Journal Article Epublish
PubMed: 38770008 DOI: 10.1183/23120541.00959-2023
Cluster analysis identifies novel real-world lung disease-pulmonary hypertension subphenotypes: implications for treatment response.
ERJ Open Res 10(3)
Authors: Shelsey W Johnson, Rui-Sheng Wang, Michael R Winter, Kari R Gillmeyer, Katarina Zeder, Elizabeth S Klings, Ronald H Goldstein, Renda Soylemez Wiener, Bradley A Maron
Affiliations
- VA Boston Healthcare System, Boston, MA, USA.
- The Pulmonary Center, Division of Pulmonary, Allergy, Sleep and Critical Care, and Boston University School of Medicine, Boston, MA, USA.
- Department of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Boston University School of Public Health, Biostatistics and Epidemiology Data Analytics Center, Boston, MA, USA.
- Center for Healthcare Organization and Implementation Research, VA Bedford Healthcare System, Bedford, MA, USA.
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
- The University of Maryland-Institute for Health Computing, Bethesda, MD, USA.
Abstract
Background
Clinical trials repurposing pulmonary arterial hypertension (PAH) therapies to patients with lung disease- or hypoxia-pulmonary hypertension (PH) (classified as World Health Organization Group 3 PH) have failed to show a consistent benefit. However, Group 3 PH clinical heterogeneity suggests robust phenotyping may inform detection of treatment-responsive subgroups. We hypothesised that cluster analysis would identify subphenotypes with differential responses to oral PAH therapy.
Methods
Two k-means analyses were performed on a national cohort of US veterans with Group 3 PH; an inclusive model (I) of all treated patients (n=196) and a haemodynamic model (H) limited to patients with right heart catheterisations (n=112). The primary outcome was organ failure or all-cause mortality by cluster. An exploratory analysis evaluated within-cluster treatment effects.
Results
Three distinct clusters of Group 3 PH patients were identified. In the inclusive model (C1 n=43, 21.9%; C2 n=102, 52.0%; C3 n=51, 26.0%), lung disease and spirometry drove cluster assignment. By contrast, in the haemodynamic model (C1 n=44, 39.3%; C2 n=43, 38.4%; C3 n=25, 22.3%), right heart catheterisation data surpassed the importance of lung disease and spirometry. In the haemodynamic model, compared to C3, C1 experienced the greatest hazard for respiratory failure or death (HR 6.1, 95% CI 3.2-11.8). In an exploratory analysis, cluster determined treatment response (p=0.006). Conclusions regarding within-cluster treatment responses were limited by significant differences between select variables in the treated and untreated groups.
Conclusions
Cluster analysis identifies novel real-world subphenotypes of Group 3 PH patients with distinct clinical trajectories. Future studies may consider this methodological approach to identify subgroups of heterogeneous patients that may be responsive to existing pulmonary vasodilatory therapies.
MeSH
Heart, Spirometry, Therapeutics, Hypertension, Pulmonary, Lung Diseases, Cluster Analysis