IVIg Use Associated with Ten-Fold Reduction of Serious Infections in Multiple Myeloma Patients Treated with Anti-BCMA Bispecific Antibodies
Bibliographic record
Abstract
BCMA-targeted bispecific antibodies (BiAb) are efficacious in relapsed/refractory multiple myeloma; however, serious infections have emerged as important toxicities. In this retrospective study, we characterized all infections and their risk factors, and evaluated the impact of infection prophylaxis in patients treated with BCMA-targeted BiAbs. Among 37 patients, 15 (41%) experienced a grade 3-5 infection, with two infection-related deaths during deep remissions. Most (84%) infections occurred during disease remissions. The cumulative probability of grade 3-5 infection increased over time with no plateau. Among responders (n = 26), profound hypogammaglobulinemia occurred in 100% and continued throughout the entire duration of treatment. During periods when patients were receiving intravenous immunoglobulin (IVIg), the rate of grade 3-5 infections was 90% lower than during observation (incidence rate ratio, 0.10; 95% confidence interval, 0.01-0.80; P = 0.0307). No other risk factors for infection were identified. This study demonstrates that profound hypogammaglobulinemia is universal with BCMA-targeted BiAbs, with intravenous immunoglobulin potentially abrogating most of the infection risk. SIGNIFICANCE: To the best of our knowledge, this is the first study to comprehensively analyze risk factors and mitigation strategies to prevent infections in myeloma patients receiving anti-BCMA bispecific antibodies. Profound and prolonged hypogammaglobulinemia was universal among responders, while immunoglobulin replacement was associated with 90% lower rates of grade 3-5 infections. See related commentary by Garfall and Stadtmauer, p. 427 . This article is featured in Selected Articles from This Issue, p. 419.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".