Mei-Sing Ong1, Mary P Mullen2, Eric D Austin2, Peter Szolovits2, Marc D Natter2, Alon Geva2, Tianxi Cai2, Sek Won Kong2, Kenneth D Mandl2. 1. From the Computational Health Informatics Program (M.-S.O., M.D.N., A.G., S.W.K., K.D.M.), Department of Cardiology (M.P.M.), Division of Critical Care Medicine, Department of Anesthesiology, Perioperative, and Pain Medicine (A.G.), and Department of Anesthesia (A.G.), Harvard School of Medicine, Boston Children's Hospital, MA; Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN (E.D.A.); Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge (P.S.); Department of Pediatrics, Massachusetts General Hospital, Boston (M.D.N.); and Department of Biostatistics, Harvard School of Public Health, Boston, MA. (T.C.). mei-sing_ong@hms.harvard.edu. 2. From the Computational Health Informatics Program (M.-S.O., M.D.N., A.G., S.W.K., K.D.M.), Department of Cardiology (M.P.M.), Division of Critical Care Medicine, Department of Anesthesiology, Perioperative, and Pain Medicine (A.G.), and Department of Anesthesia (A.G.), Harvard School of Medicine, Boston Children's Hospital, MA; Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN (E.D.A.); Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge (P.S.); Department of Pediatrics, Massachusetts General Hospital, Boston (M.D.N.); and Department of Biostatistics, Harvard School of Public Health, Boston, MA. (T.C.).
Abstract
RATIONALE: Pediatric pulmonary hypertension (PH) is a heterogeneous condition with varying natural history and therapeutic response. Precise classification of PH subtypes is, therefore, crucial for individualizing care. However, gaps remain in our understanding of the spectrum of PH in children. OBJECTIVE: We seek to study the manifestations of PH in children and to assess the feasibility of applying a network-based approach to discern disease subtypes from comorbidity data recorded in longitudinal data sets. METHODS AND RESULTS: A retrospective cohort study comprising 6 943 263 children (<18 years of age) enrolled in a commercial health insurance plan in the United States, between January 2010 and May 2013. A total of 1583 (0.02%) children met the criteria for PH. We identified comorbidities significantly associated with PH compared with the general population of children without PH. A Bayesian comorbidity network was constructed to model the interdependencies of these comorbidities, and network-clustering analysis was applied to derive disease subtypes comprising subgraphs of highly connected comorbid conditions. A total of 186 comorbidities were found to be significantly associated with PH. Network analysis of comorbidity patterns captured most of the major PH subtypes with known pathological basis defined by the World Health Organization and Panama classifications. The analysis further identified many subtypes documented in only a few case studies, including rare subtypes associated with several well-described genetic syndromes. CONCLUSIONS: Application of network science to model comorbidity patterns recorded in longitudinal data sets can facilitate the discovery of disease subtypes. Our analysis relearned established subtypes, thus validating the approach, and identified rare subtypes that are difficult to discern through clinical observations, providing impetus for deeper investigation of the disease subtypes that will enrich current disease classifications.
RATIONALE: Pediatric pulmonary hypertension (PH) is a heterogeneous condition with varying natural history and therapeutic response. Precise classification of PH subtypes is, therefore, crucial for individualizing care. However, gaps remain in our understanding of the spectrum of PH in children. OBJECTIVE: We seek to study the manifestations of PH in children and to assess the feasibility of applying a network-based approach to discern disease subtypes from comorbidity data recorded in longitudinal data sets. METHODS AND RESULTS: A retrospective cohort study comprising 6 943 263 children (<18 years of age) enrolled in a commercial health insurance plan in the United States, between January 2010 and May 2013. A total of 1583 (0.02%) children met the criteria for PH. We identified comorbidities significantly associated with PH compared with the general population of children without PH. A Bayesian comorbidity network was constructed to model the interdependencies of these comorbidities, and network-clustering analysis was applied to derive disease subtypes comprising subgraphs of highly connected comorbid conditions. A total of 186 comorbidities were found to be significantly associated with PH. Network analysis of comorbidity patterns captured most of the major PH subtypes with known pathological basis defined by the World Health Organization and Panama classifications. The analysis further identified many subtypes documented in only a few case studies, including rare subtypes associated with several well-described genetic syndromes. CONCLUSIONS: Application of network science to model comorbidity patterns recorded in longitudinal data sets can facilitate the discovery of disease subtypes. Our analysis relearned established subtypes, thus validating the approach, and identified rare subtypes that are difficult to discern through clinical observations, providing impetus for deeper investigation of the disease subtypes that will enrich current disease classifications.
Authors: Johannes M Douwes; Tilman Humpl; Damien Bonnet; Maurice Beghetti; D Dunbar Ivy; Rolf M F Berger Journal: J Am Coll Cardiol Date: 2016-03-22 Impact factor: 24.094
Authors: Milton Packer; John McMurray; Barry M Massie; Abraham Caspi; Vincent Charlon; Alain Cohen-Solal; Wolfgang Kiowski; William Kostuk; Henry Krum; Barry Levine; Paolo Rizzon; Jordi Soler; Karl Swedberg; Susan Anderson; David L Demets Journal: J Card Fail Date: 2005-02 Impact factor: 5.712
Authors: Hude Quan; Nadia Khan; Brenda R Hemmelgarn; Karen Tu; Guanmin Chen; Norm Campbell; Michael D Hill; William A Ghali; Finlay A McAlister Journal: Hypertension Date: 2009-10-26 Impact factor: 10.190
Authors: Susanne M Bechtold; Robert Dalla Pozza; Axel Becker; Anette Meidert; Christoph Döhlemann; Hans Peter Schwarz Journal: Eur J Pediatr Date: 2004-01-10 Impact factor: 3.183
Authors: Yukiko Kimura; Jennifer E Weiss; Kathryn L Haroldson; Tzielan Lee; Marilynn Punaro; Sheila Oliveira; Egla Rabinovich; Meredith Riebschleger; Jordi Antón; Peter R Blier; Valeria Gerloni; Melissa M Hazen; Elizabeth Kessler; Karen Onel; Murray H Passo; Robert M Rennebohm; Carol A Wallace; Patricia Woo; Nico Wulffraat Journal: Arthritis Care Res (Hoboken) Date: 2013-05 Impact factor: 4.794
Authors: William M Oldham; Rudolf K F Oliveira; Rui-Sheng Wang; Alexander R Opotowsky; David M Rubins; Jon Hainer; Bradley M Wertheim; George A Alba; Gaurav Choudhary; Adrienn Tornyos; Calum A MacRae; Joseph Loscalzo; Jane A Leopold; Aaron B Waxman; Horst Olschewski; Gabor Kovacs; David M Systrom; Bradley A Maron Journal: Circ Res Date: 2018-02-05 Impact factor: 17.367
Authors: Alon Geva; Steven H Abman; Shannon F Manzi; Dunbar D Ivy; Mary P Mullen; John Griffin; Chen Lin; Guergana K Savova; Kenneth D Mandl Journal: J Am Med Inform Assoc Date: 2020-02-01 Impact factor: 4.497
Authors: Jocelyn R Farmer; Mei-Sing Ong; Sara Barmettler; Lael M Yonker; Ramsay Fuleihan; Kathleen E Sullivan; Charlotte Cunningham-Rundles; Jolan E Walter Journal: Front Immunol Date: 2018-01-09 Impact factor: 7.561