Evidence map›Paper›PMID 38188937›Full record

ArticleEuropean heart journal open2024

Unsupervised cluster analysis reveals different phenotypes in patients after transcatheter aortic valve replacement.

Kenya Kusunose, Takumasa Tsuji, Yukina Hirata, Tomonori Takahashi, Masataka Sata, Kimi Sato, Noor Albakaa, Tomoko Ishizu, Jun'ichi Kotoku, Yoshihiro Seo and 1 more

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Article in European heart journal open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 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.

Kenya KusunoseDepartment of Cardiovascular Medicine, Nephrology, and Neurology, Graduate School of Medicine, University of the Ryukyus, 207 Uehara, Nishihara Town, Okinawa 903-0215, Japan.ORCID https://orcid.org/0000-0002-4909-754X
Takumasa TsujiDepartment of Radiological Technology, Graduate School of Medical Care and Technology, Teikyo University, Tokyo, Japan.
Yukina HirataUltrasound Examination Center, Tokushima University Hospital, Tokushima, Japan.ORCID https://orcid.org/0000-0002-3379-9387
Tomonori TakahashiDepartment of Cardiovascular Medicine, Tokushima University Hospital, 2-50-1 Kuramoto, Tokushima 770-8503, Japan.
Masataka SataDepartment of Cardiovascular Medicine, Tokushima University Hospital, 2-50-1 Kuramoto, Tokushima 770-8503, Japan.
Kimi SatoDepartment of Cardiology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.
Noor AlbakaaDepartment of Cardiology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.
Tomoko IshizuDepartment of Cardiology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.ORCID https://orcid.org/0000-0003-3794-899X
Jun'ichi KotokuDepartment of Radiological Technology, Graduate School of Medical Care and Technology, Teikyo University, Tokyo, Japan.
Yoshihiro SeoDepartment of Cardiology, Graduate School of Medical Sciences, Nagoya City University, Nagoya, Japan.
JSE-TAVI investigators

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: The aim of this study was to identify phenotypes with potential prognostic significance in aortic stenosis (AS) patients after transcatheter aortic valve replacement (TAVR) through a clustering approach. Methods and results: This multi-centre retrospective study included 1365 patients with severe AS who underwent TAVR between January 2015 and March 2019. Among demographics, laboratory, and echocardiography parameters, 20 variables were selected through dimension reduction and used for unsupervised clustering. Phenotypes and outcomes were compared between clusters. Patients were randomly divided into a derivation cohort ( Conclusion: Unsupervised cluster analysis of patients after TAVR revealed three different groups for assessment of prognosis. This provides a new perspective in the categorization of patients after TAVR that considers comorbidities and extravalvular cardiac dysfunction.

Indexed as

Aortic stenosisArtificial intelligenceEchocardiographyMachine learning

Identifiers

PMID38188937
PMCPMC10766904

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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.