Medical Complications In Patients With Myelofibrosis By Frequency Of Blood Transfusion and Iron Chelation Therapy
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».