A Systematic Review and Meta-Analysis of the Effect of Iron Chelation Therapy on Survival in Patients with Lower Risk Myelodysplastic Syndrome
Bibliographic record
Abstract
Abstract Background: Anemia is the most common cytopenia among patients with myelodysplastic syndromes (MDS) often necessitating regular red blood cell (RBC) transfusions. As a result, most patients develop iron overload (IO). RBC transfusion dependency and IO have been associated with worse clinical outcomes, including inferior overall survival (OS), in MDS patients. While iron chelation therapy (ICT) is recommended by most clinical guidelines and experts for select MDS patients, the use of ICT remains controversial in the absence of published, randomized controlled trials. To evaluate the impact of ICT on survival among lower risk MDS patients, we performed a systematic review and meta-analysis (SRMA) of the literature. Methods: We conducted an SRMA according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Meta-Analysis of Observational Studies in Epidemiology (MOOSE) guidelines. PubMed and Embase electronic databases were searched through October 2016 with search terms “MDS” OR “myelodysplastic syndrome” AND “iron chelation” OR “iron chelating” OR “iron overload” AND “survival” OR “death” AND “study” OR “trial.” Additionally, the Cochrane Library, the World Health Organization (WHO) International Clinical Trials Registry, and abstracts from recent American Society of Hematology annual meetings (years 2014-2016) were searched. One study, published in 2014, had an updated analysis in 2017 that was used for the current analysis. Information abstracted included data relating to study and patient characteristics, outcome measures (mortality and progression to acute myeloid leukemia [AML]), and measures of effect (calculated odds, hazard ratios [HRs], and confounding variables for adjustment). A Random Effects model was used to compute an adjusted HR (aHR) among the different studies. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of studies. Study heterogeneity was assessed using the Cochran Q and the I2 statistic. Publication bias was assessed through visual examination of a funnel plot and with the Egger's test for small-study effects. Results: Of the 331 records screened, 23 were assessed for eligibility, and 9 were included (Figure 1). Of these 9 studies, 6 were considered to be of high quality based on the NOS. All 9 were observational studies (4 prospective and 5 retrospective); 8 were identified as including patients with the International Prognostic Scoring System (IPSS)-defined low and intermediate-1 risk MDS. In these 8 studies, ICT was associated with an overall lower risk of mortality (aHR 0.43; 95% confidence interval [CI] 0.30-0.60; p Conclusions: This SRMA suggests a significant reduction in the adjusted risk of mortality among IPSS low and intermediate-1 risk MDS patients treated with ICT. Furthermore, our analysis suggests that ICT is associated with delayed progression to AML, although the causality of these associations cannot be established based on this study. Randomized controlled studies are needed to confirm these findings. Download : Download high-res image (132KB) Download : Download full-size image Disclosures Ballas: Novartis: Honoraria, Speakers Bureau. Zeidan: AbbVie, Otsuka, Pfizer, Gilead, Celgene, Ariad, Incyte: Consultancy, Honoraria; Takeda: Speakers Bureau; Otsuka: Consultancy.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".