Evidence map›Paper›PMID 40208429›Full record

ArticleThe international journal of cardiovascular imaging2025

Prevalence of low-flow state by automated machine-learning 3D echocardiography in patients with moderate-to-severe aortic stenosis and normal ejection-fraction.

Andrea Barbieri, Vera Laus, Francesca Bursi, Silvia Bonatti, Mattia Malaguti, Matteo Paolini, Giuseppe Boriani

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Article in The international journal of cardiovascular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Andrea BarbieriCardiology Division, Department of Biomedical, Metabolic and Neural Sciences, Policlinico di Modena, University of Modena and Reggio Emilia, Via del Pozzo, 71, Modena, 41124, Italy. barbieriandrea65@gmail.com.
Vera LausCardiology Division, Department of Biomedical, Metabolic and Neural Sciences, Policlinico di Modena, University of Modena and Reggio Emilia, Via del Pozzo, 71, Modena, 41124, Italy.
Francesca BursiDepartment of Health Sciences, Division of Cardiology, ASST Santi Paolo e Carlo, San Paolo Hospital, University of Milan, Milan, Italy.
Silvia BonattiCardiology Division, Department of Biomedical, Metabolic and Neural Sciences, Policlinico di Modena, University of Modena and Reggio Emilia, Via del Pozzo, 71, Modena, 41124, Italy.
Mattia MalagutiCardiology Division, Department of Biomedical, Metabolic and Neural Sciences, Policlinico di Modena, University of Modena and Reggio Emilia, Via del Pozzo, 71, Modena, 41124, Italy.
Matteo PaoliniCardiology Division, Department of Biomedical, Metabolic and Neural Sciences, Policlinico di Modena, University of Modena and Reggio Emilia, Via del Pozzo, 71, Modena, 41124, Italy.
Giuseppe BorianiCardiology Division, Department of Biomedical, Metabolic and Neural Sciences, Policlinico di Modena, University of Modena and Reggio Emilia, Via del Pozzo, 71, Modena, 41124, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn aortic valve stenosis (AS), measurement of stroke volume index (SVi) by the 2D Doppler- technique is required to calculate the aortic valve area (AVA) and determine flow status but is prone to systematic errors. PURPOSE: To investigate the prevalence of low-flow (LF) state (SVi ≤ 35 ml/m

methodsConsecutive patients with moderate or severe AS (≤1.5 cm

resultsWe included 57 patients (median 78 years [70-85]); 40% were women. The mean AVA was 1.03 ± 0.37 cm

conclusionsIn patients with moderate or severe AS and normal EF, the prevalence of LF state defined as SVi ≤ 35 ml/m

Indexed as

Aortic ValveAortic Valve StenosisEchocardiography, DopplerEchocardiography, Three-DimensionalHemodynamicsImage Interpretation, Computer-AssistedMachine LearningStroke VolumeVentricular Function, LeftAgedAged, 80 and overAutomationBlood Flow VelocityFemaleHumansMale3D-echocardiographyAortic stenosisArtificial intelligenceMachine learning

Identifiers

PMID40208429

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.