ArticleAmerican journal of physiology. Heart and circulatory physiology2022
Ensemble machine learning model identifies patients with HFpEF from matrix-related plasma biomarkers.
Article in American journal of physiology. Heart and circulatory physiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 13 citations in OpenAlex.
- Mechanical-medical convergence in heart failure: Artificial intelligence, finite-element modeling, and 3D printing for diagnosis and prognosis.Bioengineering & translational medicine · 2026Review
- Article
- Machine Learning Applications for Risk Stratification in Heart Failure with Preserved Ejection Fraction: A New Era in Cardiology.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence in heart failure.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2026Review
- Redox-Driven Precision Medicine for Life-Course Prevention of Cardiovascular-Kidney-Metabolic Syndrome.Antioxidants (Basel, Switzerland) · 2026Review
- The Narrative Review: Advancements in Heart Failure Diagnosis and Management using Artificial Intelligence: A New Era of Patient Care.Current cardiology reviews · 2026Review
- Integrative transcriptomic and single-cell analysis reveals mitochondrial-related gene biomarkers in heart failure with preserved ejection fraction.Scientific reports · 2025Article
- Comprehensive Plasma Proteomic Profiling Reveals Differentially Regulated Signaling Pathways Underlying Left Ventricular Hypertrophy Between Hypertrophic Cardiomyopathy and Aortic Stenosis.Journal of cardiovascular translational research · 2025Article
- Integration of PCG spectrogram texture and deep features for the diagnosis of heart failure with preserved ejection fraction using heterogeneous stacking ensemble learning.Frontiers in physiology · 2025Article
- Guidelines for mechanistic modeling and analysis in cardiovascular research.American journal of physiology. Heart and circulatory physiology · 2024Review
- The value of speckle-tracking stratified strain combined with myocardial work measurement in evaluating left ventricular function in patients with heart failure with preserved ejection fraction.Quantitative imaging in medicine and surgery · 2024Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
Arterial hypertension can lead to structural changes within the heart including left ventricular hypertrophy (LVH) and eventually heart failure with preserved ejection fraction (HFpEF). The initial diagnosis of HFpEF is costly and generally based on later stage remodeling; thus, improved predictive diagnostic tools offer potential clinical benefit. Recent work has shown predictive value of multibiomarker plasma panels for the classification of patients with LVH and HFpEF. We hypothesized that machine learning algorithms could substantially improve the predictive value of circulating plasma biomarkers by leveraging more sophisticated statistical approaches. In this work, we developed an ensemble classification algorithm for the diagnosis of HFpEF within a population of 480 individuals including patients with HFpEF, patients with LVH, and referent control patients. Algorithms showed strong diagnostic performance with receiver-operating-characteristic curve (ROC) areas of 0.92 for identifying patients with LVH and 0.90 for identifying patients with HFpEF using demographic information, plasma biomarkers related to extracellular matrix remodeling, and echocardiogram data. More impressively, the ensemble algorithm produced an ROC area of 0.88 for HFpEF diagnosis using only demographic and plasma panel data. Our findings demonstrate that machine learning-based classification algorithms show promise as a noninvasive diagnostic tool for HFpEF, while also suggesting priority biomarkers for future mechanistic studies to elucidate more specific regulatory roles.
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