Portal de investigação
Machine Learning-Derived Echocardiographic Phenotypes Predict Heart Failure Incidence in Asymptomatic Individuals
Data de publicação:
Data Ahead of Print:
Autores da FMUP
Participantes de fora da FMUP
- Kobayashi, M
- Huttin, O
- Magnusson, M
- Bozec, E
- Huby, AC
- Preud'homme, G
- Duarte, K
- Lamiral, Z
- Dalleau, K
- Bresso, E
- Smaïl-Tabbone, M
- Devignes, MD
- Nilsson, PM
- Leosdottir, M
- Boivin, JM
- Zannad, F
- Rossignol, P
- Girerd, N
- STANISLAS Study Investigators
Unidades de investigação
Abstract
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.
Dados da publicação
- ISSN/ISSNe:
- 1936-878X, 1876-7591
- Tipo:
- Article
- Páginas:
- 193-208
- Link para outro recurso:
- www.scopus.com
JACC-CARDIOVASCULAR IMAGING Elsevier Inc.
Citações Recebidas na Web of Science: 27
Citações Recebidas na Scopus: 53
Documentos
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Filiações
Keywords
- biomarkers; cardiovascular diseases; cluster analysis; echocardiogram; heart failure; machine learning; prognosis
Campos de estudo
Financiamento
Proyectos asociados
Dapagliflozin, Spironolactone or Both for HFpEF (SOGALDI-PEF) - NCT05676684
Investigador Principal: João Pedro Melo Marques Pinho Ferreira
Ensaio Clínico Académico (SOGALDI-PEF) . AstraZeneca . 2022
Citar a publicação
Kobayashi M,Huttin O,Magnusson M,Ferreira JP,Bozec E,Huby AC,Preud'homme G,Duarte K,Lamiral Z,Dalleau K,Bresso E,Smaïl M,Devignes MD,Nilsson PM,Leosdottir M,Boivin JM,Zannad F,Rossignol P,Girerd N,STANISLAS I. Machine Learning-Derived Echocardiographic Phenotypes Predict Heart Failure Incidence in Asymptomatic Individuals. JACC Cardiovasc. Imaging. 2022. 15. (2):p. 193-208. IF:14,000. (1).
