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Machine Learning-Derived Echocardiographic Phenotypes Predict Heart Failure Incidence in Asymptomatic Individuals
Masatake Kobayashi1, Olivier Huttin1, Martin Magnusson2
1Centre d'Investigations Cliniques Plurithématique 1433, Université de Lorraine, Institut national de la santé et de la recherche médicale 1116, Centre Hospitalier Universitaire Régional de Nancy, France; French Clinical Research Infrastructure Network "Investigation" Network Initiative-Cardiovascular and Renal Clinical Trialists" Cardiovascular and Renal Clinical Trialists Network, France.
Insights
Echocardiography can identify distinct cardiac profiles in asymptomatic individuals, predicting future heart failure hospitalization and cardiovascular mortality risk. The e'VM algorithm classifies these phenotypes for better risk stratification.
Area of Science:
- Cardiology
- Medical Imaging
- Public Health
Background:
- Asymptomatic cardiac dysfunction poses a significant risk for long-term cardiovascular morbidity and mortality.
- Accurate echocardiographic classification of asymptomatic individuals remains a clinical challenge.
Purpose of the Study:
- To identify homogeneous echocardiographic phenotypes in community-based cohorts.
- To assess the association of these phenotypes with cardiovascular outcomes.
- To validate an algorithm for predicting these phenotypes.
Main Methods:
- K-means clustering was used to identify echocardiographic phenotypes in the STANISLAS cohort.
- Phenotypes were externally validated in the Malmö Preventive Project cohort.
- Associations with vascular function, biomarkers, cardiovascular mortality, and heart failure hospitalization were assessed.
Main Results:
- Three phenotypes were identified: 'mostly normal' (MN), 'diastolic changes' (D), and 'diastolic changes with structural remodeling' (D/S).
- The D/S phenotype showed the highest left ventricular remodeling biomarkers, while the D phenotype had increased inflammatory biomarkers.
- The e'VM algorithm predicted phenotypes associated with significantly higher risks of cardiovascular mortality and heart failure hospitalization.
Conclusions:
- Echocardiographic data-driven classification using the e'VM algorithm can identify distinct profiles in asymptomatic individuals.
- These identified profiles are associated with varying long-term risks of heart failure hospitalization.
- The e'VM algorithm offers a simple method for risk stratification in asymptomatic populations.
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).
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