Age and ABO Blood Group Are Significant Predictors of Thrombosis in Children with Acute Lymphoblastic Leukemia (ALL): Results from Dana-Farber Cancer Institute (DFCI) ALL Consortium Trial 05-001
Notice bibliographique
Résumé
Abstract Background Thromboembolism (TE) is a serious complication in pediatric patients with ALL associated with significant morbidity, mortality and therapy alterations. Thromboprophylaxis, though effective in preventing TE in patients at high risk for TE, is associated with increased risk of bleeding. Hence it is important to identify the population at the highest risk for TE for whom preventive strategies may be warranted. However, predictors of TE in pediatric patients with ALL remain uncertain and the role of inherited thrombophilia is ill defined. Adult studies of non-cancer patients and our prior smaller pilot study of pediatric ALL patients showed higher rates of TE with non-O blood types. Aim To identify variables at the time of ALL diagnosis that are associated with increased risk of TE in children and adolescents treated on DFCI ALL 05-001 trial. Design/Methods Eligible and consenting patients (1 to ≤18 yrs. of age) with newly diagnosedALL were enrolled on the DFCI 05-001 trial between 2005-2011 at 11 participating institutions in the USA and Canada. Risk categorization and protocol therapy have previously been described (Lancet Oncol 2015;16:1677-90). Treatment included one dose of pegaspargase (2500 IU/m2) during remission induction and 30 consecutive weeks of L-asparaginase during post-induction therapy (either pegaspargase 2500 IU/m2 every 2-weeks or native E.coli L-asparaginase 25000 IU/m2 weekly). Baseline clinical and laboratory data as well as all TE events requiring intervention (Grade 2 or higher) were prospectively collected. Genomic DNA was isolated from peripheral blood or bone marrow obtained at the time of complete remission in participants who consented for the collection of research samples. Single nucleotide polymorphisms (SNP) were detected using PCR-based allelic discrimination assays for prothrombin (PT)gene G20210A (rs1799963) and Factor V G1691A (rs6025) (Factor V Leiden, FVL) (Applied Biosystems, Carlsbad CA). The cumulative incidence of TE was estimated with induction failure, relapse and death considered as competing events. Effect of clinical [age, gender, body mass index (BMI) ALL immunophenotype, risk-group] and laboratory variables [presenting leukocyte count, blood group, SNPs for PT and FVL] at ALL diagnosis on the cumulative incidence of TE was evaluated in univariate and multivariable competing risk regression models. The impact of TE on ALL outcome (event free and overall survival) was further explored using a time-varying covariate in Cox regression modelling. Results Of 794 enrolled patients [median age 4.97 (range 1.04 -17.96) yrs.; males 441], 100 developed TE; the 25-month cumulative incidence was 13.0% (95%CI 10.7,15.5). Twenty-one patients had 21 events during induction (3 with CNS TE) and 79 patients had 88 events post-induction (16 CNS TE). Table 1 provides the cumulative incidence of the time to first event of TE by patient characteristics and is compared using Gray test. Sex, BMI and thrombophilia SNPs had no detectable impact on the incidence of TE. There was no difference in incidence of TE by asparaginase type (data not shown). In the final competing risk multivariable regression model older age group and non-O blood group were each significantly associated with TE (Table 2). Figure 1 depicts the effect of blood group type on the incidence of TE within each age group; the cumulative incidence of TE was significantly higher for non-O blood group only for patients Conclusion Older age and non-O blood group are significant predictors of TE during ALL therapy. The effect of non-O blood group is more pronounced in younger age patients. Polymorphisms of FVL and PT gene had no impact on the risk of TE. We recommend further evaluation of these risk factors and recommend thromboprophylaxis for pediatric patients with ALL over 10 years of age (and especially those 15 years or older) receiving asparaginase-intensive regimens, and consideration of thromboprophylaxis in younger patients with non-O blood group. Download : Download high-res image (161KB) Download : Download full-size image Disclosures Neuberg: Synta Pharmaceuticals: Other: Stock shares.
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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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».