ArticleChest2024
Equivalency of Multiple Biomarkers to Clinical Pulmonary Arterial Hypertension Survival Risk Models.
Article in Chest, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Association between upper airway obstruction and pulmonary hypertension in children with Down syndrome.Current opinion in pulmonary medicine · 2026Review
- The Role of the Cellular Communication Network Protein Family in Pulmonary Arterial Hypertension.Pulmonary circulation · 2026Article
- Trends and future projections of the disease burden of pulmonary arterial Hypertension-related heart failure in China and G20 countries: Based on the global burden of disease 2023 database.International journal of cardiology. Cardiovascular risk and prevention · 2026Article
- Hemodynamic and Thrombotic Vulnerability in Pulmonary Arterial Hypertension at High Altitude: Multivariable Predictors of Mortality.Medicina (Kaunas, Lithuania) · 2026Article
- Experimental animal models and patient-derived platforms to bridge preclinical discovery and translational therapeutics in pulmonary arterial hypertension.Journal of translational medicine · 2025Review
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
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 QUESTION: Can 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 pro-B-type natriuretic peptide (NT-proBNP), soluble suppressor of tumorigenicity, IL-6, endostatin, galectin 3, hepatoma derived growth factor, 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 and European Respiratory Society risk models alone and combined clinical and biomarker models.
resultsThe algorithm generated five clusters with good risk discrimination using six 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 European Society of Cardiology and European Respiratory Society score and REVEAL score improved the European Society of Cardiology and European Respiratory Society 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.
interpretationIn this study, a 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.
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