Factor IX inhibitors in haemophilia B: A report of National Haemophilia Registry in China
Bibliographic record
Abstract
INTRODUCTION: The development of inhibitors against factor FIX (FIX) is the most serious complication of FIX replacement therapy in haemophilia B (HB) patients. Currently, only few cohorts of HB inhibitor patients have been reported worldwide. AIM: This Chinese nationwide study of HB inhibitor patients explored their risk factors for FIX inhibitor development and experience on their management. METHODS: We retrospectively analysed patient characteristics, F9 genotypes, treatment strategies and outcomes of HB inhibitor patients registered to the Chinese National Registry and Patient Organization Registry. RESULTS: Forty-four unique HB inhibitor patients were identified in 4485 unique HB patients registered by year 2021 to the two Registries. Inhibitor diagnosis were usually delayed and the low prevalence (.98%) may suggest some inhibitor patients were not identified. Their median age at inhibitor diagnosis was 7.5 (IQR, 3.0-14.8) years. Most patients (95.5%) had high-titre inhibitors. Allergic/Anaphylactic reactions occurred in 59.1% patients. Large deletions and nonsense mutations were the most common F9 mutation types in our FIX inhibitor patients. Patients with large F9 gene deletions were more likely to develop inhibitors (p = .0002), while those with missense mutations had a low risk (p < .0001). Thirteen (29.5%) patients received immune tolerance induction (ITI) therapy using low-dose prothrombin complex concentrate regimens. Twelve completed ITI with three (25.0%) achieving success. Nephrotic syndrome developed in two (16.7%) patients during ITI. CONCLUSION: This study reports the largest Chinese cohort of HB inhibitor patients. Large deletions were most significantly associated with inhibitor development. Low-dose ITI might be feasible for FIX inhibitor eradication.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".