A Globally Applicable "Triple AAA" Risk Model for Essential Thrombocythemia Based on Age, Absolute Neutrophil Count, and Absolute Lymphocyte Count
Notice bibliographique
Résumé
Abstract Background The detrimental effect of leukocytosis on survival in myeloproliferative neoplasms (MPN) has been well established for primary myelofibrosis (PMF), polycythemia vera (PV) and essential thrombocythemia (ET ) (JCO. 2018;36:310; BJH. 2020;189:291) Previous studies have also implicated leukocytosis as a risk factor for leukemic transformation (Mayo Clin Proc. 2017;92:1118) and thrombosis in MPN (Blood Adv. 2019;3:1729). However, it is currently not clear as to which component(s) of white blood cells is responsible for these observations. In the current study, we sought to examine the individual prognostic contribution of absolute neutrophil (ANC), lymphocyte (ALC) and monocyte (AMC) counts, on overall (OS), leukemia-free (LFS), and myelofibrosis-free (MFFS) survival and in ET. Methods Study patients (n=349) were retrospectively recruited from the Mayo Clinic MPN database of 1,249 WHO-defined ET patients, evaluated over five decades (1967-2021), based on availability of information on ANC, ALC and AMC. Conventional criteria were used for diagnosis and definitions of major complications, including leukemic or fibrotic transformation (Blood 2016;127:2391). Conventional statistical methods were applied using JMP Pro 14.0.0 software package, SAS Institute, Cary, NC. Multivariable analyses included previously established risk factors for survival. Results 349 patients (median age 57 years, range 18-89; females 61%) with ET were included in the study: 46% JAK2, 34% CALR, 16% triple-negative and 4% MPL mutated; IPSET risk category high 24%, intermediate 41%, and low 35%; presenting median (range) values were 13.8 g/dL (11.1-16.4) for hemoglobin, 8.2 x 10(9)/L (3.2-52) for leukocyte count, and 859 x 10(9)/L (451-3460) for platelet count; palpable splenomegaly was present in 48 (14%); median followup was 10 years (range 0-47), during which time 118 deaths, 52 fibrotic progressions, and 14 leukemic transformations were documented. Multivariable analysis identified older age (p<0.001), increased ANC (p<0.001), decreased ALC (p=0.03), and male sex (p=0.04), but not AMC (p=0.8), venous thrombosis (p=0.4), or arterial thrombosis (p=0.4), as independent risk factors for OS. ANC of ≥8 x 10(9)/L and ALC of <1.8 x 10(9)/L were determined as appropriate cut-off values by ROC analysis. Subsequent multivariable analysis using these cut-off values resulted in HR (95% CI) of 5.2 (3.4-7.9; p<0.001) for age >60 years, 3.1 (2.1-4.6; p<0.001) for ANC, and 2.0 (1.4-3.0; p<0.001) for ALC; male sex was no longer significant in this analysis (p=0.14). An operational HR-based risk score assigned 3 adverse risk points for older age (>60 years), 2 for increased ANC (≥8 x 10(9)/L) and 1 for ALC (<1.8 x 10(9)/L), resulting in an new Age Anc Alc (AAA; triple A) risk model for survival in ET with estimates of median survival ranging from 9.7 to 36.6 years (Figure 1). In addition, ALC <1.8 x 10(9)/L was associated with inferior LFS (p=0.06) and MFFS (p=0.07) while AMC as a continuous variable showed borderline significance for MFFS (p=0.18). In univariate analysis, JAK2V617F allele burden showed significant association with OS (p=0.02), LFS (p=0.05) and MFFS (p=0.001); however significance for OS was lost in mutivariable analysis that included ANC and ALC. An external validation cohort from the University of Florence (n=485) confirmed the independent survival risk contribution from age >60 years (p<0.001; HR 9.9, 95% CI 5.4-18.0), ANC ≥8 x 10(9)/L (p=0.02; 1.9, 1.1-3.5) and ALC <1.8 x 10(9)/L (p<0.001; 2.0, 1.3-3.0). The Triple A risk model was also effectively applied on the Florence validation cohort (Figure 1): median follow-up 8.4 years; 87 deaths; 43 fibrotic progression; 12 leukemic transformations. The association of ALC <1.8 x 10(9)/L (p=0.04) and AMC (p=0.002) with fibrotic transformation was also validated in the Florence cohort. Conclusions: The current study identifies increased ANC and decreased ALC as age-independent risk factors for survival in ET, thus allowing the development of a globally applicable simple to use "Triple A" risk model that is based on Age, ANC and ALC. Decreased ALC also predicted fibrotic and leukemic progression. Our observations suggest potential value for immune profiling as an additional prognostic tool in MPN. Figure 1 Figure 1. Disclosures Szuber: Novartis: Honoraria. Vannucchi: BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Incyte: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Membership on an entity's Board of Directors or advisory committees.
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 | 0,008 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 ».