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Machine Learning-De...
Machine Learning-Derived Echocardiographic Phenotypes Predict Heart Failure Incidence in Asymptomatic Individuals
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- Kobayashi, Masatake (author)
- University Hospital of Nancy
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- Huttin, Olivier (author)
- University Hospital of Nancy
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- Magnusson, Martin (author)
- 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 (author)
- University Hospital of Nancy
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- Bozec, Erwan (author)
- University Hospital of Nancy
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- Huby, Anne-Cecile (author)
- University Hospital of Nancy
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- Preud'homme, Gregoire (author)
- University Hospital of Nancy
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- Duarte, Kevin (author)
- University Hospital of Nancy
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- Lamiral, Zohra (author)
- University Hospital of Nancy
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- Dalleau, Kevin (author)
- University of Lorraine
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- Bresso, Emmanuel (author)
- University of Lorraine
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- Smaïl-Tabbone, Malika (author)
- University of Lorraine
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- Devignes, Marie-Dominique (author)
- University of Lorraine
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- Nilsson, Peter M (author)
- 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
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- Leosdottir, Margret (author)
- Lund University,Lunds universitet,Internmedicin - epidemiologi,Forskargrupper vid Lunds universitet,Internal Medicine - Epidemiology,Lund University Research Groups,Skåne University Hospital
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- Boivin, Jean-Marc (author)
- University Hospital of Nancy
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- Zannad, Faiez (author)
- University Hospital of Nancy
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- Rossignol, Patrick (author)
- University Hospital of Nancy
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- Girerd, Nicolas (author)
- University Hospital of Nancy
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(creator_code:org_t)
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- Elsevier BV, 2022
- 2022
- English.
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In: JACC: Cardiovascular Imaging. - : Elsevier BV. - 1876-7591 .- 1936-878X. ; 15:2, s. 193-208
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Abstract
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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).
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
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- art (subject category)
- ref (subject category)
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- By the author/editor
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Kobayashi, Masat ...
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Huttin, Olivier
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Magnusson, Marti ...
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Ferreira, João P ...
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Bozec, Erwan
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Huby, Anne-Cecil ...
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show more...
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Preud'homme, Gre ...
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Duarte, Kevin
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Lamiral, Zohra
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Dalleau, Kevin
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Bresso, Emmanuel
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Smaïl-Tabbone, M ...
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Devignes, Marie- ...
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Nilsson, Peter M
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Leosdottir, Marg ...
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Boivin, Jean-Mar ...
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Zannad, Faiez
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Rossignol, Patri ...
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Girerd, Nicolas
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- About the subject
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- MEDICAL AND HEALTH SCIENCES
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MEDICAL AND HEAL ...
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and Clinical Medicin ...
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and Cardiac and Card ...
- Articles in the publication
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JACC: Cardiovasc ...
- By the university
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Lund University