Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-17T21:26:12.009Z Has data issue: false hasContentIssue false

42855 A Phenomics Approach to the Categorization and Refinement of Heart Failure

Published online by Cambridge University Press:  30 March 2021

Nosheen Reza
Affiliation:
Perelman School of Medicine at the University of Pennsylvania
William Bone
Affiliation:
Perelman School of Medicine at the University of Pennsylvania
Pankhuri Singhal
Affiliation:
Perelman School of Medicine at the University of Pennsylvania
Anurag Verma
Affiliation:
Perelman School of Medicine at the University of Pennsylvania
Ashwin C. Murthy
Affiliation:
Perelman School of Medicine at the University of Pennsylvania
Srinivas Denduluri
Affiliation:
Perelman School of Medicine at the University of Pennsylvania
Srinath Adusumalli
Affiliation:
Perelman School of Medicine at the University of Pennsylvania
Macrylyn D. Ritchie
Affiliation:
Perelman School of Medicine at the University of Pennsylvania
Thomas P. Cappola
Affiliation:
Perelman School of Medicine at the University of Pennsylvania
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

ABSTRACT IMPACT: Measuring and analyzing qualitative and quantitative traits using phenomics approaches will yield previously unrecognized heart failure subphenotypes and has the potential to improve our knowledge of heart failure pathophysiology, identify novel biomarkers of disease, and guide the development of targeted therapeutics for heart failure. OBJECTIVES/GOALS: Current classification schemes fail to capture the broader pathophysiologic heterogeneity in heart failure. Phenomics offers a newer unbiased approach to identify subtypes of complex disease syndromes, like heart failure. The goal of this research is to use data-driven associations to redefine the classification of the heart failure syndrome. METHODS/STUDY POPULATION: We will identify < 10 subphenotypes of patients with heart failure using unsupervised machine learning approaches for dense multidimensional quantitative (i.e. demographics, comorbid conditions, physiologic measurements, clinical laboratory, imaging, and medication variables; disease diagnosis, procedure, and billing codes) and qualitative data extracted from an integrated health system electronic health record. The heart failure subphenotypes we identify from the integrated health system electronic health record will be replicated in other heart failure population datasets using unsupervised learning approaches. We will explore the potential to establish associations between identified subphenotypes and clinical outcomes (e.g. all-cause mortality, cardiovascular mortality). RESULTS/ANTICIPATED RESULTS: We expect to identify < 10 mutually exclusive phenogroups of patients with heart failure that have differential risk profiles and clinical trajectories. DISCUSSION/SIGNIFICANCE OF FINDINGS: We will attempt to derive and validate a data-driven unbiased approach to the categorization of novel phenogroups in heart failure. This has the potential to improve our knowledge of heart failure pathophysiology, identify novel biomarkers of disease, and guide the development of targeted therapeutics for heart failure.

Type
Data Science/Biostatistics/Informatics
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Association for Clinical and Translational Science 2021