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Sökning: onr:"swepub:oai:lup.lub.lu.se:b3894f90-0c70-470d-a1eb-53e8cd1e5049" > Machine Learning-De...

Machine Learning-Derived Echocardiographic Phenotypes Predict Heart Failure Incidence in Asymptomatic Individuals

Kobayashi, Masatake (författare)
University Hospital of Nancy
Huttin, Olivier (författare)
University Hospital of Nancy
Magnusson, Martin (författare)
Lund University,Lunds universitet,Kardiovaskulär forskning - hypertoni,Forskargrupper vid Lunds universitet,WCMM- Wallenberg center för molekylär medicinsk forskning,Medicinska fakulteten,Cardiovascular Research - Hypertension,Lund University Research Groups,WCMM-Wallenberg Centre for Molecular Medicine,Faculty of Medicine
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Ferreira, João Pedro (författare)
University Hospital of Nancy
Bozec, Erwan (författare)
University Hospital of Nancy
Huby, Anne-Cecile (författare)
University Hospital of Nancy
Preud'homme, Gregoire (författare)
University Hospital of Nancy
Duarte, Kevin (författare)
University Hospital of Nancy
Lamiral, Zohra (författare)
University Hospital of Nancy
Dalleau, Kevin (författare)
University of Lorraine
Bresso, Emmanuel (författare)
University of Lorraine
Smaïl-Tabbone, Malika (författare)
University of Lorraine
Devignes, Marie-Dominique (författare)
University of Lorraine
Nilsson, Peter M (författare)
Lund University,Lunds universitet,Enheten för medicinens historia,Sektion V,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Internmedicin - epidemiologi,Forskargrupper vid Lunds universitet,History of Medicine,Section V,Department of Clinical Sciences, Lund,Faculty of Medicine,Internal Medicine - Epidemiology,Lund University Research Groups,Skåne University Hospital
Leosdottir, Margret (författare)
Lund University,Lunds universitet,Internmedicin - epidemiologi,Forskargrupper vid Lunds universitet,Internal Medicine - Epidemiology,Lund University Research Groups,Skåne University Hospital
Boivin, Jean-Marc (författare)
University Hospital of Nancy
Zannad, Faiez (författare)
University Hospital of Nancy
Rossignol, Patrick (författare)
University Hospital of Nancy
Girerd, Nicolas (författare)
University Hospital of Nancy
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 (creator_code:org_t)
 
Elsevier BV, 2022
2022
Engelska.
Ingår i: JACC: Cardiovascular Imaging. - : Elsevier BV. - 1876-7591 .- 1936-878X. ; 15:2, s. 193-208
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • OBJECTIVES: This study sought to identify homogenous echocardiographic phenotypes in community-based cohorts and assess their association with outcomes.BACKGROUND: Asymptomatic cardiac dysfunction leads to a high risk of long-term cardiovascular morbidity and mortality; however, better echocardiographic classification of asymptomatic individuals remains a challenge.METHODS: Echocardiographic phenotypes were identified using K-means clustering in the first generation of the STANISLAS (Yearly non-invasive follow-up of Health status of Lorraine insured inhabitants) cohort (N = 827; mean age: 60 ± 5 years; men: 48%), and their associations with vascular function and circulating biomarkers were also assessed. These phenotypes were externally validated in the Malmö Preventive Project cohort (N = 1,394; mean age: 67 ± 6 years; men: 70%), and their associations with the composite of cardiovascular mortality (CVM) or heart failure hospitalization (HFH) were assessed as well.RESULTS: Three echocardiographic phenotypes were identified as "mostly normal (MN)" (n = 334), "diastolic changes (D)" (n = 323), and "diastolic changes with structural remodeling (D/S)" (n = 170). The D and D/S phenotypes had similar ages, body mass indices, cardiovascular risk factors, vascular impairments, and diastolic function changes. The D phenotype consisted mainly of women and featured increased levels of inflammatory biomarkers, whereas the D/S phenotype, consisted predominantly of men, displayed the highest values of left ventricular mass, volume, and remodeling biomarkers. The phenotypes were predicted based on a simple algorithm including e', left ventricular mass and volume (e'VM algorithm). In the Malmö cohort, subgroups derived from e'VM algorithm were significantly associated with a higher risk of CVM and HFH (adjusted HR in the D phenotype = 1.87; 95% CI: 1.04 to 3.37; adjusted HR in the D/S phenotype = 3.02; 95% CI: 1.71 to 5.34).CONCLUSIONS: Among asymptomatic, middle-aged individuals, echocardiographic data-driven classification based on the simple e'VM algorithm identified profiles with different long-term HF risk. (4th Visit at 17 Years of Cohort STANISLAS-Stanislas Ancillary Study ESCIF [STANISLASV4]; NCT01391442).

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Kardiologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)

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