Improved Leukemia-Free and Overall Survival in Patients with Myelodysplastic Syndrome Receiving Iron Chelation Therapy: A Subgroup Analysis.
Bibliographic record
Abstract
Abstract Myelodysplastic syndrome (MDS) is characterized by ineffective hematopoiesis and risk of progression to acute myeloid leukemia (AML). Many MDS patients (pts) require red blood cell (RBC) transfusions, risking iron overload (IOL)-related organ dysfunction. We previously showed in a multivariate analysis of 178 pts, improved survival in 18 pts with low and int-1 IPSS risk MDS and IOL receiving iron chelation therapy (ICT), and now examine the effect of ICT on AML-free survival (LFS). The effect of ICT on cytopenias and RBC transfusion requirements (TR); was also examined. To control for possible bias favoring ICT pts, a subgroup analysis was performed. Each of 18 ICT pts had a non-ICT control pt (CP) selected and these groups are matched for gender; neutrophil count (NC), platelet count (PLTC) and hemoglobin (Hb) at diagnosis (Dx); MDS subtype; no of cytopenias, karyotype; IPSS score; ECOG Performance Status; no of serious infections; initial ferritin level (FL); total RBC units (U) received; primary MDS treatment (Rx); and duration of follow-up (FU; all p=NS). Median age in ICT pts was 64 (range 32–70) years (y) vs. 78 (39–81)y for CP (p=0.04). Features of the 178 pts are previously reported. In a univariate analysis (n=178), factors significant for LFS were: MDS subtype; IPSS risk; increased FL; total RBCU transfused; ≥1 serious infection; and receipt of ICT (all p<0.05). Factors significant for LFS in a multivariate analysis (n=178) were: IPSS score (p=0.0001) and receipt of ICT (p=0.03). For low and int-1 IPSS, median LFS was not reached (NR) at 226 months (mo) for ICT pts and 40 mo for non-ICT pts (n=76) and 4y LFS was 64% and 42% respectively (p=0.002). In the subgroup analysis comparing ICT pts to 18 CP with matched features, at a median FU of 51.4 (7.1–225.8) mo and 44.8 (10.1–224) mo respectively, median LFS was NR at 226 mo vs. 40 mo in CP, and 4y LFS was 64% and 49% respectively (p=0.009). Median OS for ICT pts was NR at 226 mo vs. 40.5 mo for CP, and 4y OS was 64% and 49% respectively (p=0.01). There were 5 deaths (28%) in ICT pts (cardiac/IOL, n=2; infection, n=1; other MDS-related, n=1; AML, n=1) and 15 (83%) in CP (other MDS-related, n=6; AML, n=4; bleeding, n=2; infection, n=2; MDS-unrelated, n=1). Although non-ICT pts were older, only 1 death was age-related and all others were from MDS. One ICT pt developed AML at 15 mo from MDS Dx as did 4 non-ICT pts (p=0.06) at a median of 35 (19–71) mo; 2 pts received chemotherapy and both died of progressive AML. In ICT pts, mean ± standard error of the mean (sem) initial/pre-ICT FL was higher than in CP (4038±627 vs. 1759±1108 ug/L respectively, p=0.09), and FU levels decreased for ICT pts (3070±411, p=0.09) but not for CP (2185±996 ug/L, p=NS). There was no difference between ICT pts and CP in mean ± sem initial (2.0±0.3 and 2.0±0.5) and FU (3.4±0.9 and 7.4±4) x109/L NC; initial (236±32 and 104±25) and FU (164±24 and 82±20) x109/L PLTC; or initial (2.6±0.3 and 3.0±0.5) and FU (4.2±0.5 and 4.3±0.9) RBC-TR (no RBCU/4wk; all p=NS). In conclusion, LFS and OS in MDS pts with IOL receiving ICT were improved compared to non-ICT control pts matched for baseline features. These results support findings in the larger cohort of MDS pts and suggest there may be a beneficial effect of ICT on AML transformation and OS. Prospective studies of ICT in MDS pts are warranted.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".