Infection Prophylaxis with Intravenous Immunoglobulin in Multiple Myeloma Patients Treated with Teclistamab
Bibliographic record
Abstract
Introduction: B-cell maturation antigen-targeting bispecific antibodies such as teclistamab have been associated with increased risk of infections as compared with conventional treatment regimens. This study explored the efficacy of intravenous immunoglobulin (IVIG) prophylaxis in reducing infection-related hospitalizations (IRHs) in MM patients who underwent treatment with teclistamab. METHODS: This was a retrospective study of MM treated with teclistamab at Taussig Cancer Center from December 16, 2022, to March 31, 2024. The primary endpoint was incidence rate of IRHs per patient-day on-IVIG vs. off-IVIG. RESULTS: Among the 44 patients included in the study, there were 19 infectious episodes that required inpatient hospitalization, occurring among 17 patients. Five infections occurred during 4,378 days during the "on-IVIG" period, compared to 14 infections occurring during 4,619 days for the "off-IVIG" period for an infectious incidence rate ratio between the two groups of 2.65 (p value = 0.027). CONCLUSION: Patients treated with bispecific antibodies such as teclistamab are highly susceptible to infections due to impaired humoral immunity and hypogammaglobulinemia. Our findings demonstrate a reduction in infection incidence in patients while receiving IVIG. These results support the use of IVIG as an effective prophylactic strategy to reduce infectious risk in this vulnerable patient population. .
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 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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".