• Megan Griffiths, Catherine E Simpson, Jun Yang, Dhananjay Vaidya, Melanie K Nies, Stephanie Brandal, Rachel Damico, Paul Hassoun, Dunbar D Ivy, Eric D Austin, Michael W Pauciulo, Katie A Lutz, MartinLisa JLJDivision of Human Genetics, Cincinnati Children's Hospital Medical Center; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati., Erika B Rosenzweig, Raymond L Benza, William C Nichols, Cedric Manlhiot, and Allen D Everett.
• Blalock-Taussig-Thomas Congenital Heart Center, Department of Pediatrics, Johns Hopkins University, Baltimore, MD; Division of Pediatric Cardiology, Department of Pediatrics, University of Texas Southwestern, Dallas, TX. Electronic address: Megan.Griffiths@UTSouthwestern.edu.
• Chest. 2024 Aug 16.

BackgroundRisk 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 QuestionCan a multiple blood biomarker model of PAH, using previously described biomarkers, improve risk discrimination over current models?Study Design And MethodsUsing a multiplex enzyme-linked immunosorbent assay, we measured N-terminal fragment of the prohormone brain natriuretic peptide (NT-proBNP), soluble suppressor of tumorigenicity, IL-6, endostatin, galectin 3, HDGF, and insulin-like growth factor binding proteins (IGFBP1-7) in training (n = 1,623), test (n = 696), and validation (n = 237) cohorts. Clinical variables and biomarkers were evaluated by principal component analysis. NT-proBNP was not included to develop a model independent of NT-proBNP. Unsupervised k-means clustering classified participants into clusters. Transplant-free survival by cluster was examined using Kaplan-Meier and Cox proportional hazard regressions. Hazard by cluster was compared with NT-proBNP, Registry to Evaluate Early and Long-Term PAH Disease Management (REVEAL), and European Society of Cardiology (ESC) and European Respiratory Society (ERS) risk models alone and combined clinical and biomarker models.ResultsThe 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 (area under the receiver operating characteristic curve, 0.74). Adding the biomarker model to the ESC and ERS score and REVEAL score improved the ESC and ERS score and REVEAL score. The best overall model was the biomarker model adjusted for NT-proBNP with the best C statistic, Akaike information criterion, and calibration for the adjusted model compared with either the biomarker or NT-proBNP model alone.InterpretationA multibiomarker 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 can enable more frequent, noninvasive monitoring of risk in PAH to support therapeutic decision-making.Copyright © 2024 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

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