Concomitant cardiac amyloidosis and aortic stenosis: update on diagnosis and management
Bibliographic record
Abstract
Concomitant aortic stenosis (AS) and cardiac amyloidosis (CA) represent a significant and increasingly recognized clinical challenge, particularly in elderly populations. This review aims to present current knowledge on the prevalence, clinical characteristics, imaging findings, outcomes and management strategies for patients with both AS and CA. Studies indicate that transthyretin cardiac amyloidosis (ATTR-CA) frequently coexists with AS, especially in patients undergoing transcatheter aortic valve replacement (TAVR), with prevalence rates ranging from 4% to 16%. The dual pathology exacerbates heart failure risk, increases mortality, and complicates therapeutic decision-making. Diagnosing CA in the presence of AS is complex due to overlapping clinical and imaging features. A multi-parametric diagnostic approach is essential, incorporating clinical assessment, advanced echocardiography, cardiac magnetic resonance imaging, and bone scintigraphy of the heart. The presence of CA influences the management of AS, often favoring TAVR over surgical valve replacement due to increased surgical risk. Emerging pharmacological treatments for ATTR-CA offer survival benefits and may alter the natural disease progression. This review highlights the need for heightened clinical awareness, early diagnosis through advanced imaging modalities, and tailored therapeutic strategies to improve outcomes in patients with concomitant AS and CA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".