Prediction of inhibitor development in previously untreated and minimally treated children with severe and moderately severe hemophilia A using a machine-learning network.

Autor:	Jardim LL; Instituto René Rachou (Fiocruz Minas), Belo Horizonte, Minas Gerais, Brazil; Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, the Netherlands., Schieber TA; Faculdade de Ciências Econômicas, School of Economics, Universidade Federal de Minas Gerais, Brazil., Santana MP; Fundação Hemominas, Belo Horizonte, Minas Gerais, Brazil., Cerqueira MH; Instituto de Hematologia Arthur de Siqueira Cavalcanti, Rio de Janeiro, Brazil., Lorenzato CS; Centro de Hematologia e Hemoterapia do Paraná, Curitiba, Brazil., Franco VKB; Centro de Hematologia e Hemoterapia de Santa Catarina, Florianópolis, Brazil., Zuccherato LW; Faculty of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil., da Silva Santos BA; Faculty of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil., Chaves DG; Fundação Hemominas, Belo Horizonte, Minas Gerais, Brazil., Ravetti MG; Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil., Rezende SM; Faculty of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil. Electronic address: suely.rezende@uol.com.br.
Jazyk:	angličtina
Zdroj:	Journal of thrombosis and haemostasis : JTH [J Thromb Haemost] 2024 Sep; Vol. 22 (9), pp. 2426-2437. Date of Electronic Publication: 2024 May 27.
DOI:	10.1016/j.jtha.2024.05.017
Abstrakt:	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. Competing Interests: Declaration of competing interests The authors state that they have no conflict of interest. (Copyright © 2024 International Society on Thrombosis and Haemostasis. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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