Finding commonalities in rare diseases through the undiagnosed diseases network.
J Am Med Inform Assoc
; 28(8): 1694-1702, 2021 07 30.
Article
em En
| MEDLINE
| ID: mdl-34009343
ABSTRACT
OBJECTIVE:
When studying any specific rare disease, heterogeneity and scarcity of affected individuals has historically hindered investigators from discerning on what to focus to understand and diagnose a disease. New nongenomic methodologies must be developed that identify similarities in seemingly dissimilar conditions. MATERIALS ANDMETHODS:
This observational study analyzes 1042 patients from the Undiagnosed Diseases Network (2015-2019), a multicenter, nationwide research study using phenotypic data annotated by specialized staff using Human Phenotype Ontology terms. We used Louvain community detection to cluster patients linked by Jaccard pairwise similarity and 2 support vector classifier to assign new cases. We further validated the clusters' most representative comorbidities using a national claims database (67 million patients).RESULTS:
Patients were divided into 2 groups those with symptom onset before 18 years of age (n = 810) and at 18 years of age or older (n = 232) (average symptom onset age 10 [interquartile range, 0-14] years). For 810 pediatric patients, we identified 4 statistically significant clusters. Two clusters were characterized by growth disorders, and developmental delay enriched for hypotonia presented a higher likelihood of diagnosis. Support vector classifier showed 0.89 balanced accuracy (0.83 for Human Phenotype Ontology terms only) on test data. DISCUSSIONS To set the framework for future discovery, we chose as our endpoint the successful grouping of patients by phenotypic similarity and provide a classification tool to assign new patients to those clusters.CONCLUSION:
This study shows that despite the scarcity and heterogeneity of patients, we can still find commonalities that can potentially be harnessed to uncover new insights and targets for therapy.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Doenças não Diagnosticadas
Tipo de estudo:
Clinical_trials
/
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
Limite:
Adolescent
/
Adult
/
Child
/
Child, preschool
/
Humans
/
Infant
/
Newborn
Idioma:
En
Revista:
J Am Med Inform Assoc
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Estados Unidos