Medical Complications In Patients With Myelofibrosis By Frequency Of Blood Transfusion and Iron Chelation Therapy
Bibliographic record
Abstract
Abstract Introduction Myelofibrosis (MF) is a chronic myeloid neoplasm in which 75% of patients have anemia and 25% are erythrocyte transfusion-dependent (TD) (Emanuel et. al. JCO 2012). MF patients receiving chronic transfusions are at risk of multiple co-morbid conditions and iron overload may develop with potential for end-organ damage. Iron chelating therapies (ICTs) help eliminate iron surplus by binding with plasma iron to form a non-toxic conjugate that can be safely excreted from the body. The objective of this study was to examine transfusion patterns and incidence of MF-related complications in TD MF patients treated with vs. without ICT from a US managed care perspective. Methods Two commercial claims databases, Truven Health Analytics MarketScan (2000-2012) and PharMetrics Integrated Database (2001-2012) were used to address the study objectives. Patients with ≥2 MF ICD-9 diagnosis codes (238.76, 289.83) ≥30 days apart, ≥18 years at the time of the first observed diagnosis for MF, and ≥6 months of continuous enrollment prior to the first observed evidence of transfusion dependency (index date), defined as ≥3 transfusion events within any 3-month period, were included (modified Gale et al. Criteria). A transfusion event was defined as a unique day when ≥1 procedure code for packed red blood cells, whole blood, or exchange transfusion was recorded. Frequency of transfusions and time from first evidence of TD to initiation of ICT were analyzed. Incidence of MF-related complications was assessed using incidence rates (IR) and compared between TD patients with vs. without ICT using adjusted incidence rate ratios (aIRR), adjusting for baseline comorbidities and complications. Results Characteristics of MF patients receiving ICT: Of the 571 TD MF patients who met the inclusion criteria, 103 (18%) received ICT and 468 (82%) did not. Mean (SD) age was similar between the two groups (with ICT: 67.2 (10.4) vs. without ICT: 66.6 (11.7), p=0.65), but the proportion of men was higher in the ICT group (with ICT: 70.9% vs. without ICT: 58.3%, p=0.02). The mean (SD) observation time was longer for patients with ICT than without ICT (months, 22.2 [13.9] vs. 12.6 [11.6], p<0.001). A greater proportion of TD patients without ICT had a history of essential thrombocythemia (with ICT: 11.7% vs. without ICT: 20.9%), whereas the proportion of patients with prior polycythemia vera was similar between the two groups (with ICT: 11.7% vs. without ICT: 13.7%). Differences in TD MF patients by utilization of ICT: Overall, patients without ICT were generally more ill than those with ICT (Charlson Comorbidity Index [CCI], with ICT: 1.8 vs. without ICT: 2.3, p=0.01). The mean (SD) number of transfusion events per year was similar between the two groups with 22.4 (19.5) events/year in the group with ICT compared to 22.2 (28.5) events/year in the group without ICT (p=0.94). Among patients receiving ICT, therapy was initiated after a median time of 5.6 months following the first evidence of TD. TD patients with ICT had lower rates of thrombocytopenia (aIRR: 0.54; 95% confidence interval [CI]: 0.40-0.74) and pancytopenia (aIRR: 0.53; 95% CI: 0.37-0.76), but higher rates of anemia (aIRR: 1.61; 95% CI: 1.27-2.02). The incidence of other MF-related complications considered was similar between the two groups. Conclusion In this first analysis of the utilization of ICT amongst MF patients whom are TD we identified several important differences between those receiving ICT compared to those who do not. Whether these difference are a result of ICT, or a reflection of whether physician practice leads to ICT utilization in certain subsets of MF patients (i.e. those with less thrombocytopenia/leukopenia) could not be assessed with the current data. Potential short term and long term benefits of ICT in MF need to be validated in prospective clinical trials. Disclosures Vekeman: Novartis Pharmaceuticals: Research Funding. Cheng:Novartis Pharmaceuticals: Research Funding. Sasane:Novartis Pharmaceuticals: Employment. Huynh:Novartis Pharmaceuticals: Research Funding. Kaminsky:Novartis Pharmaceuticals: Research Funding. Duh:Novartis Pharmaceuticals: Research Funding. Paley:Novartis Pharmaceuticals: Employment. Mesa:Novartis Pharmaceuticals: Research Funding; Incyte Corporation: Research Funding; Gilead Sciences: Research Funding; CTI: Research Funding; Celgene: Research Funding; Genentech: Research Funding; NS Pharma: Research Funding; Lilly: Research Funding.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.003 | 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".