Identification of risk features for complication in Gaucher’s disease patients: a machine learning analysis of the Spanish registry of Gaucher disease

Orphanet Journal of Rare Diseases

Mercedes Roca-Espiau¹  Irene Serrano-Gonzalo²  Blanca Medrano-Engay²  Laura López de Frutos³  Pilar Giraldo³  Marcio M. Andrade-Campos⁴  Jorge J. Cebolla⁵  Jorge Pérez-Heredia⁶  Beatriz Gomez-Barrera⁷  David Iniguez⁸ 

[1] Fundación Española para el Estudio y Terapéutica de la Enfermedad de Gaucher y otras lisosomales (FEETEG), Zaragoza, Spain;Centro de Imagen. Vivo, Zaragoza, Spain;Fundación Española para el Estudio y Terapéutica de la Enfermedad de Gaucher y otras lisosomales (FEETEG), Zaragoza, Spain;Grupo de Investigación en Enfermedades Metabólicas y Hematológicas Raras (GIIS-012), Instituto Investigación Sanitaria Aragón, Zaragoza, Spain;Grupo Español de Enfermedades de Depósito Lisosomal, Sociedad Española de Hematología y Hemoterapia, (GEEDL), Zaragoza, Spain;Fundación Española para el Estudio y Terapéutica de la Enfermedad de Gaucher y otras lisosomales (FEETEG), Zaragoza, Spain;Grupo de Investigación en Enfermedades Metabólicas y Hematológicas Raras (GIIS-012), Instituto Investigación Sanitaria Aragón, Zaragoza, Spain;Grupo Español de Enfermedades de Depósito Lisosomal, Sociedad Española de Hematología y Hemoterapia, (GEEDL), Zaragoza, Spain;Hospital del Mar Institut Hospital del Mar d’Investigacions Mèdiques, Barcelona, Spain;Fundación Española para el Estudio y Terapéutica de la Enfermedad de Gaucher y otras lisosomales (FEETEG), Zaragoza, Spain;Grupo de Investigación en Enfermedades Metabólicas y Hematológicas Raras (GIIS-012), Instituto Investigación Sanitaria Aragón, Zaragoza, Spain;Departamento de Bioquímica, Biología Molecular y Celular, Universidad de Zaragoza, Zaragoza, Spain;Instituto de Biocomputación y Física de Sistemas Complejos (BIFI), Zaragoza, Spain;Kampal Solutions, Universidad de Zaragoza, Zaragoza, Spain;Kampal Solutions, Universidad de Zaragoza, Zaragoza, Spain;Instituto de Biocomputación y Física de Sistemas Complejos (BIFI), Zaragoza, Spain;

关键词: Gaucher disease;    Machine learning;    Bone crisis;    Neoplasia;    ERT;   

DOI  :  10.1186/s13023-020-01520-7

来源: Springer

【 摘 要 】

BackgroundSince enzyme replacement therapy for Gaucher disease (MIM#230800) has become available, both awareness of and the natural history of the disease have changed. However, there remain unmet needs such as the identification of patients at risk of developing bone crisis during therapy and late complications such as cancer or parkinsonism. The Spanish Gaucher Disease Registry has worked since 1993 to compile demographic, clinical, genetic, analytical, imaging and follow-up data from more than 400 patients. The aims of this study were to discover correlations between patients’ characteristics at diagnosis and to identify risk features for the development of late complications; for this a machine learning approach involving correlation networks and decision trees analyses was applied.ResultsA total of 358 patients, 340 type 1 Gaucher disease and 18 type 3 cases were selected. 18% were splenectomyzed and 39% had advanced bone disease. 81% of cases carried heterozygous genotype. 47% of them were diagnosed before the year 2000. Mean age at diagnosis and therapy were 28 and 31.5 years old (y.o.) respectively. 4% developed monoclonal gammopathy undetermined significance or Parkinson Disease, 6% cancer, and 10% died before this study. Previous splenectomy correlates with the development of skeletal complications and severe bone disease (p = 0.005); serum levels of IgA, delayed age at start therapy (> 9.5 y.o. since diagnosis) also correlates with severe bone disease at diagnosis and with the incidence of bone crisis during therapy. High IgG (> 1750 mg/dL) levels and age over 60 y.o. at diagnosis were found to be related with the development of cancer. When modelling the decision tree, patients with a delayed diagnosis and therapy were the most severe and with higher risk of complications.ConclusionsOur work confirms previous observations, highlights the importance of early diagnosis and therapy and identifies new risk features such as high IgA and IgG levels for long-term complications.

【 授权许可】

CC BY