Agents contributing to secondary immunodeficiency development in patients with multiple myeloma, chronic lymphocytic leukemia and non-Hodgkin lymphoma: A systematic literature review
Bibliographic record
Abstract
Introduction: Patients with hematological malignancies (HMs), like chronic lymphocytic leukemia (CLL), multiple myeloma (MM), and non-Hodgkin lymphoma (NHL), have a high risk of secondary immunodeficiency (SID), SID-related infections, and mortality. Here, we report the results of a systematic literature review on the potential association of various cancer regimens with infection rates, neutropenia, lymphocytopenia, or hypogammaglobulinemia, indicative of SID. Methods: A systematic literature search was performed in 03/2022 using PubMed to search for clinical trials that mentioned in the title and/or abstract selected cancer (CLL, MM, or NHL) treatments covering 12 classes of drugs, including B-lineage monoclonal antibodies, CAR T therapies, proteasome inhibitors, kinase inhibitors, immunomodulators, antimetabolites, anti-tumor antibiotics, alkylating agents, Bcl-2 antagonists, histone deacetylase inhibitors, vinca alkaloids, and selective inhibitors of nuclear export. To be included, a publication had to report at least one of the following: percentages of patients with any grade and/or grade ≥3 infections, any grade and/or grade ≥3 neutropenia, or hypogammaglobulinemia. From the relevant publications, the percentages of patients with lymphocytopenia and specific types of infection (fungal, viral, bacterial, respiratory [upper or lower respiratory tract], bronchitis, pneumonia, urinary tract infection, skin, gastrointestinal, and sepsis) were collected. Results: Of 89 relevant studies, 17, 38, and 34 included patients with CLL, MM, and NHL, respectively. In CLL, MM, and NHL, any grade infections were seen in 51.3%, 35.9% and 31.1% of patients, and any grade neutropenia in 36.3%, 36.4%, and 35.4% of patients, respectively. The highest proportion of patients with grade ≥3 infections across classes of drugs were: 41.0% in patients with MM treated with a B-lineage monoclonal antibody combination; and 29.9% and 38.0% of patients with CLL and NHL treated with a kinase inhibitor combination, respectively. In the limited studies, the mean percentage of patients with lymphocytopenia was 1.9%, 11.9%, and 38.6% in CLL, MM, and NHL, respectively. Two studies reported the proportion of patients with hypogammaglobulinemia: 0-15.3% in CLL and 5.9% in NHL (no studies reported hypogammaglobulinemia in MM). Conclusion: This review highlights cancer treatments contributing to infections and neutropenia, potentially related to SID, and shows underreporting of hypogammaglobulinemia and lymphocytopenia before and during HM therapies.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".