Very Elderly Patients with Myeloproliferative Neoplasms: Phenotypic Distinctions and Prognostic Determinants of Complications and Mortality
Notice bibliographique
Résumé
Abstract Background and methods: Increasing life expectancy has had the impact of increasing the proportion of patients with myeloproliferative neoplasms (MPN) aged ≥75 years (very elderly patients; VEP). However, few studies have evaluated the phenotype and prognostic factors specific to this population. This is a retrospective multicenter chart review (11 Quebec centers) of VEP with polycythemia vera (PV), essential thrombocytosis (ET), and myelofibrosis (MF) diagnosed between 1978 and 2019, enrolled in the CML-MPN Quebec Research Group registry. All diagnoses were made according to World Health Organization 2016 criteria (Blood. 2016;127:2391), with exemption of bone marrow biopsies in select patients. Standard statistical methods were used for all analyses. Results: Of the 753 patients studied in the registry, 114 patients (15%) were ≥75 years old (VEP) (Table 1). These subjects had a median age of 79 years (range 75-95) with incidence of PV, ET and MF of 38%, 51% and 11% respectively. Compared to patients <75 years old, VEP were less likely to be male (35% vs 45%, p = 0.05), had more frequent and higher levels of leukocytosis (11.4 vs 9.7 x 10 9 / L, p <0.0001), were more frequently "triple negative" and less commonly CALR-mutated (p = 0.0005), and presented significantly higher allelic burdens (45% vs 36%, p = 0.03). VEP also had more cardiovascular comorbidities (p <0.0001) and were more often classified as "high risk" (86% vs. 43%, <0.0001). A history of arterial thrombosis prior to/at diagnosis was more common in VEP vs younger patients (14% vs 7.2%, p = 0.02), as was a global history of arterial and/or venous thrombosis at any time during the follow up (25% vs 18%, p = 0.05). Sites of venous events were specifically skewed towards lower extremity deep vein thrombosis in VEP (75%) vs variously distributed in younger patients (p = 0.05). Bleeding rates were similar among the two groups (16% VEP and 17% non-VEP, p = 0.7) while fewer fibrotic (3% vs 8%, p = 0.03) and leukemic transformations (0 vs 2%, p = 0.04) were recorded in VEP. Significantly fewer bone marrow biopsies were performed in VEP (41% versus 54%, p = 0.01). This cohort was, however, more likely to receive cytoreductive therapy (76% vs 67%, p = 0.05), predominantly hydroxyurea (70% vs 59%, p = 0.02). Multivariate analysis revealed the presence of splenomegaly (HR 4.5; 1.2-17.1, p = 0.03) and smoking status (active/former vs never smokers) (HR 5.2; 1.1-24.9, p = 0.03) as predictors of shortened overall survival for the VEP population (Table 2). It also disclosed higher leukocyte count (p = 0.005) and the presence of diabetes mellitus (p = 0.05) as significant risk factors for shortened hemorrhage-free survival. Platelet count (p = 0.03) and smoking status (p = 0.02) were found to be significant determinants of thrombosis-free survival in univariate analysis but did not maintain their significance in multivariate testing. Kaplan-Meier survival data examining age-stratified outcomes in VEP vs younger patients revealed significantly shorter overall survival in VEP (14.2 years vs not reached, p < 0.0001) (Figure 1). The VEP cohort also displayed significantly reduced arterial thrombosis-free survival (incidence of 6.1% vs 3.9%, p = 0.01). There was no significant difference in event data for venous thrombosis-free (4% in both, p = 0.2) and myelofibrosis-free (2.6% versus 7.8%, p = 0.6) survival. Conclusion: This data addressing VEP with MPN exposes, for the first time: i) a characteristic phenotype (predominantly female, higher leukocyte counts, higher allele burden), ii) distinct adverse outcome patterns, particularly with regard to arterial thrombosis, and iii) unique and independent prognostic factors for survival, suggesting that VEP with MPN constitute a biologically, phenotypically, and prognostically distinct population. Figure 1 Figure 1. Disclosures Busque: Novartis: Consultancy. Szuber: Novartis: Honoraria.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,001 | 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 ».