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，通常使患者复杂化 自身免疫性疾病全身性硬化症（SSC），是该人群的主要死亡原因。正确的 心室（RV）适应后负载的进行性增加是SSC结果的主要决定因素 尽管指南征求了旨在识别PAH和治疗的早期检测算法 PAH治疗的进展，SSC患者经常被诊断出来，死亡率仍然极为 高的。尽管有几种策略可用，但现有的筛选算法的预测准确性较低。 因此，未满足的需要更好地描述SSC中的高风险表型并改善亚组的识别 当治疗干预措施可能影响预后时，疾病病程中PAH的风险很大。这 研究旨在通过提供无创和客观的预后定量成像标记来填补这一空白 以及基于轨迹的亚型分析，使用复杂的生物统计学和机器学习（ML）技术， 能够早期鉴定有不利的长期结局风险的亚群。利用小说 超声心动图技术，我们最近确定了RV收缩性和收缩储备的早期变化 在开发公开PAH之前，在SSC中。从这些发现中汲取了目标，在AIM 1中，我们将具体 确定将回声衍生的RV收缩性参数添加到标准筛选中是否可以 在PAH的发展中确定不同的临床表型和高风险轨迹 这些轨迹在每个变量中的性质和相互作用。我们的方法将结合ML 使用贝叶斯多元线性混合建模，以改善相似的表征和表型 亚组以及轨迹如何给不良事件带来独特的风险。 AIM 2专注于生物学 通过同时直接室内级别的RV收缩措施来验证回声衍生的技术 储备和RV - 动脉耦合，以确定RVLSS是否是RV收缩性的无创替代物 和RV收缩储备。我们的协同和互补目标将用于得出和验证 AIM 3中强大的早期检测策略，改善了现有的筛选方法，使得早期 SSC中PAH的预测。鉴于我们可以访问一个关键问题，我们有一个独特的位置 我们在世界上最大，最精致的SSC队列中，我们在 SSC中RV功能的无创和侵入性评估，以及我们在复杂多面的专业知识 完成该项目所需的方法。 RV不良适应的早期识别和表征 在SSC的临床管理中，新兴的肺血管疾病将是变化的 心肺疾病的发病率和死亡率极高的人群。如果成功，我们的 调查结果也可能适用于其他在危险中的同类人群，用于开发PAH 检测和更早的干预。