Prediction of the chance of successful immune tolerance induction in persons with severe hemophilia A and inhibitors: a clinical prediction model
Bibliographic record
Abstract
Background Inhibitor eradication to restore factor (F)VIII efficacy is the treatment goal for persons with severe hemophilia A (HA) and inhibitors. Immune tolerance induction (ITI) is demanding and successful in about 70% of people. Until now, it has remained difficult to quantify the probability of ITI success or failure, complicating the decision to initiate or not initiate ITI. Estimating the individual chance of ITI success allows clinicians, patients, and their families to support shared decision-making. Objectives We aimed to identify clinical predictors of ITI success and to develop a clinical prediction model to estimate the chance of successful ITI in persons with severe HA. Methods This multicenter study included persons with severe HA who received ITI. Clinical data were collected. Successful ITI was defined by a negative inhibitor titer and an adequate response to FVIII concentrates. A multivariable logistic regression model was developed. Model performance and internal validation were performed. Results Of 206 participants with a median age of 19.8 months (IQR, 12.1-38.8) at ITI start, 148 (71.8%) achieved ITI success. Our clinical prediction model included 4 predictors of ITI success: cumulative number of FVIII exposure days at inhibitor development, peak inhibitor titer, ethnicity, and F8 mutation type. The C statistic was 0.801 (95% CI, 0.70-0.87). Conclusion In our study, including 206 people with severe HA and inhibitors, we developed a clinical prediction model to estimate the chance of successful ITI. After future external validation, this clinical prediction model may be useful for informing clinicians and families.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".