SynthesisThe British journal of surgery2024
Risk factor-targeted abdominal aortic aneurysm screening: systematic review of risk prediction for abdominal aortic aneurysm.
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.
What it found
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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.
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.
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Who cites it
9 citing papers in PubMed.
- Prediction model for aortic dissection, aortic aneurysm, and peripheral artery disease.Hypertension research : official journal of the Japanese Society of Hypertension · 2026Article
- Abdominal aortic aneurysm progression: A review of preclinical and clinical data.Clinical research in cardiology : official journal of the German Cardiac Society · 2026Review
- TCF7L2 promotes abdominal aortic aneurysm through smooth muscle cell-mediated extracellular matrix remodeling.JCI insight · 2026Article
- Chromobox 3 assembles an epigenetic complex contributing to cystathionine γ-lyase-mediated protection against aortic aneurysm/dissection.Nature communications · 2026Article
- Adverse exposure burden, NMR metabolomics, and risk of incident abdominal aortic aneurysm.Frontiers in public health · 2026Article
- Robotic surgery in healthcare: current challenges, technological advances, and global implementation prospects.Journal of robotic surgery · 2025Review
- Development and validation of a risk prediction model for abdominal aortic aneurysm: a nationwide population-based cohort study.Scientific reports · 2025Article
- Diagnostic value of platelet to high-density lipoprotein cholesterol ratio in abdominal aortic aneurysms.Frontiers in cardiovascular medicine · 2025Article
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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.
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Registered trials
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.