Risk Factors for the Development of High-Titer Inhibitors in 260 Children with Severe Hemophilia a Born Between 1990 and 2009: The Remain Study
Notice bibliographique
Résumé
Abstract The development of anti-FVIII antibodies (i.e., inhibitors) is the major side effect of severe hemophilia A treatment. Inhibitors mainly develop in children during the first 50 exposure days and are classified in low-and high-titer (i.e., peak titer < or > 5 UB/ml). High-titer inhibitors have the major clinical impact. At diagnosis however, the real nature of the antibody is not clear in all patients, since some low-titer inhibitors may progress to high-titer. The determinants of the evolution from low- to high-titer inhibitors are still unclear and the aim of the present study was to investigate potential risk factors associated with the progression from low- to high-titer inhibitors. This study is a follow-up study of the PedNet Registry and includes 260 children with severe hemophilia A and clinically relevant inhibitors, born between 1990 and 2009 and consecutively recruited from 31 hemophilia centers in 16 countries. Clinical and laboratory data were collected from the date of first positive inhibitor test and covered a minimum of 3-years follow-up. Factors potentially associated with progression from low- to high-titer inhibitor development were analyzed using univariate and multivariate logistic regression. F8 mutation type was known in 247 patients (95%), including 202 (82%) null mutations (i.e., large deletions, nonsense mutations and inversions). Positive family history of inhibitors was present in 37 of 99 (37%) with positive family history of hemophilia. At diagnosis 49% (n=127) had low-titer inhibitors, however, upon FVIII re-exposure, 50% of low-titer inhibitors progressed to high-titer and only 25% of patients (n=69) had persistent low-titer inhibitors. Within the first 3 years of follow-up, immune tolerance induction (ITI) was equally implemented in around 80% of low-and high-titer patients but it was started later in children with high-titers (median time to ITI start 4.5 vs 0.3 months; p<0.001) in whom daily regimens and high-dose FVIII were more frequently adopted (89, 67% vs 41, 50% and 98, 74% vs 35, 43%; p=0.01 and <0.001, respectively). Overall high-titer inhibitor development was associated with null F8 mutations (OR 2.8, 95%CI 1.4-5.5) and family history of inhibitors (OR 3.9, 95%CI 1.2-12.6). The progression from low- to high-titer inhibitors during follow up, was associated with the use of high-dose ITI regimens (i.e., >100 IU/kg/day) with an OR of 3.9 (95%CI 1.5-10.0), independent from the effects of F8 mutation type (adjusted OR 3.6, 95%CI 1.4-9.8) and family history of inhibitors (adjusted OR 6.7, 95%CI 1.1-42.6). No difference was found by comparing the use of daily versus non-daily ITI. In conclusion, in a cohort of 260 children with severe hemophilia A and inhibitors, 49% presented with low-titers at diagnosis and 46% of them progressed to high-titers during follow-up. Progression to high-titer inhibitors was associated with the use of high-dose ITI. These results suggest that intensive ITI should be avoided as initial strategy in low-titer inhibitor patients. Disclosures Mancuso: Pfizer: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sobi/Biogen Idec: Consultancy, Speakers Bureau; Novo Nordisk: Consultancy, Speakers Bureau; CSL Behring: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Baxalta: Consultancy, Speakers Bureau; Bayer Healthcare: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kedrion: Consultancy. Fischer:Wyeth/Pfizer: Research Funding; Biogen: Consultancy; NovoNordisk: Consultancy, Research Funding, Speakers Bureau; Pfizer: Consultancy, Speakers Bureau; Biotest Octapharma: Speakers Bureau; CSL Behring: Speakers Bureau; Baxter: Consultancy, Research Funding, Speakers Bureau; Freeline: Consultancy; Bayer: Consultancy, Research Funding, Speakers Bureau. Santagostino:Octapharma: Consultancy; Novo Nordisk: Consultancy; Kedrion: Consultancy; Sobi: Consultancy; Biogen Idec: Consultancy; Roche: Consultancy; Grifols: Consultancy; Pfizer: Consultancy; Baxalta: Consultancy; CSL Behring: Consultancy; Bayer: Consultancy. Escuriola:Baxalta, now part of Shire: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Investigator Clinical Studies, Research Funding; Bayer: Consultancy, Honoraria, Research Funding; Biotest: Consultancy, Honoraria, Research Funding; CSL Behring: Consultancy, Honoraria, Research Funding; Grifols: Consultancy, Honoraria, Research Funding; Octapharma: Consultancy, Honoraria, Research Funding; NovoNordisk: Consultancy, Honoraria, Research Funding. Liesner:BPL: Consultancy, Honoraria, Research Funding; Bayer: Consultancy, Honoraria, Speakers Bureau; Cangene: Research Funding; CSL Behring: Consultancy, Honoraria, Research Funding; Baxalta Innovations GmbH, now a part of Shire: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; SOBI: Consultancy, Honoraria, Research Funding, Speakers Bureau; Octapharma: Consultancy, Honoraria, Research Funding, Speakers Bureau; Biogen: Consultancy, Honoraria, Research Funding; Grifols: Consultancy, Honoraria. Nolan:Sobi: Research Funding; Biogen: Research Funding.
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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 | 0,001 | 0,002 |
| 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 | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».