Evidence map›Paper›PMID 29959136›Full record

Observational studyJournal of the American Heart Association2018

Bioimpedance and New-Onset Heart Failure: A Longitudinal Study of >500 000 Individuals From the General Population.

Daniel Lindholm, Eri Fukaya, Nicholas J Leeper, Erik Ingelsson

Open access · goldAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Journal of the American Heart Association, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 2 pooled it
4.9field-weighted citation impact, top 4% 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

24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 65 citations in OpenAlex.

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  16. Cardiovascular Diseases Diagnosis by Impedance Cardiography.Journal of electrical bioimpedance · 2022
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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

4 authors at 1 institution in 2 countries.

Daniel LindholmDepartment of Medical Sciences, Cardiology, Uppsala University, Uppsala, Sweden.
Eri FukayaDivision of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA.
Nicholas J LeeperDivision of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA.
Erik IngelssonDivision of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA eriking@stanford.edu.
Stanford University · US

Funding

Causal associations of circulating biomarkers with cardiovascular diseaseR01HL135313 · NHLBI · STANFORD UNIVERSITY · PI INGELSSON, ERIK · 2017 to 2019
$1.6M
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NHLBI NIH HHS R01 HL135313
6 · The paper itself

Abstract

backgroundHeart failure constitutes a high burden on patients and society, but although lifetime risk is high, it is difficult to predict without costly or invasive testing. We aimed to establish new risk factors of heart failure, which potentially could enable early diagnosis and preemptive treatment. METHODS AND

resultsWe applied machine learning in the UK Biobank in an agnostic search of risk factors for heart failure in 500 451 individuals, excluding individuals with prior heart failure. Novel factors were then subjected to several in-depth analyses, including multivariable Cox models of incident heart failure, and assessment of discrimination and calibration. Machine learning confirmed many known and putative risk factors for heart failure and identified several novel candidates. Mean reticulocyte volume appeared as one novel factor and leg bioimpedance another, the latter appearing as the most important new marker. Leg bioimpedance was lower in those who developed heart failure during an up to 9.8-year follow-up. When adjusting for known heart failure risk factors, leg bioimpedance was inversely related to heart failure (hazard ratio [95% confidence interval], 0.60 [0.48-0.73] and 0.75 [0.59-0.94], in age- and sex-adjusted and fully adjusted models, respectively, comparing the upper versus lower quartile). A model including leg bioimpedance, age, sex, and self-reported history of myocardial infarction showed good discrimination for future heart failure hospitalization (Concordance index [C-index]=0.82) and good calibration.

conclusionsLeg bioimpedance is inversely associated with heart failure incidence in the general population. A simple model of exclusively noninvasive measures, combining leg bioimpedance with history of myocardial infarction, age, and sex provides accurate predictive capacity.

Indexed as

Body CompositionDecision Support TechniquesMachine LearningAgedAge FactorsElectric ImpedanceFemaleHeart FailureHumansIncidenceLegLongitudinal StudiesMaleMiddle AgedMyocardial InfarctionPrognosisheart failuremachine learningrisk factors

Identifiers

PMID29959136
PMCPMC6064899
OpenAlexW2950145193

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.