Pomalidomide Use and Kidney Outcomes in Patients With Relapsed/Refractory Multiple Myeloma and Chronic Kidney Disease: A Real-World, Population-Based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Pomalidomide-based regimens are the cornerstone of treatment for relapsed/refractory MM (RRMM). Despite the high incidence of chronic kidney disease (CKD) in RRMM, individuals with advanced CKD have been excluded from phase II/III RCTs, creating a gap in our understanding of the effects of pomalidomide use in patients with RRMM complicated with advanced CKD. We undertook a cohort to study to understand the efficacy safety of pomalidomide-based regimens among patients with CKD using real-world data. METHODS: Population-based, cohort study of patients ≥ 18 years with RRMM treated with pomalidomide in Ontario, Canada. Primary outcome was all-cause mortality. Secondary outcomes were time-to-major adverse kidney events (MAKE), time-to-next treatment, kidney response and safety. RESULTS: ). Mean age was 70.2 years, 43.3% were women. Patients with advanced CKD had a higher risk of all-cause mortality compared to the preserved kidney function group (aHR 1.37, 95% CI 1.06, 1.78). MAKE was higher in advanced CKD (aHR 1.70, 95% CI 1.03, 2.35). Kidney response was similar between moderate and severe CKD groups (aOR 1.04, 95%, CI 0.56-1.90). Safety outcomes were similar between groups. CONCLUSIONS: Patients with advanced CKD and RRMM on pomalidomide-based regimens exhibited reduced survival and a higher risk for MAKE. However, the probability of experiencing some degree of kidney recovery is 50% in both moderate and severe CKD, with comparable safety outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".