4.6 Article

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

Journal

JACC-CARDIOVASCULAR IMAGING
Volume 15, Issue 2, Pages 193-208

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.jcmg.2021.07.004

Keywords

biomarkers; cardiovascular diseases; cluster analysis; echocardiogram; heart failure; machine learning; prognosis

Funding

  1. CHRU
  2. Programme Hospitalier de Recherche Clinique Interregional
  3. French National Research Agency Fighting Heart Failure [ANR-15-RHU-0004]
  4. FEDER Lorraine
  5. French Programme d'investissements d'avenir project Lorraine Universite d'Excellence GEENAGE [ANR-15-IDEX-04-LUE]
  6. Contrat de Plan Etat Region Lorraine
  7. FEDER IT2MP
  8. European Union Commission [305507]
  9. French National Research Agency
  10. French PIA project Lorraine Universite d'Excellence GEENAGE [ANR15-IDEX-04-LUE]
  11. Medical Faculty of Lund University
  12. Skane University Hospital
  13. Crafoord Foundation
  14. Ernhold Lundstroms Research Foundation
  15. Region Skane
  16. Hulda and Conrad Mossfelt Foundation
  17. Southwest Skanes Diabetes Foundation
  18. Kockska Foundation
  19. Research Funds of Region Skane
  20. Swedish Heart and Lung Foundation
  21. 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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