Megan Griffiths, Catherine E. Simpson, Jun Yang, Dhananjay Vaidya, Melanie K. Nies, Stephanie Brandal, Rachel Damico, Paul Hassoun, D. Dunbar Ivy, Eric D. Austin, Michael W. Pauciulo, Katie A. Lutz, Lisa J. Martin, Erika B. Rosenzweig, Raymond L. Benza, William C. Nichols, Cedric Manlhiot, Allen D. Everett
Johns Hopkins University. University of Texas Southwestern. Children’s Hospital Colorado. Vanderbilt University Medical Center. University of Cincinnati College of Medicine. Columbia University Vagelos College of Physicians and Surgeons. Ohio State University.
United States
Chest
Chest 2024;
DOI: 10.1016/j.chest.2024.06.3824
Abstract
Background: Risk assessment in pulmonary arterial hypertension (PAH) is fundamental to guiding treatment and improved outcomes. Clinical models are excellent at identifying high-risk patients but leave uncertainty amongst moderate risk patients.
Research question: Can a multiple blood biomarker model of PAH, using previously described biomarkers, improve risk discrimination over current models?
Study design and methods: Using multiplex ELISA, we measured NT-proBNP, ST2, IL-6, Endostatin, Galectin-3, HDGF, and IGF binding proteins (IGFBP1-7) in train (n=1623), test (n=696) and validation (n=237) cohorts. Clinical variables, biomarkers were evaluated by principal component analysis. NT-proBNP was not included to develop an NT-proBNP independent model. Unsupervised k-means clustering classified subjects into clusters. Transplant-free survival by cluster was examined using Kaplan-Meier and Cox proportional hazard regressions. Hazard by cluster was compared to NT-proBNP, REVEAL, and ESC/ERS Risk models alone, and combined clinical and biomarker models.
Results: The algorithm generated 5 clusters with good risk discrimination using 6 biomarkers, weight, height, and age at PAH diagnosis. In the test and validation cohorts the biomarker model alone performed equivalent to REVEAL (AUC 0.74). Adding the biomarker model to the ESC/ERS, and REVEAL scores improved the ESC/ERS and REVEAL scores. The best overall model was the biomarker model adjusted for NT-proBNP with the best C-statistic, AIC, and calibration for the adjusted model compared to either the biomarker or NT-proBNP model alone.
Interpretation: A multi-biomarker model alone was equivalent to current PAH clinical mortality risk prediction models and improved performance when combined, and added to NT-proBNP. Clinical risk scores offer excellent predictive models but require multiple tests; adding blood biomarkers to models can improve prediction or enable more frequent, non-invasive monitoring of risk in PAH to support therapeutic decision making.
Category
Potential Biomarkers Associated with Pulmonary Vascular Disease
Age Focus: Pediatric Pulmonary Vascular Disease or Adult Pulmonary Vascular Disease
Fresh or Filed Publication: Fresh (PHresh). Less than 1-2 years since publication
Article Access
Free PDF File or Full Text Article Available Through PubMed or DOI: No