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PLoS One. 2016 Feb 3;11(2):e0145881. doi: 10.1371/journal.pone.0145881. eCollection 2016.

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

Author information

1
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, United States of America.
2
Program for Translational Medicine, Yale University School of Medicine, New Haven, Connecticut, United States of America.
3
Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, United States of America.
4
Duke Clinical Research Institute, Durham, North Carolina, United States of America.
5
Division of Cardiology, Department of Medicine, University of Colorado, Denver, Colorado, United States of America.
6
Division of Cardiology, Duke University Medical Center, Durham, North Carolina, United States of America.
7
Inova Heart and Vascular Institute, Falls Church, Virginia, United States of America.

Abstract

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.

PMID:
26840410
PMCID:
PMC4739604
DOI:
10.1371/journal.pone.0145881
[Indexed for MEDLINE]
Free PMC Article

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