Interleukin-1 blockade in cardiac sarcoidosis: study design of the multimodality assessment of granulomas in cardiac sarcoidosis: Anakinra Randomized Trial (MAGiC-ART)
Bibliographic record
Abstract
BACKGROUND: Sarcoidosis is an inflammatory disease characterized by the formation of granulomas, which involve the heart in up to 25% of patients. Cardiac sarcoidosis can lead to life threatening arrhythmias and heart failure. While corticosteroids have been used as a treatment for over 50 years, they are associated with hypertension, diabetes, and weight gain, further increasing cardiovascular risk. Interleukin-1 (IL-1) is the prototypical proinflammatory cytokine that works to activate the nuclear transcription factor NF-kB, one of the targets of glucocorticoids. IL-1 also plays an important role also in the pathophysiology of heart disease including atherosclerosis, myocardial infarction, and myocarditis. METHODS: Building on a network of research collaborators developed in the Cardiac Sarcoidosis Consortium, we will investigate the feasibility and tolerability of treatment of CS with anakinra at two National Institute of Health Clinical and Translational Science Award (CTSA) hubs with expertise in cardiac sarcoidosis. In this pilot study, up to 28 patients with cardiac sarcoidosis will be recruited to compare the administration of an IL-1 blocker, anakinra, 100 mg daily on top of standard of care versus standard of care only for 28 days and followed for 180 days. Utilizing surrogate endpoints of changes in systemic inflammatory biomarkers and cardiac imaging, we aim to determine whether IL-1 blockade with anakinra can combat systemic and cardiac inflammation in patients with cardiac sarcoidosis. DISCUSSION: The current trial demonstrates an innovative collaborative approach to clinical trial development in a rare, understudied disease that disproportionately affects females and minorities. Trial Registration The trial was registered prospectively with ClinicalTrials.gov on July 12, 2019, identifier NCT04017936.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".