Journal
JACC-CARDIOVASCULAR IMAGING
Volume 15, Issue 2, Pages 193-208Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jcmg.2021.07.004
Keywords
biomarkers; cardiovascular diseases; cluster analysis; echocardiogram; heart failure; machine learning; prognosis
Funding
- CHRU
- Programme Hospitalier de Recherche Clinique Interregional
- French National Research Agency Fighting Heart Failure [ANR-15-RHU-0004]
- FEDER Lorraine
- French Programme d'investissements d'avenir project Lorraine Universite d'Excellence GEENAGE [ANR-15-IDEX-04-LUE]
- Contrat de Plan Etat Region Lorraine
- FEDER IT2MP
- European Union Commission [305507]
- French National Research Agency
- French PIA project Lorraine Universite d'Excellence GEENAGE [ANR15-IDEX-04-LUE]
- Medical Faculty of Lund University
- Skane University Hospital
- Crafoord Foundation
- Ernhold Lundstroms Research Foundation
- Region Skane
- Hulda and Conrad Mossfelt Foundation
- Southwest Skanes Diabetes Foundation
- Kockska Foundation
- Research Funds of Region Skane
- Swedish Heart and Lung Foundation
- Wallenberg Center for Molecular Medicine, Lund University
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This study identified three echocardiographic phenotypes in community-based cohorts, including mostly normal, diastolic changes, and diastolic changes with structural remodeling. These phenotypes were associated with vascular function and circulating biomarkers. In a separate cohort, they were also found to be associated with cardiovascular mortality or heart failure hospitalization.
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 Malmo 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 (e0VM algorithm). In the Malmo 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 STANISLASStanislas Ancillary Study ESCIF [STANISLASV4]; NCT01391442) (C) 2022 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation.
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