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Precision phenotyping of dilated cardiomyopathy using multidimensional data.

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Title: Precision phenotyping of dilated cardiomyopathy using multidimensional data.
Authors: Tayal, U
Verdonschot, JAJ
Hazebroek, MR
Howard, J
Gregson, J
Newsome, S
Gulati, A
Pua, CJ
Halliday, BP
Lota, AS
Buchan, RJ
Whiffin, N
Kanapeckaite, L
Baruah, R
Jarman, JWE
O'Regan, DP
Barton, PJR
Ware, JS
Pennell, DJ
Adriaans, BP
Bekkers, SCAM
Donovan, J
Frenneaux, M
Cooper, LT
Januzzi, JL
Cleland, JGF
Cook, SA
Deo, RC
Heymans, SRB
Prasad, SK
Item Type: Journal Article
Abstract: BACKGROUND: Dilated cardiomyopathy (DCM) is a final common manifestation of heterogenous etiologies. Adverse outcomes highlight the need for disease stratification beyond ejection fraction. OBJECTIVES: The purpose of this study was to identify novel, reproducible subphenotypes of DCM using multiparametric data for improved patient stratification. METHODS: Longitudinal, observational UK-derivation (n = 426; median age 54 years; 67% men) and Dutch-validation (n = 239; median age 56 years; 64% men) cohorts of DCM patients (enrolled 2009-2016) with clinical, genetic, cardiovascular magnetic resonance, and proteomic assessments. Machine learning with profile regression identified novel disease subtypes. Penalized multinomial logistic regression was used for validation. Nested Cox models compared novel groupings to conventional risk measures. Primary composite outcome was cardiovascular death, heart failure, or arrhythmia events (median follow-up 4 years). RESULTS: In total, 3 novel DCM subtypes were identified: profibrotic metabolic, mild nonfibrotic, and biventricular impairment. Prognosis differed between subtypes in both the derivation (P < 0.0001) and validation cohorts. The novel profibrotic metabolic subtype had more diabetes, universal myocardial fibrosis, preserved right ventricular function, and elevated creatinine. For clinical application, 5 variables were sufficient for classification (left and right ventricular end-systolic volumes, left atrial volume, myocardial fibrosis, and creatinine). Adding the novel DCM subtype improved the C-statistic from 0.60 to 0.76. Interleukin-4 receptor-alpha was identified as a novel prognostic biomarker in derivation (HR: 3.6; 95% CI: 1.9-6.5; P = 0.00002) and validation cohorts (HR: 1.94; 95% CI: 1.3-2.8; P = 0.00005). CONCLUSIONS: Three reproducible, mechanistically distinct DCM subtypes were identified using widely available clinical and biological data, adding prognostic value to traditional risk models. They may improve patient selection for novel interventions, thereby enabling precision medicine.
Issue Date: 7-Jun-2022
Date of Acceptance: 21-Mar-2022
URI: http://hdl.handle.net/10044/1/97196
DOI: 10.1016/j.jacc.2022.03.375
ISSN: 0735-1097
Publisher: Elsevier
Start Page: 2219
End Page: 2232
Journal / Book Title: Journal of the American College of Cardiology
Volume: 79
Issue: 22
Copyright Statement: © 2022 THE AUTHORS. PUBLISHED BY ELSEVIER ON BEHALF OF THE AMERICAN COLLEGE OF CARDIOLOGY FO UNDATION. THIS IS AN OPEN ACCESS ARTICLE UNDER THE CC BY LICENSE ( http://creativecommons.org/licenses/by/4.0/ ) .
Sponsor/Funder: FONDATION LEDUCQ
Imperial College Healthcare NHS Trust- BRC Funding
Funder's Grant Number: 16CVD03
RDB02
Keywords: heart
machine learning
proteomics
heart
machine learning
proteomics
Cardiovascular System & Hematology
1102 Cardiorespiratory Medicine and Haematology
1117 Public Health and Health Services
Publication Status: Published
Conference Place: United States
Open Access location: https://www.sciencedirect.com/science/article/pii/S0735109722046861?via%3Dihub
Online Publication Date: 2022-05-30
Appears in Collections:National Heart and Lung Institute



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