Early Detection of Right Ventricular Dysfunction and Emerging Pulmonary Hypertension in Systemic Sclerosis

系统性硬化症患者右心室功能障碍和肺动脉高压的早期发现

• 批准号: 10585312
• 负责人: Monica Mukherjee
• 金额: $ 65.34万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2028-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10585312
• 关键词: Affect; Algorithm Design; Algorithms; Autoimmune Diseases; Awareness; Biological; Biological Markers; Biometry; Cardiopulmonary; Cause of Death; Characteristics; Clinical; Clinical Management; Complex; Coupling; Data; Depressed mood; Detection; Development; Diagnosis; Disease; Dyspnea; Early Diagnosis; Early Intervention; Early identification; Early treatment; Echocardiography; Fibrosis; Guidelines; Heart failure; Heterogeneity; Image; Imaging Techniques; Machine Learning; Measurement; Measures; Methodology; Methods; Modeling; Morbidity - disease rate; Myocardial; Nature; Outcome; Patients; Performance; Phenotype; Physiological; Population; Positioning Attribute; Process; Prognosis; Pulmonary Heart Disease; Pulmonary Hypertension; Recommendation; Research; Rest; Right Ventricular Dysfunction; Right Ventricular Function; Risk; Secondary to; Stress; Subgroup; Systemic Scleroderma; Technical Expertise; Techniques; Testing; Therapeutic; Therapeutic Intervention; Tissues; Validation; Vascular Diseases; Ventricular; adverse event risk; case control; clinical application; clinical phenotype; clinically relevant; cohort; design; disability; hemodynamics; high risk; imaging biomarker; improved; improved outcome; indexing; innovation; mortality; novel; patient population; patient stratification; patient subsets; predicting response; pressure; prognostic; prospective; pulmonary arterial hypertension; pulmonary vascular disorder; quantitative imaging; rate of change; recruit; response; right ventricular failure; screening; systemic autoimmune disease; targeted treatment

Pulmonary arterial hypertension (PAH) is a highly morbid disease that commonly complicates patients with the autoimmune disease systemic sclerosis (SSc) and is a leading cause of death in this population. Right ventricular (RV) adaptation to progressive increases in afterload is the main determinant of outcome in SSc and despite guideline-recommended early detection algorithms designed to identify PAH and therapeutic advances in the treatment of PAH, SSc patients are often diagnosed late, and mortality remains exceedingly high. Although several strategies are available, existing screening algorithms have low predictive accuracy. Thus, an unmet need to better delineate high-risk phenotypes in SSc and improve identification of subgroups at greatest risk of PAH earlier in the disease course when therapeutic interventions may affect prognosis. This research aims to fill this gap by providing noninvasive and objective prognostic quantitative imaging markers and trajectory-based subtype analysis using sophisticated biostatistical and machine-learning (ML) techniques, enabling early identification of subpopulations at risk for adverse, long-term outcomes. Utilizing novel echocardiographic techniques, we recently identified early changes in RV contractility and contractile reserve in SSc prior to the development of overt PAH. Drawing from these findings, in Aim 1, we will specifically determine whether the addition of echo-derived parameters of RV contractility to standard screening can identify distinct clinical phenotypes and high-risk trajectories in the development of PAH, while characterizing the nature and interaction of these trajectories across each of the variables. Our methodology will combine ML with Bayesian multivariate linear mixed modeling to improve characterization and phenotyping of similar subgroups and how trajectories present unique risk for adverse events. Aim 2 focuses on the biologic validation of echo-derived techniques with simultaneous direct chamber-level measures of RV contractile reserve and RV-arterial coupling to determine whether RVLSS is a noninvasive surrogate for RV contractility and RV contractile reserve. Our synergistic and complementary aims will be used to derive and validate a robust early detection strategy in Aim 3, improving upon existing screening methods, enabling the early prediction of PAH in SSc. We are uniquely positioned to study these critical questions given our access to one of the largest and finely phenotyped SSc cohorts in the world, our strong track record of excellence in the noninvasive and invasive assessment of RV function in SSc, and our expertise in the complex multifaceted methodology necessary to complete this project. Early identification and characterization of RV maladaptation to emerging pulmonary vascular disease would be transformative in the clinical management of SSc, a population with exceedingly high morbidity and mortality from cardiopulmonary disease. If successful, our findings may also be applicable to other cohorts who are at-risk for the development of PAH to allow for earlier detection and earlier intervention.

肺动脉高压（PAH）是一种高度病态的疾病，其通常使患有肺动脉高压的患者并发症。 自身免疫性疾病系统性硬化症（SSc），并且是该人群中死亡的主要原因。权 心室（RV）对后负荷进行性增加的适应性是SSc结局的主要决定因素 尽管指南推荐的早期检测算法旨在识别PAH和治疗 PAH治疗的进展，SSc患者通常诊断较晚，死亡率仍然极高 高虽然有几种策略是可用的，但现有的筛选算法具有较低的预测准确性。 因此，需要更好地描述SSc的高风险表型并改善亚组的识别， 在病程早期，当治疗干预可能影响预后时，PAH的风险最大。这 研究旨在通过提供非侵入性和客观的预后定量成像标记物来填补这一空白 以及使用复杂的生物统计学和机器学习（ML）技术进行基于概率的亚型分析， 从而能够早期识别处于不良长期结果风险中的亚群。利用新 超声心动图技术，我们最近确定了RV收缩力和收缩储备的早期变化 在出现明显PAH之前，SSc中。根据这些发现，在目标1中，我们将具体 确定在标准筛查中增加超声心动图衍生的RV收缩性参数是否可以 识别PAH发展中不同的临床表型和高风险轨迹，同时表征 这些轨迹在每个变量上的性质和相互作用。我们的方法将联合收割机ML 与贝叶斯多元线性混合建模，以改善表征和表型相似 亚组以及轨迹如何呈现不良事件的独特风险。目标2关注生物学 同时直接测量心室水平RV收缩功能的回声衍生技术的验证 储备和RV-动脉耦合，以确定RVLSS是否是RV收缩性的无创替代品 和RV收缩储备。我们的协同和互补的目标将被用来推导和验证一个 目标3中的强有力的早期检测战略，改进现有的筛查方法， SSc中PAH的预测。鉴于我们有机会研究这些关键问题，我们处于独特的地位 在世界上最大和最好的表型SSc队列中，我们在 SSc中RV功能的无创和有创评估，以及我们在复杂多方面的专业知识， 完成该项目所需的方法。RV适应不良的早期识别和表征 新出现的肺血管疾病将是SSc临床管理的变革， 心肺疾病发病率和死亡率极高的人群。如果成功，我们 研究结果也可能适用于其他有PAH发生风险的队列， 检测和早期干预。