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

Publication ,  Journal Article
Rambarat, P; Zern, EK; Wang, D; Roshandelpoor, A; Zarbafian, S; Liu, EE; Wang, JK; McNeill, JN; Andrews, CT; Pomerantsev, EV; Diamant, N ...
Published in: PLoS One
2023

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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Published In

PLoS One

DOI

EISSN

1932-6203

Publication Date

2023

Volume

18

Issue

8

Start / End Page

e0290553

Location

United States

Related Subject Headings

  • Middle Aged
  • Male
  • Hypertension, Pulmonary
  • Hypertension
  • Humans
  • Heart Diseases
  • General Science & Technology
  • Female
  • Cluster Analysis
  • Atrial Fibrillation
 

Citation

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Rambarat, P., Zern, E. K., Wang, D., Roshandelpoor, A., Zarbafian, S., Liu, E. E., … Ho, J. E. (2023). Identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis. PLoS One, 18(8), e0290553. https://doi.org/10.1371/journal.pone.0290553
Rambarat, Paula, Emily K. Zern, Dongyu Wang, Athar Roshandelpoor, Shahrooz Zarbafian, Elizabeth E. Liu, Jessica K. Wang, et al. “Identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis.PLoS One 18, no. 8 (2023): e0290553. https://doi.org/10.1371/journal.pone.0290553.
Rambarat P, Zern EK, Wang D, Roshandelpoor A, Zarbafian S, Liu EE, et al. Identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis. PLoS One. 2023;18(8):e0290553.
Rambarat, Paula, et al. “Identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis.PLoS One, vol. 18, no. 8, 2023, p. e0290553. Pubmed, doi:10.1371/journal.pone.0290553.
Rambarat P, Zern EK, Wang D, Roshandelpoor A, Zarbafian S, Liu EE, Wang JK, McNeill JN, Andrews CT, Pomerantsev EV, Diamant N, Batra P, Lubitz SA, Picard MH, Ho JE. Identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis. PLoS One. 2023;18(8):e0290553.

Published In

PLoS One

DOI

EISSN

1932-6203

Publication Date

2023

Volume

18

Issue

8

Start / End Page

e0290553

Location

United States

Related Subject Headings

  • Middle Aged
  • Male
  • Hypertension, Pulmonary
  • Hypertension
  • Humans
  • Heart Diseases
  • General Science & Technology
  • Female
  • Cluster Analysis
  • Atrial Fibrillation