Heart Failure Classification

Heart Failure Classification: A Comprehensive Overview

Universal Definition and Classification of Heart Failure

Universal Definition of Heart Failure

Heart failure (HF) is defined as a clinical syndrome characterized by symptoms and/or signs caused by a structural and/or functional cardiac abnormality. This condition is corroborated by elevated natriuretic peptide levels and/or objective evidence of pulmonary or systemic congestion .

Stages of Heart Failure

The stages of HF have been revised to better categorize the progression of the disease:

Stage A (At-risk for HF): Patients at risk for HF but without current or prior symptoms or signs of HF and without structural or biomarker evidence of heart disease.
Stage B (Pre-heart failure): Patients without current or prior symptoms or signs of HF but with evidence of structural heart disease, abnormal cardiac function, or elevated natriuretic peptide levels.
Stage C (Heart failure): Patients with current or prior symptoms and/or signs of HF caused by a structural and/or functional cardiac abnormality.
Stage D (Advanced heart failure): Patients with severe symptoms and/or signs of HF at rest, recurrent hospitalizations despite guideline-directed management and therapy (GDMT), refractory or intolerant to GDMT, requiring advanced therapies such as consideration for transplant, mechanical circulatory support, or palliative care .

Classification by Left Ventricular Ejection Fraction (LVEF)

HF is also classified based on left ventricular ejection fraction (LVEF):

HF with reduced EF (HFrEF): LVEF ≤ 40%
HF with mid-range EF (HFmrEF): LVEF 41-49%
HF with preserved EF (HFpEF): LVEF ≥ 50%
HF with improved EF (HFimpEF): Baseline LVEF ≤ 40%, a ≥ 10 point increase from baseline LVEF, and a second measurement of LVEF > 40% .

New York Heart Association (NYHA) Classification

NYHA Functional Classification

The NYHA classification is a widely used tool for risk stratification in HF, categorizing patients based on the severity of their symptoms:

Class I: No limitation of physical activity.
Class II: Slight limitation of physical activity; comfortable at rest.
Class III: Marked limitation of physical activity; comfortable at rest.
Class IV: Unable to carry out any physical activity without discomfort; symptoms of HF at rest.

Limitations of NYHA Classification

Despite its widespread use, the NYHA classification has limitations in adequately stratifying risk. Studies have shown significant overlap in clinical parameters such as NT-proBNP levels, KCCQ scores, and 6-minute walk distances among different NYHA classes, indicating a need for improved phenotyping to facilitate better risk stratification and response to interventions.

TNM-like and HLM Classification Systems

MOGE(S) and HLM Classifications

The MOGE(S) classification, similar to the TNM system in oncology, includes morphofunctional characteristics (M), organ involvement (O), genetic or familial inheritance pattern (G), etiological information (E), and functional status (S). However, a new TNM-like classification named HLM has been proposed, focusing on heart damage (H), lung involvement (L), and malfunction of peripheral organs (M). This system aims to provide simplicity, clinical usefulness, and efficacy for planning therapeutic strategies and determining patient prognosis.

Machine Learning and Data-Mining Approaches

Advanced Classification Methods

Modern data-mining and machine-learning methods, such as bootstrap aggregation (bagging), boosting, random forests, and support vector machines, have shown superior performance in predicting and classifying HF subtypes compared to conventional classification trees. However, logistic regression remains more effective for predicting the presence of HFpEF.

Phenomapping and Phenotype-Based Classification

Phenomapping for HFpEF

Phenomapping, using dense phenotypic data and statistical learning algorithms, has identified distinct phenotypic categories within HFpEF. This novel classification method has demonstrated the ability to stratify risk and improve the understanding of this heterogeneous syndrome, potentially leading to more targeted therapies .

Beyond Ejection Fraction: A Continuous Spectrum

Dynamic Nature of HF

The traditional classification of HF based on LVEF has been challenged, suggesting that HF is a dynamic syndrome with a spectrum of phenotypes. Recognizing the continuous nature of HF progression can lead to novel stratification methods and better clinical trial designs.

Conclusion

The classification of heart failure has evolved significantly, incorporating stages, LVEF categories, and advanced phenotyping methods. While traditional systems like NYHA and LVEF-based classifications remain useful, new approaches such as phenomapping and machine learning offer promising improvements in understanding and managing this complex syndrome.

Summary

Not a yes or no question