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Enregistrement W2899858024 · doi:10.1111/bjh.15657

The incidence of symptomatic osteonecrosis after allogeneic haematopoietic stem cell transplantation in children with acute lymphoblastic leukaemia – controversy on dexamethasone as a risk factor

2018· letter· en· W2899858024 sur OpenAlexaff
Reo Tanoshima, Bruce Carleton

Notice bibliographique

RevueBritish Journal of Haematology · 2018
Typeletter
Langueen
DomaineMedicine
ThématiqueAcute Lymphoblastic Leukemia research
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésMedicinePrednisoneCumulative incidenceIncidence (geometry)DexamethasoneHematopoietic stem cell transplantationTransplantationPopulationInternal medicineHematologyRisk factorDiseasePediatricsImmunology

Résumé

récupéré en direct d'OpenAlex

In a recent article in the British Journal of Haematology, Kuhlen et al (2018) described the incidence and risk factors of symptomatic osteonecrosis (ON) after allogeneic haematopoietic stem cell transplantation (HSCT) in children with high-risk acute lymphoblastic leukaemia (ALL). This study identified a low 5-year cumulative incidence of ON of 9%, and that older age at HSCT (10–15 years), diagnosis of ON before HSCT, and the presence of chronic graft-versus-host-disease (cGVHD) are risk factors of ON after HSCT. We read this article with thorough interest. We believe it is also of value to readers to understand the contribution of the types of corticosteroids to the development of ON (especially dexamethasone versus prednisone), which was not discussed in the article. The prevalence of ON in the paediatric patients after HSCT varied (3·9–29·5%), and this is partly attributable to the heterogeneity of the study population, comprising different diseases and highly variable transplant approaches (Kuhlen et al, 2018). We think it is feasible to consider additional risk factors beyond those that the authors analysed, such as age, pre-existing ON before HSCT and cGVHD, to include the types and doses of corticosteroids before and after HSCT. Corticosteroids are one of the essential medications used in the induction phase for ALL (Inaba & Pui, 2010), and resistance to initial corticosteroid treatment is a single unfavourable prognostic factor of ALL. Prednisone has been the most commonly used corticosteroid in the treatment of patients with ALL, but dexamethasone has been increasingly used for a decade, with the aim of increasing corticosteroid potency (Inaba & Pui, 2010). A major concern of using dexamethasone for ALL treatment is its high toxic potency, and studies have compared the prevalence of adverse drug reactions by corticosteroid types. Dexamethasone use has been shown to have a higher risk of ON than with other corticosteroids (Inaba & Pui, 2010). However, the impact of dexamethasone on the development of ON compared with other corticosteroids in ALL treatment remains controversial. Studies conducted at the Dana-Farber Cancer Institute showed the 5-year cumulative prevalence of bone morbidity was increased in a childhood ALL protocol with dexamethasone compared to that with prednisone (36% vs. 20%) (Silverman et al, 2001; Strauss et al, 2001). The Japanese Children's Cancer and Leukaemia Study Group demonstrated the 5-year incidence of ON was higher in the protocol with dexamethasone (ALL2004: 3·6%) than in those with only prednisolone (ALL941: 0·76% and ALL2000: 0·35%) (Hyakuna et al, 2014). In the Childhood Cancer Survivor Study of 9261 childhood cancer survivors, the prevalence of ON was significantly higher in the patients with dexamethasone with or without prednisone than in the patients with prednisone alone (Kadan-Lottick et al, 2008). On the other hand, a recent randomized trial of dexamethasone versus prednisone or prednisolone for paediatric ALL failed to prove the increased risk of dexamethasone. (Mitchell et al, 2005; Möricke et al, 2016). The UK Medical Research Council protocol for childhood ALL (ALL 97/99) found no excessive risk of ON in the dexamethasone arm compared to prednisolone arm (relative risk: 0·67, 95% confidence interval: 0·24–1·88) (Mitchell et al, 2005). The five-year cumulative incidence of ON in a randomized trial of the Associazione Italiana di Ematologia e Oncologia Pediatrica - Berlin-Frankfürt-Münster (AIEOP-BFM) ALL2000 protocol did not demonstrate a difference between the dexamethasone and prednisone groups (4·6% vs. 5·1%, P = 0·69) (Möricke et al, 2016). Finally, a systematic review and meta-analysis published in 2011 concluded there was no significant difference in ON between dexamethasone and prednisone use in induction therapy (risk ratio: 1·11, 95% confidence interval: 0·82–1·50) (Teuffel et al, 2011). The controversy of the risk of ON with dexamethasone might be explained by the heterogeneity of population demographics, treatment phases of using dexamethasone and doses of corticosteroids used. Other biomarkers, such as genetic variants, might also be attributed to ON (Karol et al, 2015). The risk factors of ON in patients after HSCT are also controversial, owing to the diverse indications of HSCT, differences in donor type, conditioning regimens, the presence of cGVHD and the subsequent use of corticosteroids (Kuhlen et al, 2018). The duration and doses of the different corticosteroids used in the study by Kuhlen et al (2018) would help to inform the current evidence base on this important adverse drug reaction. The authors have no competing interests to declare. RT: contributed to the concept of the work and wrote the initial draft. BC: contributed to the concept of the work, critically reviewed the draft and approved the final version of the manuscript.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Étude de cas · Signal consensuel: Étude de cas
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,184
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,006
Tête enseignante GPT0,240
Écart entre enseignants0,234 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeÉtude de cas
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2018
Routes d'admission1
Résumé présentoui

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