Transthyretin Amyloid Cardiomyopathy—Current and Future Therapies
Bibliographic record
Abstract
Objective: To describe the clinical presentation of transthyretin amyloid cardiomyopathy (ATTR-CM) and discuss current treatments and investigational products and their effect on patient outcomes. Data Sources: A literature search was performed in PubMed (September 2018 to December 2020) using the following keywords: transthyretin amyloidosis, cardiomyopathy, polyneuropathy and transthyretin amyloid cardiomyopathy, monoclonal light-chain, tafamidis, cardiac amyloidosis, ATTR cardiomyopathy, green tea and inhibition of cardiac amyloidosis, AG10, tolcapone, tolcapone and leptomeningeal ATTR, PRX004, NI006, patisiran, inotersen, vutrisiran, AKCEA-TTR-LRx, and NTLA-2001. Study Selection and Data Extraction: Clinical trials were evaluated for evidence supporting pharmacology, safety, efficacy, and measured outcomes. Data Synthesis: Until 2019, there were no approved treatments for ATTR-CM. Treatment consisted of symptom management and organ transplant. Nonpharmacological and pharmacological treatments focused on the symptoms of heart failure (HF) associated with ATTR-CM. However, there are several emerging therapies recently approved or in development to address the underlying pathophysiology. Treatment classes for ATTR-CM include transthyretin stabilizers, human monoclonal antibodies, gene silencers, and CRISPR/Cas9 gene editing. Relevance to Patient Care and Clinical Practice: ATTR-CM is a complex disease in which amyloidosis causes cardiomyopathy. Underdiagnosis is attributed to the clinical presentation being heterogeneous, indistinguishable from HF caused by other etiologies, and the need for invasive testing modalities, including endomyocardial biopsy. Improved diagnostic approaches along with targeted therapies can slow disease progression and enhance patient quality of life. Conclusion: Diagnostic modalities along with biomarker and genetic testing could detect disease earlier and target therapy more accurately. Novel therapies demonstrate potential treatment benefits and can help shape the standard of care for these patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".