Use of Biologic Therapy in <scp>AA</scp> Amyloidosis Patients Undergoing Dialysis—A Systematic Literature Review
Bibliographic record
Abstract
BACKGROUND: The advent of biological agents has provided significant therapeutic opportunities for patients with AA amyloidosis. However, when these patients reach end-stage renal disease and begin dialysis, some clinicians may discontinue biological treatments due to the heightened risk of infections. Given that AA amyloidosis is a progressive condition, there is a potential for the disease to affect additional organs in these patients. Consequently, we aimed to evaluate the benefits and risks associated with biological agents in AA amyloidosis patients receiving dialysis. METHOD: We performed a systematic literature review in Cochrane Database and MEDLINE about the use of biologic agents in AA amyloidosis patients undergoing dialysis. RESULTS: We identified fifty-five patients across twenty-two studies. Familial Mediterranean fever was the etiology in 21 patients (71.4% anakinra and 28.6% canakinumab), rheumatoid arthritis in 17 patients (52.9% etanercept and 47.1% tocilizumab), unknown etiology in 8 patients (62.5% anakinra and 37.5% tocilizumab), ankylosing spondylitis in 5 patients (40% etanercept, 40% adalimumab, and 20% infliximab), hidradenitis suppurativa in 3 patients and tumor necrosis factor receptor-associated periodic syndrome (TRAPS) in 1 patient. Biologic agents were effective or partially effective for primary disease control in 52 patients (94.5%). Two patients were able to discontinue dialysis. Most frequent side effects were infections (8 episodes in 7 patients). Eight patients died (5 due to infections, one due to cardiac causes and two due to pulmonary hemorrhage). CONCLUSION: Biologic agents are effective in AA amyloidosis patients that are treated with dialysis and seem to have an acceptable safety profile.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".