Evidence map›Paper›PMID 34849588›Full record

ArticleThe British journal of surgery2022

Growth prediction model for abdominal aortic aneurysms.

Robin Ristl, Johannes Klopf, Andreas Scheuba, Florian Wolf, Martin Funovics, Bernd Gollackner, Anders Wanhainen, Christoph Neumayer, Martin Posch, Christine Brostjan and 1 more

Abstract readValidation Study
In one paragraph

Article in The British journal of surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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

11 authors.

Robin RistlCentre for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
Johannes KlopfDepartment of General Surgery, Division of Vascular Surgery, Medical University of Vienna, Vienna, Austria.
Andreas ScheubaDepartment of General Surgery, Division of Vascular Surgery, Medical University of Vienna, Vienna, Austria.
Florian WolfDepartment of Biomedical Imaging and Image Guided Therapy, Division of Cardiovascular and Interventional Radiology, Medical University of Vienna, Vienna, Austria.
Martin FunovicsDepartment of Biomedical Imaging and Image Guided Therapy, Division of Cardiovascular and Interventional Radiology, Medical University of Vienna, Vienna, Austria.
Bernd GollacknerDepartment of General Surgery, Division of Vascular Surgery, Medical University of Vienna, Vienna, Austria.
Anders WanhainenDepartment of Surgical Sciences, Uppsala University, Uppsala, Sweden.ORCID 0000-0002-3273-8726
Christoph NeumayerDepartment of General Surgery, Division of Vascular Surgery, Medical University of Vienna, Vienna, Austria.
Martin PoschCentre for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
Christine BrostjanDepartment of General Surgery, Division of Vascular Surgery, Medical University of Vienna, Vienna, Austria.ORCID 0000-0003-1462-5397
Wolf EilenbergDepartment of General Surgery, Division of Vascular Surgery, Medical University of Vienna, Vienna, Austria.ORCID 0000-0002-9322-6193

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe most relevant determinant in scheduling monitoring intervals for abdominal aortic aneurysms (AAAs) is maximum diameter. The aim of the study was to develop a statistical model that takes into account specific characteristics of AAA growth distributions such as between-patient variability as well as within-patient variability across time, and allows probabilistic statements to be made regarding expected AAA growth.

methodsCT angiography (CTA) data from patients monitored at 6-month intervals with maximum AAA diameters at baseline between 30 and 66 mm were used to develop the model. By extending the model of geometric Brownian motion with a log-normal random effect, a stochastic growth model was developed. An additional set of ultrasound-based growth data was used for external validation.

resultsThe study data included 363 CTAs from 87 patients, and the external validation set comprised 390 patients. Internal and external cross-validation showed that the stochastic growth model allowed accurate description of the distribution of aneurysm growth. Median relative growth within 1 year was 4.1 (5-95 per cent quantile 0.5-13.3) per cent. Model calculations further resulted in relative 1-year growth of 7.0 (1.0-16.4) per cent for patients with previously observed rapid 1-year growth of 10 per cent, and 2.6 (0.3-8.3) per cent for those with previously observed slow growth of 1 per cent. The probability of exceeding a threshold of 55 mm was calculated to be 1.78 per cent at most when adhering to the current RESCAN guidelines for rescreening intervals. An online calculator based on the fitted model was made available.

conclusionThe stochastic growth model was found to provide a reliable tool for predicting AAA growth.

Indexed as

Models, StatisticalAgedAortic Aneurysm, AbdominalComputed Tomography AngiographyDisease ProgressionFemaleHumansMalePrognosisRisk FactorsStochastic ProcessesTime Factors

Identifiers

PMID34849588
PMCPMC10364708

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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.