdam, The Netherlands.
Eric A DuboisDepartment of Cardiology, Erasmus Medical Centre, Rotterdam, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatients with suspected non-ST-elevation acute coronary syndrome (NSTE-ACS) assigned to the "observe" zone of the European Society of Cardiology (ESC) 0/1-h algorithm form a heterogeneous group known to have an unfavourable prognosis. We aim to elucidate the clinical characteristics and management of these patients and generate a model that is predictive of a coronary diagnosis at index visit to the emergency department (ED).

methodsA retrospective observational cohort study, including adult patients presenting to the ED with suspected NSTE-ACS assigned to the "observe" zone of the ESC 0/1-h algorithm. Multivariable logistic regression analysis was performed for the prediction of a coronary diagnosis. Internal validation was performed using bootstrap resampling.

resultsA total of 750 patients were included; mean age 66 ± 13 years, 35% women, 50% with prior history of coronary artery disease (CAD). In 372 (50%) patients a diagnosis was established within 30 days of index presentation, of whom 169 (45%) patients had a coronary-related event. Multivariable logistic regression analysis generated a 12-point risk score incorporating 5 variables for the prediction of such event, including type of angina, chest pain occurring during inspiration, prior history of CAD, ST-segment deviation on electrocardiogram, and estimated glomerular filtration rate <60. The final model had an optimism-corrected c-statistic of 0.78 (95% confidence interval [CI]: 0.74-0.82). A score <6 ruled out a coronary event in 276 (37%) patients, with a sensitivity and negative predictive value of 90% (95% CI: 84-94) and 94% (91-96), respectively.

conclusionA score <6 identifies patients at low risk of a coronary diagnosis and can guide clinical decision-making in choosing the appropriate diagnostic test.

Indexed as

Acute Coronary SyndromeCardiologyCoronary Artery DiseaseAdultAgedAlgorithmsFemaleHumansMaleMiddle AgedRetrospective StudiesRisk AssessmentAcute chest painNon-ST-elevation acute coronary syndromeObserve groupRisk score

Identifiers

PMID35196652
PMCPMC9393837

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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.