| Author | Jardim, Letícia Lemos | |
| Author | Schieber, Tiago A. | |
| Author | Santana, Marcio Portugal | |
| Author | Cerqueira, Mônica Hermida | |
| Author | Lorenzato, Claudia Santos | |
| Author | Franco, Vivian Karla Brognoli | |
| Author | Zuccherato, Luciana Werneck | |
| Author | Santos, Brendon Ayala da Silva | |
| Author | Chaves, Daniel Gonçalves | |
| Author | Ravetti, Martín Gomez | |
| Author | Rezende, Suely Meireles | |
| Access date | 2024-06-25T17:56:15Z | |
| Available date | 2024-06-25T17:56:15Z | |
| Document date | 2024 | |
| Citation | JARDIM, Letícia Lemos et al. Prediction of inhibitor development in previously untreated and minimally treated children with severe and moderately-severe hemophilia A using a machine-learning network. J Thromb Haemost, 2024. doi.org/10.1016/j.jtha.2024.05.017. | |
| URI | https://www.arca.fiocruz.br/handle/icict/64659 | |
| Language | eng | en_US |
| Rights | restricted access | |
| Subject in Portuguese | Fator VIII | pt_BR |
| Subject in Portuguese | Hemophilia A | pt_BR |
| Title | Prediction of inhibitor development in previously untreated and minimally treated children with severe and moderately-severe hemophilia A using a machine-learning network | en_US |
| Type | Preprint | |
| Abstract | Background: Prediction of inhibitor development in patients with hemophilia A (HA) remains a challenge. Objectives: To construct a predictive model for inhibitor development in HA using a network of clinical variables and biomarkers based on the individual similarity network. Methods: Previously untreated and minimally treated children with severe/moderately severe HA, participants of the HEMFIL Cohort Study, were followed up until reaching 75 exposure days (EDs) without inhibitor (INH-) or upon inhibitor development (INH+). Clinical data and biological samples were collected before the start of factor (F)VIII replacement (T0). A predictive model (HemfilNET) was built to compare the networks and potential global topological differences between INH- and INH+ at T0, considering the network robustness. For validation, the "leave-one-out" cross-validation technique was employed. Accuracy, precision, recall, and F1-score were used as evaluation metrics for the machine-learning model. Results: We included 95 children with HA (CHA), of whom 31 (33%) developed inhibitors. The algorithm, featuring 37 variables, identified distinct patterns of networks at T0 for INH+ and INH-. The accuracy of the model was 74.2% for CHA INH+ and 98.4% for INH-. By focusing the analysis on CHA with high-risk F8 mutations for inhibitor development, the accuracy in identifying CHA INH+ increased to 82.1%. Conclusion: Our machine-learning algorithm demonstrated an overall accuracy of 90.5% for predicting inhibitor development in CHA, which further improved when restricting the analysis to CHA with a high-risk F8 genotype. However, our model requires validation in other cohorts. Yet, missing data for some variables hindered more precise predictions. | en_US |
| Affilliation | Fundação Oswaldo Cruz. Instituto René Rachou. Belo Horizonte, MG, Brazil/Department of Clinical Epidemiology. Leiden University Medical Centre. Leiden, the Netherlands | |
| Affilliation | School of Economics. Universidade Federal de Minas Gerais. Belo Horizonte, MG, Brazil | |
| Affilliation | Fundação Hemominas. Belo Horizonte, MG, Brazil | |
| Affilliation | Instituto de Hematologia Arthur de Siqueira Cavalcanti. Rio de Janeiro, RJ, Brazil | |
| Affilliation | Centro de Hematologia e Hemoterapia do Paraná. Curitiba, PR, Brazil | |
| Affilliation | Centro de Hematologia e Hemoterapia de Santa Catarina. Florianópolis, SC, Brazil | |
| Affilliation | Faculty of Medicine. Universidade Federal de Minas Gerais. Belo Horizonte, MG, Brazil | |
| Affilliation | Faculty of Medicine. Universidade Federal de Minas Gerais. Belo Horizonte, MG, Brazil | |
| Affilliation | Fundação Hemominas. Belo Horizonte, MG, Brazil | |
| Affilliation | Universidade Federal de Minas Gerais. Departamento de Ciência da Computação. Belo Horizonte, MG, Brazil | |
| Affilliation | Faculty of Medicine. Universidade Federal de Minas Gerais. Belo Horizonte, MG, Brazil | |
| Subject | factor VIII | en_US |
| Subject | hemophilia A | en_US |
| Subject | inhibitor | en_US |
| Subject | machine-learning | en_US |
| Subject | previously untreated children | en_US |
| Embargo date | 2150-12-31 | |
| xmlui.metadata.dc.subject.ods | 04 Educação de qualidade | |
