A Systematic Review and Meta-Analysis of Deferiprone Monotherapy and in Combination with Deferoxamine for Reduction of Iron Overload in Chronically Transfused Patients with β-Thalassemia
Bibliographic record
Abstract
β-Thalassemia major (β-TM) patients require life-long blood transfusions, resulting in iron overload with multi-organ morbidity and mortality. Evidence from small randomized controlled trials (RCTs) published to date for deferiprone (DFP) monotherapy or in combination with deferoxamine (DFO) is unclear. We summarized evidence on the efficacy of DFP monotherapy compared to DFO, and DFP-DFO combination therapy compared to DFP or DFO monotherapy in chronically transfused β-TM. We searched four electronic databases and examined the grey literature. Two authors independently assessed trial quality and extracted data. We calculated the relative risk for dichotomous outcomes and mean difference (MD) for continuous outcomes. We identified 15 RCTs (1003 participants) that met the inclusion criteria. Deferiprone was more efficacious than DFO in improving cardiac ejection fraction [MD 2.88, 95% CI (95% confidence interval) 1.12 to 4.64, p = 0.001) and endocrine dysfunction (MD 0.09, 95% CI 0.08 to 0.10, p < 0.00001). The DFP-DFO combination therapy was more efficacious than DFP or DFO monotherapy in improving cardiac ejection fraction (MD 5.67, 95% CI 1.32 to 10.02, p = 0.008). There was no significant difference in all other outcomes examined. Meta-analysis on changes in myocardial iron content was not possible due to differences in data presentation. The quality of evidence for all outcomes was low. There is currently insufficient evidence to show that DFP is superior to DFO in the treatment of iron overload. The use of DFP must be weighed against the potential side-effects, patient compliance and preference. Large RCTs with clinically relevant outcomes are required.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.006 | 0.006 |
| 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".