Background: This 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.
Methods: Electronic 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.
Results: The 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.
Conclusion: This 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.
Men in the UK are offered screening for abdominal aortic aneurysm (AAA) when they are 65 years old. Increasing costs in health services and the fact that AAA is becoming rarer mean that the future of screening is uncertain. One possible future of screening is to screen using a predictive model. This is called targeted screening. A predictive model can tell who is most likely to have an AAA. The aim of this study was to explore models in the literature and the main risk factors for AAA that these models identified. A systematic literature review was conducted to identify all relevant models. The initial search identified 4813 scientific articles. After title, abstract, and full-text screening, 37 models were found. The 37 models were analysed to identify the most common factors used to predict AAA. The factors age, sex, height and weight, smoking, high blood pressure, and heart disease were found to be the best predictors of developing an AAA. Only four of the studies reported the model in full, making them suitable for use in targeted screening. None of the four models had been tested based on data external to where they were developed, which limits their validity for use in screening programmes.
© The Author(s) 2024. Published by Oxford University Press on behalf of BJS Foundation Ltd.