Evidence map›Paper›PMID 30482050›Full record

ArticleEuropean journal of preventive cardiology2019

Opportunistic screening models for high-risk men and women to detect diastolic dysfunction and heart failure with preserved ejection fraction in the community.

Aisha Gohar, Rogier F Kievit, Gideon B Valstar, Arno W Hoes, Evelien E Van Riet, Yvonne van Mourik, Loes C Bertens, Leandra J Boonman-Winter, Michiel L Bots, Hester M Den Ruijter and 1 more

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In one paragraph

Article in European journal of preventive cardiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.4field-weighted citation impact, top 10% 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

11 citing papers in PubMed, 22 citations in OpenAlex.

  1. Article
  2. Women's heart disease research in the netherlands: angina with non-obstructive coronary artery disease and beyond.Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation · 2025
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  11. Sex and Gender Differences in Heart Failure.International journal of heart failure · 2020
    Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 4 institutions in 1 country.

Aisha Gohar1 Julius Center for Health Sciences and Primary Care, Utrecht University, The Netherlands.
Rogier F Kievit1 Julius Center for Health Sciences and Primary Care, Utrecht University, The Netherlands.
Gideon B Valstar2 Laboratory for Experimental Cardiology, Utrecht University, The Netherlands.
Arno W Hoes1 Julius Center for Health Sciences and Primary Care, Utrecht University, The Netherlands.
Evelien E Van Riet1 Julius Center for Health Sciences and Primary Care, Utrecht University, The Netherlands.
Yvonne van Mourik1 Julius Center for Health Sciences and Primary Care, Utrecht University, The Netherlands.
Loes C Bertens3 Department of Obstetrics and Gynaecology, Erasmus Medical Center, The Netherlands.
Leandra J Boonman-Winter4 Amphia Academy, The Netherlands.
Michiel L Bots1 Julius Center for Health Sciences and Primary Care, Utrecht University, The Netherlands.
Hester M Den Ruijter2 Laboratory for Experimental Cardiology, Utrecht University, The Netherlands.
Frans H Rutten1 Julius Center for Health Sciences and Primary Care, Utrecht University, The Netherlands.
Utrecht University · NLUniversity Medical Center Utrecht · NLAmphia Ziekenhuis · NLErasmus MC · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe prevalence of undetected left ventricular diastolic dysfunction is high, especially in the elderly with comorbidities. Left ventricular diastolic dysfunction is a prognostic indicator of heart failure, in particularly of heart failure with preserved ejection fraction and of future cardiovascular and all-cause mortality. Therefore we aimed to develop sex-specific diagnostic models to enable the early identification of men and women at high-risk of left ventricular diastolic dysfunction with or without symptoms of heart failure who require more aggressive preventative strategies.

designIndividual patient data from four primary care heart failure-screening studies were analysed (1371 participants, excluding patients classified as heart failure and left ventricular ejection fraction <50%).

methodsEleven candidate predictors were entered into logistic regression models to be associated with the presence of left ventricular diastolic dysfunction/heart failure with preserved ejection fraction in men and women separately. Internal-external cross-validation was performed to develop and validate the models.

resultsIncreased age and β-blocker therapy remained as predictors in both the models for men and women. The model for men additionally consisted of increased body mass index, moderate to severe shortness of breath, increased pulse pressure and history of ischaemic heart disease. The models performed moderately and similarly well in men (c-statistics range 0.60-0.75) and women (c-statistics range 0.51-0.76) and the performance improved significantly following the addition of N-terminal pro b-type natriuretic peptide (c-statistics range 0.61-0.80 in women and 0.68-0.80 in men).

conclusionsWe provide an easy-to-use screening tool for use in the community, which can improve the early detection of left ventricular diastolic dysfunction/heart failure with preserved ejection fraction in high-risk men and women and optimise tailoring of preventive interventions.

Indexed as

Mass ScreeningStroke VolumeVentricular Function, LeftAgedAged, 80 and overDiastoleFemaleHealth StatusHeart FailureHumansMaleMiddle AgedPredictive Value of TestsPrimary Health CarePrognosisReproducibility of Resultscommunityheart failure with preserved ejection fractionleft ventricular diastolic dysfunctionScreeningsex

Identifiers

PMID30482050
PMCPMC6431757
OpenAlexW2902685762

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LicenceCC BY-NC
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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.