Griffiths Megan, Simpson Catherine E, Yang Jun, Vaidya Dhananjay, Nies Melanie K, Brandal Stephanie, Damico Rachel, Hassoun Paul, Ivy Dunbar D, Austin Eric D, Pauciulo Michael W, Lutz Katie A, Martin Lisa J, Rosenzweig Erika B, Benza Raymond L, Nichols William C, Manlhiot Cedric, Everett Allen D
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.
Division of Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, MD.
Chest. 2024 Dec;166(6):1511-1531. doi: 10.1016/j.chest.2024.06.3824. Epub 2024 Aug 16.
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.
Can a multiple blood biomarker model of PAH, using previously described biomarkers, improve risk discrimination over current models?
Using 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.
The 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.
In 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.
肺动脉高压（PAH）的风险评估对于指导治疗和改善预后至关重要。临床模型在识别高危患者方面表现出色，但在中度风险患者中仍存在不确定性。
使用先前描述的生物标志物构建的PAH多血液生物标志物模型，能否比当前模型更好地进行风险判别？
我们使用多重酶联免疫吸附测定法，在训练队列（n = 1,623）、测试队列（n = 696）和验证队列（n = 237）中测量了N末端B型利钠肽原（NT-proBNP）、可溶性肿瘤抑制因子、白细胞介素-6、内皮抑素、半乳糖凝集素-3、肝癌衍生生长因子和胰岛素样生长因子结合蛋白（IGFBP1-7）。通过主成分分析评估临床变量和生物标志物。构建一个独立于NT-proBNP的模型时未纳入NT-proBNP。采用无监督k均值聚类将参与者分为不同类别。使用Kaplan-Meier法和Cox比例风险回归分析按类别划分的无移植生存期。将按类别划分的风险与单独的NT-proBNP、评估PAH疾病早期和长期管理的注册研究（REVEAL）以及欧洲心脏病学会和欧洲呼吸学会风险模型，以及临床和生物标志物联合模型进行比较。
该算法使用六种生物标志物、体重、身高和PAH诊断时的年龄生成了五个具有良好风险判别的类别。在测试和验证队列中，单独的生物标志物模型表现与REVEAL相当（受试者工作特征曲线下面积为0.74）。将生物标志物模型添加到欧洲心脏病学会和欧洲呼吸学会评分以及REVEAL评分中，可改善欧洲心脏病学会和欧洲呼吸学会评分以及REVEAL评分。总体最佳模型是针对NT-proBNP进行调整的生物标志物模型，与单独的生物标志物或NT-proBNP模型相比，该调整模型具有最佳的C统计量、赤池信息准则和校准。
在本研究中，单独的多生物标志物模型与当前PAH临床死亡率风险预测模型相当，并且在与NT-proBNP联合或添加到NT-proBNP模型中时可提高性能。临床风险评分提供了出色的预测模型，但需要多项检测；将血液生物标志物添加到模型中可以改善预测，或者能够更频繁、无创地监测PAH风险以支持治疗决策。
