Cargando…
42855 A Phenomics Approach to the Categorization and Refinement of Heart Failure
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...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8827895/ http://dx.doi.org/10.1017/cts.2021.524 |
_version_ | 1784647739918778368 |
---|---|
author | Reza, Nosheen Bone, William Singhal, Pankhuri Verma, Anurag Murthy, Ashwin C. Denduluri, Srinivas Adusumalli, Srinath Ritchie, Macrylyn D. Cappola, Thomas P. |
author_facet | Reza, Nosheen Bone, William Singhal, Pankhuri Verma, Anurag Murthy, Ashwin C. Denduluri, Srinivas Adusumalli, Srinath Ritchie, Macrylyn D. Cappola, Thomas P. |
author_sort | Reza, Nosheen |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8827895 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-88278952022-03-04 42855 A Phenomics Approach to the Categorization and Refinement of Heart Failure Reza, Nosheen Bone, William Singhal, Pankhuri Verma, Anurag Murthy, Ashwin C. Denduluri, Srinivas Adusumalli, Srinath Ritchie, Macrylyn D. Cappola, Thomas P. J Clin Transl Sci Data Science/Biostatistics/Informatics 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. Cambridge University Press 2021-03-30 /pmc/articles/PMC8827895/ http://dx.doi.org/10.1017/cts.2021.524 Text en © The Association for Clinical and Translational Science 2021 https://creativecommons.org/licenses/by/4.0/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. |
spellingShingle | Data Science/Biostatistics/Informatics Reza, Nosheen Bone, William Singhal, Pankhuri Verma, Anurag Murthy, Ashwin C. Denduluri, Srinivas Adusumalli, Srinath Ritchie, Macrylyn D. Cappola, Thomas P. 42855 A Phenomics Approach to the Categorization and Refinement of Heart Failure |
title | 42855 A Phenomics Approach to the Categorization and Refinement of Heart Failure |
title_full | 42855 A Phenomics Approach to the Categorization and Refinement of Heart Failure |
title_fullStr | 42855 A Phenomics Approach to the Categorization and Refinement of Heart Failure |
title_full_unstemmed | 42855 A Phenomics Approach to the Categorization and Refinement of Heart Failure |
title_short | 42855 A Phenomics Approach to the Categorization and Refinement of Heart Failure |
title_sort | 42855 a phenomics approach to the categorization and refinement of heart failure |
topic | Data Science/Biostatistics/Informatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8827895/ http://dx.doi.org/10.1017/cts.2021.524 |
work_keys_str_mv | AT rezanosheen 42855aphenomicsapproachtothecategorizationandrefinementofheartfailure AT bonewilliam 42855aphenomicsapproachtothecategorizationandrefinementofheartfailure AT singhalpankhuri 42855aphenomicsapproachtothecategorizationandrefinementofheartfailure AT vermaanurag 42855aphenomicsapproachtothecategorizationandrefinementofheartfailure AT murthyashwinc 42855aphenomicsapproachtothecategorizationandrefinementofheartfailure AT dendulurisrinivas 42855aphenomicsapproachtothecategorizationandrefinementofheartfailure AT adusumallisrinath 42855aphenomicsapproachtothecategorizationandrefinementofheartfailure AT ritchiemacrylynd 42855aphenomicsapproachtothecategorizationandrefinementofheartfailure AT cappolathomasp 42855aphenomicsapproachtothecategorizationandrefinementofheartfailure |