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Identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis

INTRODUCTION: The classification and management of pulmonary hypertension (PH) is challenging due to clinical heterogeneity of patients. We sought to identify distinct multimorbid phenogroups of patients with PH that are at particularly high-risk for adverse events. METHODS: A hospital-based cohort...

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Autores principales: Rambarat, Paula, Zern, Emily K., Wang, Dongyu, Roshandelpoor, Athar, Zarbafian, Shahrooz, Liu, Elizabeth E., Wang, Jessica K., McNeill, Jenna N., Andrews, Carl T., Pomerantsev, Eugene V., Diamant, Nathaniel, Batra, Puneet, Lubitz, Steven A., Picard, Michael H., Ho, Jennifer E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10456132/
https://www.ncbi.nlm.nih.gov/pubmed/37624825
http://dx.doi.org/10.1371/journal.pone.0290553
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author Rambarat, Paula
Zern, Emily K.
Wang, Dongyu
Roshandelpoor, Athar
Zarbafian, Shahrooz
Liu, Elizabeth E.
Wang, Jessica K.
McNeill, Jenna N.
Andrews, Carl T.
Pomerantsev, Eugene V.
Diamant, Nathaniel
Batra, Puneet
Lubitz, Steven A.
Picard, Michael H.
Ho, Jennifer E.
author_facet Rambarat, Paula
Zern, Emily K.
Wang, Dongyu
Roshandelpoor, Athar
Zarbafian, Shahrooz
Liu, Elizabeth E.
Wang, Jessica K.
McNeill, Jenna N.
Andrews, Carl T.
Pomerantsev, Eugene V.
Diamant, Nathaniel
Batra, Puneet
Lubitz, Steven A.
Picard, Michael H.
Ho, Jennifer E.
author_sort Rambarat, Paula
collection PubMed
description INTRODUCTION: The classification and management of pulmonary hypertension (PH) is challenging due to clinical heterogeneity of patients. We sought to identify distinct multimorbid phenogroups of patients with PH that are at particularly high-risk for adverse events. METHODS: A hospital-based cohort of patients referred for right heart catheterization between 2005–2016 with PH were included. Key exclusion criteria were shock, cardiac arrest, cardiac transplant, or valvular surgery. K-prototypes was used to cluster patients into phenogroups based on 12 clinical covariates. RESULTS: Among 5208 patients with mean age 64±12 years, 39% women, we identified 5 distinct multimorbid PH phenogroups with similar hemodynamic measures yet differing clinical outcomes: (1) “young men with obesity”, (2) “women with hypertension”, (3) “men with overweight”, (4) “men with cardiometabolic and cardiovascular disease”, and (5) “men with structural heart disease and atrial fibrillation.” Over a median follow-up of 6.3 years, we observed 2182 deaths and 2002 major cardiovascular events (MACE). In age- and sex-adjusted analyses, phenogroups 4 and 5 had higher risk of MACE (HR 1.68, 95% CI 1.41–2.00 and HR 1.52, 95% CI 1.24–1.87, respectively, compared to the lowest risk phenogroup 1). Phenogroup 4 had the highest risk of mortality (HR 1.26, 95% CI 1.04–1.52, relative to phenogroup 1). CONCLUSIONS: Cluster-based analyses identify patients with PH and specific comorbid cardiometabolic and cardiovascular disease burden that are at highest risk for adverse clinical outcomes. Interestingly, cardiopulmonary hemodynamics were similar across phenogroups, highlighting the importance of multimorbidity on clinical trajectory. Further studies are needed to better understand comorbid heterogeneity among patients with PH.
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spelling pubmed-104561322023-08-26 Identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis Rambarat, Paula Zern, Emily K. Wang, Dongyu Roshandelpoor, Athar Zarbafian, Shahrooz Liu, Elizabeth E. Wang, Jessica K. McNeill, Jenna N. Andrews, Carl T. Pomerantsev, Eugene V. Diamant, Nathaniel Batra, Puneet Lubitz, Steven A. Picard, Michael H. Ho, Jennifer E. PLoS One Research Article INTRODUCTION: The classification and management of pulmonary hypertension (PH) is challenging due to clinical heterogeneity of patients. We sought to identify distinct multimorbid phenogroups of patients with PH that are at particularly high-risk for adverse events. METHODS: A hospital-based cohort of patients referred for right heart catheterization between 2005–2016 with PH were included. Key exclusion criteria were shock, cardiac arrest, cardiac transplant, or valvular surgery. K-prototypes was used to cluster patients into phenogroups based on 12 clinical covariates. RESULTS: Among 5208 patients with mean age 64±12 years, 39% women, we identified 5 distinct multimorbid PH phenogroups with similar hemodynamic measures yet differing clinical outcomes: (1) “young men with obesity”, (2) “women with hypertension”, (3) “men with overweight”, (4) “men with cardiometabolic and cardiovascular disease”, and (5) “men with structural heart disease and atrial fibrillation.” Over a median follow-up of 6.3 years, we observed 2182 deaths and 2002 major cardiovascular events (MACE). In age- and sex-adjusted analyses, phenogroups 4 and 5 had higher risk of MACE (HR 1.68, 95% CI 1.41–2.00 and HR 1.52, 95% CI 1.24–1.87, respectively, compared to the lowest risk phenogroup 1). Phenogroup 4 had the highest risk of mortality (HR 1.26, 95% CI 1.04–1.52, relative to phenogroup 1). CONCLUSIONS: Cluster-based analyses identify patients with PH and specific comorbid cardiometabolic and cardiovascular disease burden that are at highest risk for adverse clinical outcomes. Interestingly, cardiopulmonary hemodynamics were similar across phenogroups, highlighting the importance of multimorbidity on clinical trajectory. Further studies are needed to better understand comorbid heterogeneity among patients with PH. Public Library of Science 2023-08-25 /pmc/articles/PMC10456132/ /pubmed/37624825 http://dx.doi.org/10.1371/journal.pone.0290553 Text en © 2023 Rambarat et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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
Rambarat, Paula
Zern, Emily K.
Wang, Dongyu
Roshandelpoor, Athar
Zarbafian, Shahrooz
Liu, Elizabeth E.
Wang, Jessica K.
McNeill, Jenna N.
Andrews, Carl T.
Pomerantsev, Eugene V.
Diamant, Nathaniel
Batra, Puneet
Lubitz, Steven A.
Picard, Michael H.
Ho, Jennifer E.
Identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis
title Identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis
title_full Identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis
title_fullStr Identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis
title_full_unstemmed Identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis
title_short Identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis
title_sort identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10456132/
https://www.ncbi.nlm.nih.gov/pubmed/37624825
http://dx.doi.org/10.1371/journal.pone.0290553
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