ArticleHaemophilia : the official journal of the World Federation of Hemophilia2021
Performance of a clinical risk prediction model for inhibitor formation in severe haemophilia A.
Article in Haemophilia : the official journal of the World Federation of Hemophilia, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 2 citations in OpenAlex.
- Immunogenic implications of translational readthrough modulate the association of F8 nonsense mutations with inhibitors in Hemophilia A.Molecular medicine (Cambridge, Mass.) · 2026Article
- Shaping hemophilia care: lessons and legacy of the SIPPET trial after 10 years.Research and practice in thrombosis and haemostasis · 2026Review
- Prediction of the chance of successful immune tolerance induction in persons with severe hemophilia A and inhibitors: a clinical prediction model.Research and practice in thrombosis and haemostasis · 2024Article
- Predicting inhibitor development using a random peptide phage-display library approach in the SIPPET cohort.Blood advances · 2024Article
Corrections and comments
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Authors and funding
14 authors at 8 institutions in 5 countries.
Funding
Abstract
backgroundThere is a need to identify patients with haemophilia who have a very low or high risk of developing inhibitors. These patients could be candidates for personalized treatment strategies.
aimsThe aim of this study was to externally validate a previously published prediction model for inhibitor development and to develop a new prediction model that incorporates novel predictors.
methodsThe population consisted of 251 previously untreated or minimally treated patients with severe haemophilia A enrolled in the SIPPET study. The outcome was inhibitor formation. Model discrimination was measured using the C-statistic, and model calibration was assessed with a calibration plot. The new model was internally validated using bootstrap resampling.
resultsFirstly, the previously published prediction model was validated. It consisted of three variables: family history of inhibitor development, F8 gene mutation and intensity of first treatment with factor VIII (FVIII). The C-statistic was 0.53 (95% CI: 0.46-0.60), and calibration was limited. Furthermore, a new prediction model was developed that consisted of four predictors: F8 gene mutation, intensity of first treatment with FVIII, the presence of factor VIII non-neutralizing antibodies before treatment initiation and lastly FVIII product type (recombinant vs. plasma-derived). The C-statistic was 0.66 (95 CI: 0.57-0.75), and calibration was moderate. Using a model cut-off point of 10%, positive- and negative predictive values were 0.22 and 0.95, respectively.
conclusionPerformance of all prediction models was limited. However, the new model with all predictors may be useful for identifying a small number of patients with a low risk of inhibitor formation.
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