Real-World Utilization and Safety of Daratumumab IV Rapid Infusions Administered in a Community Setting: A Retrospective Observational Study
Bibliographic record
Abstract
BACKGROUND: Some institutions have implemented a daratumumab intravenous rapid-infusion protocol in which patients with multiple myeloma (MM) receive their third and subsequent infusions within ~ 90 min instead of ≥ 3 h. OBJECTIVE: This study sought to understand the utilization, effectiveness, and infusion reactions (IRs) observed in patients with MM who received daratumumab rapid infusions. METHODS: Electronic medical records from Florida Cancer Specialists & Research Institute were used. Adult patients with MM who received one or more rapid daratumumab infusion (full dose in ≤ 110 min) at their third or later infusion of the first daratumumab-containing regimen (index date: 16 November 2015 to 15 March 2019) were included. IRs included events that (1) occurred ≤ 24 h post-daratumumab infusion or (2) were stated as an IR in the patient charts. Non-IR adverse events (AEs) were events attributed to daratumumab in patient charts that did not meet the IR definition. RESULTS: In total, 147 patients received one or more rapid infusion in their first daratumumab-containing regimen. Median time from initial MM diagnosis to index date was 2.5 years. Non-IR AEs occurred in 10.2% of patients during treatment, and 36.7% experienced one or more IR after receiving a daratumumab infusion. No IRs occurred after a rapid infusion. The overall response rate was 91.1% (after rapid infusions only: 71.3%). CONCLUSIONS: This study provides real-world evidence on the practice patterns of daratumumab rapid infusions in a large community-based oncology clinic system. These results suggest that treatment regimens including daratumumab rapid infusions at the third infusion or later were well-tolerated, and their effectiveness was comparable to that observed in clinical trials.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".