Evidence map›Paper›PMID 35629216›Full record

ArticleJournal of personalized medicine2022

Application of Machine Learning Methods to Analyze Occurrence and Clinical Features of Ascending Aortic Dilatation in Patients with and without Bicuspid Aortic Valve.

Olga Irtyuga, Georgy Kopanitsa, Anna Kostareva, Oleg Metsker, Vladimir Uspensky, Gordeev Mikhail, Giuseppe Faggian, Giunai Sefieva, Ilia Derevitskii, Anna Malashicheva and 1 more

Open access · goldAbstract read
In one paragraph

Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.8field-weighted citation impact, top 29% of its field
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

4 citing papers in PubMed, 5 citations in OpenAlex.

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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 at 3 institutions in 2 countries.

Olga IrtyugaAlmazov National Medical Research Centre, 197341 Saint Petersburg, Russia.ORCID 0000-0002-8656-3191
Georgy KopanitsaAlmazov National Medical Research Centre, 197341 Saint Petersburg, Russia.ORCID 0000-0002-6231-8036
Anna KostarevaAlmazov National Medical Research Centre, 197341 Saint Petersburg, Russia.ORCID 0000-0002-9349-6257
Oleg MetskerAlmazov National Medical Research Centre, 197341 Saint Petersburg, Russia.
Vladimir UspenskyAlmazov National Medical Research Centre, 197341 Saint Petersburg, Russia.
Gordeev MikhailAlmazov National Medical Research Centre, 197341 Saint Petersburg, Russia.
Giuseppe FaggianDepartment of Cardiac Surgery, Medical School, University of Verona, 37126 Verona, Italy.ORCID 0000-0001-5371-5761
Giunai SefievaAlmazov National Medical Research Centre, 197341 Saint Petersburg, Russia.
Ilia DerevitskiiDepartment of Cardiac Surgery, Medical School, ITMO University, 49 Kronverskiy Prospect, 197101 Saint Petersburg, Russia.ORCID 0000-0002-8624-5046
Anna MalashichevaAlmazov National Medical Research Centre, 197341 Saint Petersburg, Russia.ORCID 0000-0002-0820-2913
Evgeny ShlyakhtoAlmazov National Medical Research Centre, 197341 Saint Petersburg, Russia.
Federal Almazov North-West Medical Research Centre · RUITMO University · RUUniversity of Verona · IT

Funding

Ministry of Science and Higher Education of the Russian Federation 075-15-2020-901
6 · The paper itself

Abstract

Aortic aneurysm (AA) rapture is one of the leading causes of death worldwide. Unfortunately, the diagnosis of AA is often verified after the onset of complications, in most cases after aortic rupture. The aim of this study was to evaluate the frequency of ascending aortic aneurysm (AscAA) and aortic dilatation (AD) in patients with cardiovascular diseases undergoing echocardiography, and to identify the main risk factors depending on the morphology of the aortic valve. We processed 84,851 echocardiographic (ECHO) records of 13,050 patients with aortic dilatation (AD) in the Almazov National Medical Research Centre from 2010 to 2018, using machine learning methodologies. Despite a high prevalence of AD, the main reason for the performed ECHO was coronary artery disease (CAD) and hypertension (HP) in 33.5% and 14.2% of the patient groups, respectively. The prevalence of ascending AD (>40 mm) was 15.4% (13,050 patients; 78.3% (10,212 patients) in men and 21.7% (2838 patients) in women). Only 1.6% (n = 212) of the 13,050 patients with AD knew about AD before undergoing ECHO in our center. Among all the patients who underwent ECHO, we identified 1544 (1.8%) with bicuspid aortic valve (BAV) and 635 with BAV had AD (only 4.8% of all AD patients). According to the results of the random forest feature importance analysis, we identified the eight main factors of AD: age, male sex, vmax aortic valve (AV), aortic stenosis (AS), blood pressure, aortic regurgitation (AR), diabetes mellitus, and heart failure (HF). The known factors of AD-like HP, CAD, hyperlipidemia, BAV, and obesity, were also AD risk factors, but were not as important. Our study showed a high frequency of AscAA and dilation. Standard risk factors of AscAA such as HP, hyperlipidemia, or obesity are significantly more common in patients with AD, but the main factors in the formation of AD are age, male sex, vmax AV, blood pressure, AS, AR, HF, and diabetes mellitus. In males with BAV, AD incidence did not differ significantly, but the presence of congenital heart disease was one of the 12 main risk factors for the formation of AD and association with more significant aortic dilatation in AscAA groups.

Indexed as

aneurysmascending aortic dilatationechocardiographyrisk factors

Identifiers

PMID35629216
PMCPMC9146498
OpenAlexW4280556590

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.