A health-related quality-of-life (HRQoL) analysis of pomalidomide + low-dose dexamethasone + daratumumab in relapsed refractory multiple myeloma (RRMM) after lenalidomide treatment.
Bibliographic record
Abstract
8025 Background: Treatment (Tx) of RRMM is complex and requires evaluation of disease and patient (pt) factors to maximize efficacy and minimize toxicity. HRQoL has become an important aspect of MM Tx, as survival has improved with therapeutic advances. Results of the ongoing phase 2 MM-014 trial (NCT01946477) have demonstrated that pomalidomide (POM) + low-dose dexamethasone (LoDEX) + daratumumab (DARA) is safe and effective in RRMM pts after first- or second-line lenalidomide (LEN)-based Tx failure. Here we report the impact of this regimen on HRQoL. Methods: RRMM pts with 1 to 2 prior Tx lines, LEN-based Tx as their most recent regimen, and progressive disease during or after their last Tx line received POM + LoDEX + DARA in 28-day cycles (MM-014 cohort B). HRQoL, an exploratory endpoint for cohort B, was assessed via EuroQol’s EQ-5D. Results: As of October 15, 2018, 108 pts were evaluable for HRQoL. Baseline characteristics were similar to those of the ITT population (N = 112). EQ-5D completion rates for each cycle (1-6) were ≥ 88%. Mean change from baseline in the EQ-5D index and VAS health score was stable through 6 Tx cycles. At cycle 6, 28.8% and 39.0% of pts achieved minimum clinically important improvement in the EQ-5D index (≥ 0.1) and VAS health score (≥ 6), respectively. EQ-5D index values were stable, with a trend toward improvement in usual activities, pain/discomfort, and anxiety/depression (Table). Conclusions: In RRMM pts with early-line LEN Tx failure, HRQoL was maintained or trended toward improvement with POM + LoDEX + DARA, despite the combination of 3 drugs with distinct toxicities. These findings further support the earlier use of POM-based Tx in RRMM immediately after LEN failure. Clinical trial information: NCT01946477. [Table: see text]
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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) | 0.001 | 0.001 |
| 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) | 0.002 | 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".