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Risk Factors for the Development of High-Titer Inhibitors in 260 Children with Severe Hemophilia a Born Between 1990 and 2009: The Remain Study

2016· article· en· W2979701566 on OpenAlexaff
Maria Elisa Mancuso, Kathelijn Fischer, Elena Santagostino, Johannes Oldenburg, Helen Platokouki, Christoph Königs, Carmen Escuriola, Georges‐Étienne Rivard, Ana Rosa Cid, Manuel Carção, Rolf Ljung, Pia Petrini, Anne Rafowicz, Carmen Altisent, Gili Kenet, Raina Liesner, Karin Kurnik, Chris Van Geet, Teresa Álvarez-Roman, Tin Tin Yee, G. Auerswald, Rosario Pérez‐Garrido, Beatrice Nolan, Hérvè Chambost, Anne Mäkipernaa, Angelo Claudio Molinari, Angela Thomas, Elizabeth Chalmers, Mike Williams, Rainer Kobelt, Marijke van den Berg

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsHospital for Sick ChildrenInstitute for Clinical Evaluative SciencesSickKids FoundationCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsTiterMedicineInternal medicineUnivariate analysisImmunologyLogistic regressionConcomitantAntibodyMultivariate analysis

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.267
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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