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Bioimpedance and New-Onset Heart Failure: A Longitudinal Study of >500 000 Individuals From the General Population.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cardiology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, UCR-Uppsala Clinical Research Center.
2018 (English)In: Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, ISSN 2047-9980, E-ISSN 2047-9980, Vol. 7, no 13, article id e008970Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: Heart 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 RESULTS: We 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.

CONCLUSIONS: Leg 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.

Place, publisher, year, edition, pages
2018. Vol. 7, no 13, article id e008970
Keywords [en]
heart failure, machine learning, risk factors
National Category
Cardiac and Cardiovascular Systems
Identifiers
URN: urn:nbn:se:uu:diva-355904DOI: 10.1161/JAHA.118.008970ISI: 000452700100033PubMedID: 29959136OAI: oai:DiVA.org:uu-355904DiVA, id: diva2:1231542
Available from: 2018-07-07 Created: 2018-07-07 Last updated: 2019-01-16Bibliographically approved

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Lindholm, Daniel

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