Can Treatment with Venetoclax for Chronic Lymphocytic Leukemia (CLL) Result In Autoimmune Hemolytic Anemia?
Bibliographic record
Abstract
BACKGROUND Chronic lymphocytic leukemia (CLL) is a hematological disease characterized by the clonal proliferation and accumulation of neoplastic B lymphocytes in the blood, bone marrow, lymph nodes, and spleen. Autoimmune hemolytic anemia (AIHA) is an acquired hemolytic anemia in which the destruction of erythrocytes is helped by anti-erythrocyte auto-antibodies. This has a controversial effect on the clinical outcome and survival of patients with CLL. Venetoclax, a second-generation BH3 mimetic compound, is one of the new therapies that has been approved for the treatment of CLL. Venetoclax disrupts the antiapoptotic signaling through BCL2. Common adverse events associated with venetoclax include neutropenia, thrombocytopenia, and diarrhea. This case report describes a patient with CLL who developed AIHA when treated with venetoclax. CASE REPORT A patient of 62-year-old woman, who was treated with multiple lines of therapy, presented autoimmune hemolytic anemia after treatment with venetoclax. The anemia was resolved after holding venetoclax and being treated with rituximab. In January 2019, there were reports of 7 patients developing AIHA related to venetoclax therapy in Europe, according to the EudraVigilance database. How venetoclax can cause AIHA is not completely clear. This complication can happen when the erythrocyte antigen is altered by the drug that can produce antibodies. The other described mechanism is the binding of the drug with erythrocytes, which leads to production of an immune response. CONCLUSIONS Although AIHA can be a complication of CLL, it may be caused by treatment with venetoclax. That may be confirmed after eliminating other causes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".