Evidence map›Paper›PMID 39287492›Full record

SynthesisThe British journal of surgery2024

Risk factor-targeted abdominal aortic aneurysm screening: systematic review of risk prediction for abdominal aortic aneurysm.

Liam Musto, Aiden Smith, Coral Pepper, Sylwia Bujkiewicz, Matthew Bown

Abstract readSystematic Review
In one paragraph

Synthesis in The British journal of surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Prediction model for aortic dissection, aortic aneurysm, and peripheral artery disease.Hypertension research : official journal of the Japanese Society of Hypertension · 2026
    Article
  2. Abdominal aortic aneurysm progression: A review of preclinical and clinical data.Clinical research in cardiology : official journal of the German Cardiac Society · 2026
    Review
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  6. Review
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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

5 authors.

Liam MustoDepartment of Cardiovascular Sciences, University of Leicester, NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK.ORCID 0000-0002-7559-0063
Aiden SmithDepartment of Population Health Sciences, Biostatistics Research Group, University of Leicester, University Road, Leicester, UK.
Coral PepperLibrary and Information Services, University Hospitals of Leicester NHS Trust, Leicester Royal Infirmary, Leicester, UK.ORCID 0000-0003-1149-8150
Sylwia BujkiewiczDepartment of Population Health Sciences, Biostatistics Research Group, University of Leicester, University Road, Leicester, UK.
Matthew BownDepartment of Cardiovascular Sciences, University of Leicester, NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK.ORCID 0000-0002-6180-3611

Funding

British Heart FoundationClinical Research Fellowship fromLeicester Biomedical Research CentreNational Institute for Health and Care Research NIHR130075
6 · The paper itself

Abstract

backgroundThis systematic review aimed to investigate the current state of risk prediction for abdominal aortic aneurysm in the literature, identifying and comparing published models and describing their performance and applicability to a population-based targeted screening strategy.

methodsElectronic databases MEDLINE (via Ovid), Embase (via Ovid), MedRxiv, Web of Science, and the Cochrane Library were searched for papers reporting or validating risk prediction models for abdominal aortic aneurysm. Studies were included only if they were developed on a cohort or study group derived from the general population and used multiple variables with at least one modifiable risk factor. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool. A synthesis and comparison of the identified models was undertaken.

resultsThe search identified 4813 articles. After full-text review, 37 prediction models were identified, of which 4 were unique predictive models that were reported in full. Applicability was poor when considering targeted screening strategies using electronic health record-based populations. Common risk factors used for the predictive models were explored across all 37 models; the most common risk factors in predictive models for abdominal aortic aneurysm were: age, sex, biometrics (such as height, weight, or BMI), smoking, hypertension, hypercholesterolaemia, and history of heart disease. Few models had undergone standardized model development, adequate external validation, or impact evaluation.

conclusionThis study identified four risk models that can be replicated and used to predict abdominal aortic aneurysm with acceptable levels of discrimination. None of the models have been validated externally.

Indexed as

Aortic Aneurysm, AbdominalMass ScreeningHumansRisk AssessmentRisk Factors

Identifiers

PMID39287492
PMCPMC11406543

What OpenQuestion holds

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Registered trials

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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.