Improved survival in red blood cell transfusion dependent patients with primary myelofibrosis (PMF) receiving iron chelation therapy
Bibliographic record
Abstract
Many patients with primary myelofibrosis (PMF) become red blood cell (RBC) transfusion dependent (TD), risking iron overload (IOL). Iron chelation therapy (ICT) may decrease the risk of haemosiderosis associated organ dysfunction, though its benefit in PMF is undefined. To assess the effect of TD and ICT on survival in PMF, we retrospectively reviewed 41 patients. Clinical data were collected from the database and by chart review. The median age at PMF diagnosis was 64 (range 43-86) years. Median white blood cell (WBC) count at diagnosis was 7.6 (range 1.2-70.9) x 10(9)/L; haemoglobin 104 (62-145) G/L; platelets 300 (38-2088) x 10(9)/L. Lille, Strasser, Mayo and International Prognostic System (IPS) scores were: low risk, n = 15, 8, 11, 3; intermediate, n = 15, 19, 9, 16; high, n = 5, 11, 5, 7; respectively. Primary PMF treatment was: supportive care, n = 23; hydroxyurea, n = 10; immunomodulatory, n = 4; splenectomy, n = 2. Sixteen patients were RBC transfusion independent (TI) and 25 TD; of these 10 received ICT for a median of 18.3 (0.1-117) months. Pre-ICT ferritin levels were a median of 2318 (range 263-8400) and at follow up 1571 (1005-3211 microg/L (p = 0.01). In an analysis of TD patients, factors significant for overall survival (OS) were: WBC count at diagnosis (p = 0.002); monocyte count (p = 0.0001); Mayo score (p = 0.05); IPS (p = 0.02); number of RBC units (NRBCU) transfused (p = 0.02) and ICT (p = 0.003). In a multivariate analysis, significant factors were: NRBCU (p = 0.001) and ICT (p = 0.0001). Five year OS for TI, TD-ICT and TD-NO ICT were: 100, 89 and 34%, respectively (p = 0.003). The hazard ratio (HR) for receiving >20 RBCU was 7.6 (95% Confidence Intervals [CI] 1.2-49.3) and for ICT was 0.15 (0.03-0.77). In conclusion, 61% of PMF patients developed RBC-TD which portended inferior OS; however patients receiving ICT had comparatively improved OS, suggesting a clinical benefit. Prospective studies of IOL and the impact of ICT in PMF are warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".