Xia Tang1, Wen Chen2, Ziyi Zeng2, Keyue Ding3, Zhou Zhou4. 1. Medical Genetic Institute of Henan Province, Henan Provincial People's Hospital, Henan Key Laboratory of Genetic Diseases and Functional Genomics, Henan Provincial People's Hospital of Henan University, People's Hospital of Zhengzhou University, Zhengzhou, Henan Province 450003, China; NHC Key Laboratory of Birth Defect Prevention, Zhengzhou, Henan Province 450003, China. 2. Department of Laboratory Medicine, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China. 3. Medical Genetic Institute of Henan Province, Henan Provincial People's Hospital, Henan Key Laboratory of Genetic Diseases and Functional Genomics, Henan Provincial People's Hospital of Henan University, People's Hospital of Zhengzhou University, Zhengzhou, Henan Province 450003, China; NHC Key Laboratory of Birth Defect Prevention, Zhengzhou, Henan Province 450003, China. Electronic address: ding.keyue@gmail.com. 4. Department of Laboratory Medicine, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China. Electronic address: zhouzhou@fuwaihospital.org.
Abstract
BACKGROUND: Ebstein's anomaly (EA) is a rare congenital heart disease with significantly phenotypic heterogeneity, accompanied with multiple associated phenotypes. The classification of cases with EA based on a standardized vocabulary of phenotypic abnormalities from Human Phenotype Ontology (HPO) and its association with adverse clinical outcomes has yet to be investigated. METHODS: We developed a deep phenotyping algorithm for Chinese electronic medical records (EMRs) from the Fuwai Hospital to ascertain EA cases. EA-associated phenotypes were standardized according to HPO annotation, and an unsupervised hierarchical cluster analysis was used to classify EA cases according to their phenotypic similarities. A survival analysis was conducted to study the association of the HPO-based cluster with survival or adverse clinical outcomes. RESULTS: The ascertained EA cases were annotated to have a single or multiple HPO terms. Three distinct clusters with different combinations of HPO term in these cases were identified. The HPO-based classification of EA cases was not significantly associated with survival or adverse clinical outcomes at a mid-term follow-up. CONCLUSIONS: Our study provided an important implication for studying the classification of congenital heart disease using HPO-based annotation. A long time follow-up will enable to confirm its association with adverse clinical outcomes.
BACKGROUND:Ebstein's anomaly (EA) is a rare congenital heart disease with significantly phenotypic heterogeneity, accompanied with multiple associated phenotypes. The classification of cases with EA based on a standardized vocabulary of phenotypic abnormalities from Human Phenotype Ontology (HPO) and its association with adverse clinical outcomes has yet to be investigated. METHODS: We developed a deep phenotyping algorithm for Chinese electronic medical records (EMRs) from the Fuwai Hospital to ascertain EA cases. EA-associated phenotypes were standardized according to HPO annotation, and an unsupervised hierarchical cluster analysis was used to classify EA cases according to their phenotypic similarities. A survival analysis was conducted to study the association of the HPO-based cluster with survival or adverse clinical outcomes. RESULTS: The ascertained EA cases were annotated to have a single or multiple HPO terms. Three distinct clusters with different combinations of HPO term in these cases were identified. The HPO-based classification of EA cases was not significantly associated with survival or adverse clinical outcomes at a mid-term follow-up. CONCLUSIONS: Our study provided an important implication for studying the classification of congenital heart disease using HPO-based annotation. A long time follow-up will enable to confirm its association with adverse clinical outcomes.
Authors: Sebastian Köhler; Michael Gargano; Nicolas Matentzoglu; Leigh C Carmody; David Lewis-Smith; Nicole A Vasilevsky; Daniel Danis; Ganna Balagura; Gareth Baynam; Amy M Brower; Tiffany J Callahan; Christopher G Chute; Johanna L Est; Peter D Galer; Shiva Ganesan; Matthias Griese; Matthias Haimel; Julia Pazmandi; Marc Hanauer; Nomi L Harris; Michael J Hartnett; Maximilian Hastreiter; Fabian Hauck; Yongqun He; Tim Jeske; Hugh Kearney; Gerhard Kindle; Christoph Klein; Katrin Knoflach; Roland Krause; David Lagorce; Julie A McMurry; Jillian A Miller; Monica C Munoz-Torres; Rebecca L Peters; Christina K Rapp; Ana M Rath; Shahmir A Rind; Avi Z Rosenberg; Michael M Segal; Markus G Seidel; Damian Smedley; Tomer Talmy; Yarlalu Thomas; Samuel A Wiafe; Julie Xian; Zafer Yüksel; Ingo Helbig; Christopher J Mungall; Melissa A Haendel; Peter N Robinson Journal: Nucleic Acids Res Date: 2021-01-08 Impact factor: 16.971