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Application of machine learning approaches for predicting hemophilia A severity.
Rawal, Atul; Kidchob, Christopher; Ou, Jiayi; Sauna, Zuben E.
Afiliación
  • Rawal A; Hemostasis Branch, Division of Plasma Protein Therapeutics, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA.
  • Kidchob C; Hemostasis Branch, Division of Plasma Protein Therapeutics, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA.
  • Ou J; Hemostasis Branch, Division of Plasma Protein Therapeutics, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA.
  • Sauna ZE; Hemostasis Branch, Division of Plasma Protein Therapeutics, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA. Electronic address: Zuben.Sauna@fda.hhs.gov.
J Thromb Haemost ; 22(7): 1909-1918, 2024 Jul.
Article en En | MEDLINE | ID: mdl-38718927
ABSTRACT

BACKGROUND:

Hemophilia A (HA) is an X-linked congenital bleeding disorder, which leads to deficiency of clotting factor (F) VIII. It mostly affects males, and females are considered carriers. However, it is now recognized that variants of F8 in females can result in HA. Nonetheless, most females go undiagnosed and untreated for HA, and their bleeding complications are attributed to other causes. Predicting the severity of HA for female patients can provide valuable insights for treating the conditions associated with the disease, such as heavy bleeding.

OBJECTIVES:

To predict the severity of HA based on F8 genotype using a machine learning (ML) approach.

METHODS:

Using multiple datasets of variants in the F8 and disease severity from various repositories, we derived the sequence for the FVIII protein. Using the derived sequences, we used ML models to predict the severity of HA in female patients.

RESULTS:

Utilizing different classification models, we highlight the validity of the datasets and our approach with predictive F1 scores of 0.88, 0.99, 0.93, 0.99, and 0.90 for all the validation sets.

CONCLUSION:

Although with some limitations, ML-based approaches demonstrated the successful prediction of disease severity in female HA patients based on variants in the F8. This study confirms previous research findings that ML can help predict the severity of hemophilia. These results can be valuable for future studies in achieving better treatment and clinical outcomes for female patients with HA, which is an urgent unmet need.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Índice de Severidad de la Enfermedad / Factor VIII / Aprendizaje Automático / Hemofilia A Límite: Female / Humans / Male Idioma: En Revista: J Thromb Haemost Asunto de la revista: HEMATOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Índice de Severidad de la Enfermedad / Factor VIII / Aprendizaje Automático / Hemofilia A Límite: Female / Humans / Male Idioma: En Revista: J Thromb Haemost Asunto de la revista: HEMATOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido