Aortic Stenosis, Heart Failure, and Aortic Valve Replacement
Bibliographic record
Abstract
Importance: Heart failure (HF) and aortic stenosis (AS) frequently coexist, presenting a complex clinical challenge due to their intertwined pathophysiology and associated high morbidity and mortality. Despite numerous advancements in transcatheter and surgical aortic valve replacement (AVR), HF decompensation remains the leading cause of cardiac rehospitalization and a major predictor of mortality in patients with AS, before or after AVR. This review aims to provide a comprehensive analysis of the interplay between AS and HF, delving into myocardial changes caused by stenotic insult, the impact of AVR on these changes, and the prevalence and contributing elements of HF before and after AVR. Observations: The prevalence of HF remains high before and after AVR, particularly among patients with left ventricular dysfunction. Increased afterload from AS causes cardiac remodeling, which is initially benign but over time these changes become maladaptive, contributing to HF and increased mortality. The progression of HF is influenced by the degree of reverse cardiac remodeling, which can be affected by comorbid conditions, the hemodynamic performance of the valve prosthesis, and vascular stiffness. Several blood and imaging biomarkers offer insights into underlying AS pathophysiology, serving as mortality predictors and predicting HF in this patient population. Conclusions and Relevance: HF development in AS is multifactorial and its link to left ventricular dysfunction is a complex process. Delineating the determinants of HF admissions in AS is crucial for identifying individuals at high risk. Identifying the early signs of left ventricular decompensation by using surrogate markers may be the key, even before left ventricular function becomes impaired. Translating multimodality imaging techniques and biomarkers into routine clinical practice for evaluating cardiac damage and integrating these markers with patient and procedural factors that affect HF before and after AVR can facilitate timely intervention, minimizing the likelihood of HF progression and influencing future guidelines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".