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Author Spotlight: Unveiling Prognostic Indicators in Heart Failure - The Role of Phase Angle and Bioelectrical Impedance Analysis
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Prognostic Models in Heart Failure: Hope or Hype?
Spyridon Skoularigkis1, Christos Kourek2, Andrew Xanthopoulos1
1Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece.
Journal of Personalized Medicine
|August 27, 2025
Summary
Accurate heart failure (HF) prognostication is vital for patient care. While current tools exist, novel approaches like AI and multi-omics offer promise for personalized risk assessment, but require validation and integration.
Area of Science:
- Cardiology
- Medical Informatics
- Biostatistics
Background:
- Heart failure (HF) presents a significant global health challenge, marked by high morbidity, mortality, and healthcare expenditures.
- Effective prognostication is essential for tailoring treatments, allocating resources, and guiding patient management strategies.
Purpose of the Study:
- To review the current landscape of prognostic models for heart failure.
- To identify limitations of existing tools and explore emerging technologies for improved risk stratification.
Main Methods:
- Comprehensive literature review of prognostic models in heart failure, encompassing clinical scores, biomarkers, imaging, and artificial intelligence.
- Analysis of the generalizability, data integration, and clinical utility of various prognostic tools.
- Exploration of novel approaches including machine learning, multi-omics, and remote monitoring.
Main Results:
- Existing prognostic models, while useful, often suffer from poor generalizability, static data reliance, and limited clinical integration.
- Emerging technologies like machine learning and multi-omics show potential for dynamic and individualized HF risk assessment.
- Challenges remain in interpretability, validation, and ethical implementation of advanced prognostic methods.
Conclusions:
- Prognostic models hold significant potential to revolutionize heart failure management.
- Successful clinical translation requires continuous updating, external validation, and seamless integration into healthcare workflows.
- Addressing limitations through interdisciplinary collaboration and patient-centered innovation is crucial for realizing the full impact of prognostic tools.
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