Evaluating the Cost-Effectiveness of Changes to the Surveillance Intervals in the UK Abdominal Aortic Aneurysm Screening Programme

Machine learning algorithms for the prognostication of abdominal aortic aneurysm progression: a systematic review

INTRODUCTION

Abdominal aortic aneurysm (AAA), often characterized by an abdominal aortic diameter over 3.0 cm, is managed through screening, surveillance, and surgical intervention. AAA growth can be heterogeneous and rupture carries a high mortality rate, with size and certain risk factors influencing rupture risk. Research is ongoing to accurately predict individual AAA growth rates for personalized management. Machine learning, a subset of artificial intelligence, has shown promise in various medical fields, including endoleak detection post-EVAR. However, its application for predicting AAA growth remains insufficiently explored, thus necessitating further investigation. Subsequently, this paper aims to summarize the current status of machine learning in predicting AAA growth.

EVIDENCE ACQUISITION

A systematic database search of Embase, MEDLINE, Cochrane, PubMed and Google Scholar from inception till December 2022 was conducted of original articles that discussed the use of machine learning in predicting AAA growth using the aforementioned databases.

EVIDENCE SYNTHESIS

Overall, 2742 articles were extracted, of which seven retrospective studies involving 410 patients were included using a predetermined criteria. Six out of seven studies applied a supervised learning approach for their machine learning (ML) models, with considerable diversity observed within specific ML models. The majority of the studies concluded that machine learning models perform better in predicting AAA growth in comparison to reference models. All studies focused on predicting AAA growth over specified durations. Maximal luminal diameter was the most frequently used indicator, with alternative predictors being AAA volume, ILT (intraluminal thrombus) and flow-medicated diameter (FMD).

CONCLUSIONS

The nascent field of applying machine learning (ML) for Abdominal Aortic Aneurysm (AAA) expansion prediction exhibits potential to enhance predictive accuracy across diverse parameters. Future studies must emphasize evidencing clinical utility in a healthcare system context, thereby ensuring patient outcome improvement. This will necessitate addressing key ethical implications in establishing prospective studies related to this topic and collaboration among pivotal stakeholders within the AI field.

Health Economic Research Assessing the Value of Early Detection of Cardiovascular Disease: A Systematic Review

INTRODUCTION

Cardiovascular disease (CVD) is the most prominent cause of death worldwide and has a major impact on healthcare budgets. While early detection strategies may reduce the overall CVD burden through earlier treatment, it is unclear which strategies are (most) efficient.

AIM

This systematic review reports on the cost effectiveness of recent early detection strategies for CVD in adult populations at risk.

METHODS

PubMed and Scopus were searched to identify scientific articles published between January 2016 and May 2022. The first reviewer screened all articles, a second reviewer independently assessed a random 10% sample of the articles for validation. Discrepancies were solved through discussion, involving a third reviewer if necessary. All costs were converted to 2021 euros. Reporting quality of all studies was assessed using the CHEERS 2022 checklist.

RESULTS

In total, 49 out of 5552 articles were included for data extraction and assessment of reporting quality, reporting on 48 unique early detection strategies. Early detection of atrial fibrillation in asymptomatic patients was most frequently studied (n = 15) followed by abdominal aortic aneurysm (n = 8), hypertension (n = 7) and predicted 10-year CVD risk (n = 5). Overall, 43 strategies (87.8%) were reported as cost effective and 11 (22.5%) CVD-related strategies reported cost reductions. Reporting quality ranged between 25 and 86%.

CONCLUSIONS

Current evidence suggests that early CVD detection strategies are predominantly cost effective and may reduce CVD-related costs compared with no early detection. However, the lack of standardisation complicates the comparison of cost-effectiveness outcomes between studies. Real-world cost effectiveness of early CVD detection strategies will depend on the target country and local context.

REGISTRATION OF SYSTEMATIC REVIEW

CRD42022321585 in International Prospective Registry of Ongoing Systematic Reviews (PROSPERO) submitted at 10 May 2022.

Cost-effectiveness of repeat delayed imaging for spontaneous subarachnoid hemorrhage

BACKGROUND

In patients with intracranial aneurysm presenting with spontaneous subarachnoid hemorrhage (SAH), 15% of them could be missed by the initial diagnostic imaging. Repeat delayed imaging can help to identify previously undetected aneurysms, however, the cost-effectiveness of this strategy remains uncertain.

OBJECTIVE

The aim of this study is to assess the cost-effectiveness of repeat delayed imaging in patients with SAH who had a negative result during their initial imaging.

METHODS

A Markov model was developed to estimate the lifetime costs and quality-adjusted life-year (QALY) for patients who received or not received repeat delayed imaging. The analyses were conducted from a healthcare perspective, with costs reported in UK pounds and expressed in 2020 values. Extensive sensitivity analyses were performed to assess the robustness of the results.

RESULTS

The base case incremental cost-effectiveness ratio (ICER) of repeat delayed imaging is £9,314 per QALY compared to no-repeat delayed imaging. This ICER is below the National Institute for Health and Care Excellence (NICE) £20,000 per QALY willingness-to-pay threshold. At the NICE willingness-to-pay threshold of £20,000 per QALY, the probability that repeat delayed imaging is most cost-effective is 0.81. The results are sensitive to age, the utility of survived patients with a favorable outcome, the sensitivity of repeat delayed imaging, and the prevalence of aneurysm.

CONCLUSIONS

This study showed that, in the UK, it is cost-effective to provide repeat delayed imaging using computed tomographic angiography (CTA) for patients with SAH who had a negative result in their initial imaging.

Incidence and Outcomes of Abdominal Aortic Aneurysm Repair in New Zealand from 2001 to 2021

Purpose: The burden of abdominal aortic aneurysms (AAA) has changed in the last 20 years but is still considered to be a major cause of cardiovascular mortality. The introduction of endovascular aortic repair (EVAR) and improved peri-operative care has resulted in a steady improvement in both outcomes and long-term survival. The objective of this study was to identify the burden of AAA disease by analysing AAA-related hospitalisations and deaths. Methodology: All AAA-related hospitalisations in NZ from January 2001 to December 2021 were identified from the National Minimum Dataset, and mortality data were obtained from the NZ Mortality Collection dataset from January 2001 to December 2018. Data was analysed for patient characteristics including deprivation index, repair methods and 30-day outcomes. Results: From 2001 to 2021, 14,436 patients with an intact AAA were identified with a mean age of 75.1 years (SD 9.7 years), and 4100 (28%) were females. From 2001 to 2018, there were 5000 ruptured AAA with a mean age of 77.8 (SD 9.4), and 1676 (33%) were females. The rate of hospitalisations related to AAA has decreased from 43.7 per 100,000 in 2001 to 15.4 per 100,000 in 2018. There was a higher proportion of rupture AAA in patients living in more deprived areas. The use of EVAR for intact AAA repair has increased from 18.1% in 2001 to 64.3% in 2021. The proportion of octogenarians undergoing intact AAA repair has increased from 16.2% in 2001 to 28.4% in 2021. The 30-day mortality for intact AAA repair has declined from 5.8% in 2001 to 1.7% in 2021; however, it has remained unchanged for ruptured AAA repair at 31.6% across the same period. Conclusions: This study highlights that the incidence of AAA has declined in the last two decades. The mortality has improved for patients who had a planned repair. Understanding the contemporary burden of AAA is paramount to improve access to health, reduce variation in outcomes and promote surgical quality improvement.

Automated Screening for Abdominal Aortic Aneurysm in CT Scans under Clinical Conditions Using Deep Learning

Abdominal aortic aneurysms (AAA) may remain clinically silent until they enlarge and patients present with a potentially lethal rupture. This necessitates early detection and elective treatment. The goal of this study was to develop an easy-to-train algorithm which is capable of automated AAA screening in CT scans and can be applied to an intra-hospital environment. Three deep convolutional neural networks (ResNet, VGG-16 and AlexNet) were adapted for 3D classification and applied to a dataset consisting of 187 heterogenous CT scans. The 3D ResNet outperformed both other networks. Across the five folds of the first training dataset it achieved an accuracy of 0.856 and an area under the curve (AUC) of 0.926. Subsequently, the algorithms performance was verified on a second data set containing 106 scans, where it ran fully automated and resulted in an accuracy of 0.953 and an AUC of 0.971. A layer-wise relevance propagation (LRP) made the decision process interpretable and showed that the network correctly focused on the aortic lumen. In conclusion, the deep learning-based screening proved to be robust and showed high performance even on a heterogeneous multi-center data set. Integration into hospital workflow and its effect on aneurysm management would be an exciting topic of future research.