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Clinical Implications of Cluster Analysis-Based Classification of Acute Decompensated Heart Failure and Correlation with Bedside Hemodynamic Profiles

BACKGROUND: Classification of acute decompensated heart failure (ADHF) is based on subjective criteria that crudely capture disease heterogeneity. Improved phenotyping of the syndrome may help improve therapeutic strategies. OBJECTIVE: To derive cluster analysis-based groupings for patients hospital...

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Autores principales: Ahmad, Tariq, Desai, Nihar, Wilson, Francis, Schulte, Phillip, Dunning, Allison, Jacoby, Daniel, Allen, Larry, Fiuzat, Mona, Rogers, Joseph, Felker, G. Michael, O’Connor, Christopher, Patel, Chetan B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4739604/
https://www.ncbi.nlm.nih.gov/pubmed/26840410
http://dx.doi.org/10.1371/journal.pone.0145881
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author Ahmad, Tariq
Desai, Nihar
Wilson, Francis
Schulte, Phillip
Dunning, Allison
Jacoby, Daniel
Allen, Larry
Fiuzat, Mona
Rogers, Joseph
Felker, G. Michael
O’Connor, Christopher
Patel, Chetan B.
author_facet Ahmad, Tariq
Desai, Nihar
Wilson, Francis
Schulte, Phillip
Dunning, Allison
Jacoby, Daniel
Allen, Larry
Fiuzat, Mona
Rogers, Joseph
Felker, G. Michael
O’Connor, Christopher
Patel, Chetan B.
author_sort Ahmad, Tariq
collection PubMed
description BACKGROUND: Classification of acute decompensated heart failure (ADHF) is based on subjective criteria that crudely capture disease heterogeneity. Improved phenotyping of the syndrome may help improve therapeutic strategies. OBJECTIVE: To derive cluster analysis-based groupings for patients hospitalized with ADHF, and compare their prognostic performance to hemodynamic classifications derived at the bedside. METHODS: We performed a cluster analysis on baseline clinical variables and PAC measurements of 172 ADHF patients from the ESCAPE trial. Employing regression techniques, we examined associations between clusters and clinically determined hemodynamic profiles (warm/cold/wet/dry). We assessed association with clinical outcomes using Cox proportional hazards models. Likelihood ratio tests were used to compare the prognostic value of cluster data to that of hemodynamic data. RESULTS: We identified four advanced HF clusters: 1) male Caucasians with ischemic cardiomyopathy, multiple comorbidities, lowest B-type natriuretic peptide (BNP) levels; 2) females with non-ischemic cardiomyopathy, few comorbidities, most favorable hemodynamics; 3) young African American males with non-ischemic cardiomyopathy, most adverse hemodynamics, advanced disease; and 4) older Caucasians with ischemic cardiomyopathy, concomitant renal insufficiency, highest BNP levels. There was no association between clusters and bedside-derived hemodynamic profiles (p = 0.70). For all adverse clinical outcomes, Cluster 4 had the highest risk, and Cluster 2, the lowest. Compared to Cluster 4, Clusters 1–3 had 45–70% lower risk of all-cause mortality. Clusters were significantly associated with clinical outcomes, whereas hemodynamic profiles were not. CONCLUSIONS: By clustering patients with similar objective variables, we identified four clinically relevant phenotypes of ADHF patients, with no discernable relationship to hemodynamic profiles, but distinct associations with adverse outcomes. Our analysis suggests that ADHF classification using simultaneous considerations of etiology, comorbid conditions, and biomarker levels, may be superior to bedside classifications.
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spelling pubmed-47396042016-02-11 Clinical Implications of Cluster Analysis-Based Classification of Acute Decompensated Heart Failure and Correlation with Bedside Hemodynamic Profiles Ahmad, Tariq Desai, Nihar Wilson, Francis Schulte, Phillip Dunning, Allison Jacoby, Daniel Allen, Larry Fiuzat, Mona Rogers, Joseph Felker, G. Michael O’Connor, Christopher Patel, Chetan B. PLoS One Research Article BACKGROUND: Classification of acute decompensated heart failure (ADHF) is based on subjective criteria that crudely capture disease heterogeneity. Improved phenotyping of the syndrome may help improve therapeutic strategies. OBJECTIVE: To derive cluster analysis-based groupings for patients hospitalized with ADHF, and compare their prognostic performance to hemodynamic classifications derived at the bedside. METHODS: We performed a cluster analysis on baseline clinical variables and PAC measurements of 172 ADHF patients from the ESCAPE trial. Employing regression techniques, we examined associations between clusters and clinically determined hemodynamic profiles (warm/cold/wet/dry). We assessed association with clinical outcomes using Cox proportional hazards models. Likelihood ratio tests were used to compare the prognostic value of cluster data to that of hemodynamic data. RESULTS: We identified four advanced HF clusters: 1) male Caucasians with ischemic cardiomyopathy, multiple comorbidities, lowest B-type natriuretic peptide (BNP) levels; 2) females with non-ischemic cardiomyopathy, few comorbidities, most favorable hemodynamics; 3) young African American males with non-ischemic cardiomyopathy, most adverse hemodynamics, advanced disease; and 4) older Caucasians with ischemic cardiomyopathy, concomitant renal insufficiency, highest BNP levels. There was no association between clusters and bedside-derived hemodynamic profiles (p = 0.70). For all adverse clinical outcomes, Cluster 4 had the highest risk, and Cluster 2, the lowest. Compared to Cluster 4, Clusters 1–3 had 45–70% lower risk of all-cause mortality. Clusters were significantly associated with clinical outcomes, whereas hemodynamic profiles were not. CONCLUSIONS: By clustering patients with similar objective variables, we identified four clinically relevant phenotypes of ADHF patients, with no discernable relationship to hemodynamic profiles, but distinct associations with adverse outcomes. Our analysis suggests that ADHF classification using simultaneous considerations of etiology, comorbid conditions, and biomarker levels, may be superior to bedside classifications. Public Library of Science 2016-02-03 /pmc/articles/PMC4739604/ /pubmed/26840410 http://dx.doi.org/10.1371/journal.pone.0145881 Text en © 2016 Ahmad et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ahmad, Tariq
Desai, Nihar
Wilson, Francis
Schulte, Phillip
Dunning, Allison
Jacoby, Daniel
Allen, Larry
Fiuzat, Mona
Rogers, Joseph
Felker, G. Michael
O’Connor, Christopher
Patel, Chetan B.
Clinical Implications of Cluster Analysis-Based Classification of Acute Decompensated Heart Failure and Correlation with Bedside Hemodynamic Profiles
title Clinical Implications of Cluster Analysis-Based Classification of Acute Decompensated Heart Failure and Correlation with Bedside Hemodynamic Profiles
title_full Clinical Implications of Cluster Analysis-Based Classification of Acute Decompensated Heart Failure and Correlation with Bedside Hemodynamic Profiles
title_fullStr Clinical Implications of Cluster Analysis-Based Classification of Acute Decompensated Heart Failure and Correlation with Bedside Hemodynamic Profiles
title_full_unstemmed Clinical Implications of Cluster Analysis-Based Classification of Acute Decompensated Heart Failure and Correlation with Bedside Hemodynamic Profiles
title_short Clinical Implications of Cluster Analysis-Based Classification of Acute Decompensated Heart Failure and Correlation with Bedside Hemodynamic Profiles
title_sort clinical implications of cluster analysis-based classification of acute decompensated heart failure and correlation with bedside hemodynamic profiles
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4739604/
https://www.ncbi.nlm.nih.gov/pubmed/26840410
http://dx.doi.org/10.1371/journal.pone.0145881
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