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Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
Published on: February 13, 2021
Phenomapping of patients with heart failure with preserved ejection fraction using machine learning-based
Matthew W Segar1, Kershaw V Patel1, Colby Ayers1
1Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Insights
Unsupervised clustering identified three heart failure with preserved ejection fraction (HFpEF) phenogroups. Phenogroup 1 showed the highest risk for adverse outcomes, while Phenogroup 3 had the most favorable profile.
Area of Science:
- Cardiology
- Biostatistics
- Machine Learning in Medicine
Background:
- Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous condition.
- Identifying distinct HFpEF subgroups is crucial for targeted therapies and improved patient outcomes.
- Previous attempts to stratify HFpEF patients have faced challenges due to data complexity and heterogeneity.
Purpose of the Study:
- To apply unsupervised clustering analysis to a high-dimensional, mixed-data cohort of HFpEF patients.
- To identify distinct phenotypic subgroups within the HFpEF population.
- To evaluate the differential clinical characteristics and long-term outcomes associated with identified HFpEF phenogroups.
Main Methods:
- Utilized penalized finite mixture model-based clustering on 61 phenotypic variables from 654 Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) participants.
- Characterized three distinct HFpEF phenogroups based on co-morbidities, natriuretic peptides, and left ventricular structure/function.
- Validated findings in an internal TOPCAT cohort (n=1113) and an external RELAX trial cohort (n=198) using adjusted Cox models.
Main Results:
- Phenogroup 1 exhibited a high burden of co-morbidities and cardiac abnormalities, associated with significantly higher risks of adverse events, mortality, and HF hospitalization.
- Phenogroup 2 showed lower co-morbidities but higher diastolic dysfunction, linked to increased HF hospitalization risk but lower atherosclerotic event risk.
- Phenogroup 3 presented with lower natriuretic peptides, intermediate co-morbidities, and favorable diastolic function, serving as the reference group for lower adverse outcomes.
Conclusions:
- Machine learning-based cluster analysis effectively identifies HFpEF phenogroups with distinct clinical profiles.
- These identified phenogroups demonstrate significantly different long-term clinical outcomes, highlighting HFpEF heterogeneity.
- The findings support the potential for personalized medicine approaches in managing HFpEF based on identified patient subgroups.
Aim:
To identify distinct phenotypic subgroups in a highly-dimensional, mixed-data cohort of individuals with heart failure (HF) with preserved ejection fraction (HFpEF) using unsupervised clustering analysis.
Methods And Results:
The study included all Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) participants from the Americas (n = 1767). In the subset of participants with available echocardiographic data (derivation cohort, n = 654), we characterized three mutually exclusive phenogroups of HFpEF participants using penalized finite mixture model-based clustering analysis on 61 mixed-data phenotypic variables. Phenogroup 1 had higher burden of co-morbidities, natriuretic peptides, and abnormalities in left ventricular structure and function; phenogroup 2 had lower prevalence of cardiovascular and non-cardiac co-morbidities but higher burden of diastolic dysfunction; and phenogroup 3 had lower natriuretic peptide levels, intermediate co-morbidity burden, and the most favourable diastolic function profile. In adjusted Cox models, participants in phenogroup 1 (vs. phenogroup 3) had significantly higher risk for all adverse clinical events including the primary composite endpoint, all-cause mortality, and HF hospitalization. Phenogroup 2 (vs. phenogroup 3) was significantly associated with higher risk of HF hospitalization but a lower risk of atherosclerotic event (myocardial infarction, stroke, or cardiovascular death), and comparable risk of mortality. Similar patterns of association were also observed in the non-echocardiographic TOPCAT cohort (internal validation cohort, n = 1113) and an external cohort of patients with HFpEF [Phosphodiesterase-5 Inhibition to Improve Clinical Status and Exercise Capacity in Heart Failure with Preserved Ejection Fraction (RELAX) trial cohort, n = 198], with the highest risk of adverse outcome noted in phenogroup 1 participants.
Conclusions:
Machine learning-based cluster analysis can identify phenogroups of patients with HFpEF with distinct clinical characteristics and long-term outcomes.
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