Precision Phenotyping of Dilated Cardiomyopathy Using Multidimensional Data.

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

SUBMITTER: Tayal U 

PROVIDER: S-EPMC9168440 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature