Identification of risk features for complication in Gaucher’s disease patients: a machine learning analysis of the Spanish registry of Gaucher disease
Abstract Background Since 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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-09-01
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| Series: | Orphanet Journal of Rare Diseases |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13023-020-01520-7 |
| _version_ | 1828954880464650240 |
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| author | Marcio M. Andrade-Campos Laura López de Frutos Jorge J. Cebolla Irene Serrano-Gonzalo Blanca Medrano-Engay Mercedes Roca-Espiau Beatriz Gomez-Barrera Jorge Pérez-Heredia David Iniguez Pilar Giraldo |
| author_facet | Marcio M. Andrade-Campos Laura López de Frutos Jorge J. Cebolla Irene Serrano-Gonzalo Blanca Medrano-Engay Mercedes Roca-Espiau Beatriz Gomez-Barrera Jorge Pérez-Heredia David Iniguez Pilar Giraldo |
| author_sort | Marcio M. Andrade-Campos |
| collection | DOAJ |
| description | Abstract Background Since 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. Results A 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. Conclusions Our 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. |
| first_indexed | 2024-12-14T07:37:46Z |
| format | Article |
| id | doaj.art-29234a24352744b8afb0e10a2ead564d |
| institution | Directory Open Access Journal |
| issn | 1750-1172 |
| language | English |
| last_indexed | 2024-12-14T07:37:46Z |
| publishDate | 2020-09-01 |
| publisher | BMC |
| record_format | Article |
| series | Orphanet Journal of Rare Diseases |
| spelling | doaj.art-29234a24352744b8afb0e10a2ead564d2022-12-21T23:11:07ZengBMCOrphanet Journal of Rare Diseases1750-11722020-09-0115111110.1186/s13023-020-01520-7Identification of risk features for complication in Gaucher’s disease patients: a machine learning analysis of the Spanish registry of Gaucher diseaseMarcio M. Andrade-Campos0Laura López de Frutos1Jorge J. Cebolla2Irene Serrano-Gonzalo3Blanca Medrano-Engay4Mercedes Roca-Espiau5Beatriz Gomez-Barrera6Jorge Pérez-Heredia7David Iniguez8Pilar Giraldo9Grupo Español de Enfermedades de Depósito Lisosomal, Sociedad Española de Hematología y Hemoterapia, (GEEDL)Grupo Español de Enfermedades de Depósito Lisosomal, Sociedad Española de Hematología y Hemoterapia, (GEEDL)Grupo de Investigación en Enfermedades Metabólicas y Hematológicas Raras (GIIS-012), Instituto Investigación Sanitaria AragónFundación Española para el Estudio y Terapéutica de la Enfermedad de Gaucher y otras lisosomales (FEETEG)Fundación Española para el Estudio y Terapéutica de la Enfermedad de Gaucher y otras lisosomales (FEETEG)Fundación Española para el Estudio y Terapéutica de la Enfermedad de Gaucher y otras lisosomales (FEETEG)Kampal Solutions, Universidad de ZaragozaInstituto de Biocomputación y Física de Sistemas Complejos (BIFI)Kampal Solutions, Universidad de ZaragozaGrupo Español de Enfermedades de Depósito Lisosomal, Sociedad Española de Hematología y Hemoterapia, (GEEDL)Abstract Background Since 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. Results A 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. Conclusions Our 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.http://link.springer.com/article/10.1186/s13023-020-01520-7Gaucher diseaseMachine learningBone crisisNeoplasiaERT |
| spellingShingle | Marcio M. Andrade-Campos Laura López de Frutos Jorge J. Cebolla Irene Serrano-Gonzalo Blanca Medrano-Engay Mercedes Roca-Espiau Beatriz Gomez-Barrera Jorge Pérez-Heredia David Iniguez Pilar Giraldo 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 Gaucher disease Machine learning Bone crisis Neoplasia ERT |
| title | Identification of risk features for complication in Gaucher’s disease patients: a machine learning analysis of the Spanish registry of Gaucher disease |
| title_full | Identification of risk features for complication in Gaucher’s disease patients: a machine learning analysis of the Spanish registry of Gaucher disease |
| title_fullStr | Identification of risk features for complication in Gaucher’s disease patients: a machine learning analysis of the Spanish registry of Gaucher disease |
| title_full_unstemmed | Identification of risk features for complication in Gaucher’s disease patients: a machine learning analysis of the Spanish registry of Gaucher disease |
| title_short | Identification of risk features for complication in Gaucher’s disease patients: a machine learning analysis of the Spanish registry of Gaucher disease |
| title_sort | identification of risk features for complication in gaucher s disease patients a machine learning analysis of the spanish registry of gaucher disease |
| topic | Gaucher disease Machine learning Bone crisis Neoplasia ERT |
| url | http://link.springer.com/article/10.1186/s13023-020-01520-7 |
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