Adverse Hematological and Non-Hematological Events in Patients With Relapsed/Refractory Multiple Myeloma That Are Responsive to Daratumumab, Pomalidomide and Dexamethasone
Bibliographic record
Abstract
Background: Daratumumab, pomalidomide, and dexamethasone (DPd) is an effective option for treatment of patients with relapsed/refractory multiple myeloma (RRMM). In this study, we sought to analyze the risk of hematological and non-hematological toxicities in patients who responded to DPd treatment. Methods: We analyzed 97 patients with RRMM who were treated with DPd between January 2015 and June 2022. The patients and disease characteristics, as well as safety and efficacy outcomes were summarized as descriptive analysis. Results: The overall response rate for the entire group was 74% (n = 72). The most common grade III/IV hematological toxicities in those who responded to treatment were neutropenia (79%), leukopenia (65%), lymphopenia (56%), anemia (18%), and thrombocytopenia (8%). The most common grade III/IV non-hematological toxicities were pneumonia (17%) and peripheral neuropathy (8%). The incidence of dose reduction/interruption was 76% (55/72), which was due to hematological toxicity in 73% of the cases. The most common reason for discontinuing treatment was disease progression in 61% (44 out of 72 patients). Conclusions: Our study revealed that patients who respond to DPd are at high risk of dose reduction or treatment interruption because of hematological toxicity, typically due to neutropenia and leukopenia leading to increased risk of hospitalization and pneumonia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".