Risk Factors for the Development of High-Titer Inhibitors in 260 Children with Severe Hemophilia a Born Between 1990 and 2009: The Remain Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".