Finding commonalities in rare diseases through the undiagnosed diseases network. (3rd May 2021)

• Record Type: Journal Article
• Authors: Yates, Josephine; Gutiérrez-Sacristán, Alba; Jouhet, Vianney; LeBlanc, Kimberly; Esteves, Cecilia; DeSain, Thomas N; Benik, Nick; Stedman, Jason; Palmer, Nathan; Mellon, Guillaume; Kohane, Isaac; Avillach, Paul
• Is Part Of: Journal of the American Medical Informatics Association. Volume 28:Number 8(2021)
• Page Start: 1694
• Page End: 1702
• Publication Date: 2021-05-03
• Subjects: rare diseases -- undiagnosed diseases -- cluster analysis -- supervised machine learning -- unsupervised machine learning; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocab050
• Languages: English
• ISSNs: 1067-5027

Abstract

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 and Methods: 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.