Isatuximab plus carfilzomib and dexamethasone versus carfilzomib and dexamethasone in relapsed multiple myeloma patients with renal impairment: IKEMA subgroup analysis
Bibliographic record
Abstract
Renal impairment (RI) is common in patients with multiple myeloma (MM) and new therapies that can improve renal function are needed. The phase III IKEMA study (clinicaltrials gov. Identifier: NCT03275285) investigated isatuximab (Isa) with carfilzomib and dexamethasone (Kd) versus Kd in relapsed MM. This subgroup analysis examined results from patients with RI, defined as estimated glomerular filtration rate <60 mL/min/1.73 m². Addition of Isa prolonged progression-free survival (PFS) in patients with RI (hazard ratio: 0.27; 95% confidence interval [CI]: 0.11-0.66; median PFS not reached for Isa-Kd versus 13.4 months for Kd [20.8-month follow-up]). Complete renal responses occurred more frequently with Isa-Kd (52.0%) versus Kd (30.8%) and were durable in 32.0% versus 7.7% of patients, respectively. Treatment exposure was longer with Isa-Kd, with median number of started cycles and median duration of exposure of 20 versus 9 cycles and 81.0 versus 35.7 weeks for Isa-Kd versus Kd, respectively. Among patients with RI, the incidence of patients with grade ≥3 treatment-emergent adverse events was similar between the two arms (79.1% in Isa-Kd vs. 77.8% in Kd). In summary, the addition of Isa to Kd improved clinical outcomes with a manageable safety profile in patients with RI, consistent with the benefit observed in the overall IKEMA study population.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.017 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".