Development of Clinical Prediction Models for Pulmonary Outcomes in Sarcoidosis

结节病肺部结局临床预测模型的开发

• 批准号: 10446452
• 负责人: LAURA L KOTH
• 金额: $ 115.38万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10446452
• 关键词: Address; African American; Age; Area Under Curve; Blood; Blood Proteins; Blood Tests; CXCL10 gene; CXCL11 gene; CXCL9 gene; Caring; Chronic; Chronic Disease; Clinic; Clinical; Clinical Data; Clinical Management; Clinical Practice Guideline; Clinical Research; Collaborations; Collection; Conceptions; Costs and Benefits; Cox Proportional Hazards Models; Data; Data Analyses; Data Set; Disease Progression; Drops; Drug toxicity; Enrollment; Event; Frequencies; Future; Goals; Immunosuppression; Individual; Inflammation; Interferons; Intervention; Investigation; Laboratories; Life; Longitudinal cohort; Lung; Lung diseases; Measures; Medical; Messenger RNA; Modeling; Monitor; Motivation; Organ; Outcome; Patients; Performance; Persons; Physicians; Policies; Probability; Provider; Publishing; Pulmonary Sarcoidosis; RNA; Race; Research; Research Design; Research Personnel; Resources; Risk; Sample Size; Sarcoidosis; Schedule; Serum; Serum Proteins; Severities; Test Result; Testing; Thoracic Radiography; Time; Tobacco; Toxic effect; Training; Transcript; Translating; Translations; United States; United States National Institutes of Health; Validation; Visit; Whole Blood; Work; base; blood-based biomarker; chemokine; clinical development; clinical practice; clinically relevant; cohort; design; disease natural history; follow-up; hazard; high risk; improved; novel; novel marker; pandemic disease; patient stratification; predictive modeling; prognostic; prognostic indicator; prognostic model; prognostic tool; prognostic value; prognostication; pulmonary function; pulmonary function decline; risk stratification; sex; success; systematic review; tool

PROJECT ABSTRACT The purpose of this proposal is to develop clinical prediction tools to risk stratify patients with pulmonary sarcoidosis. Because the course of sarcoidal inflammation lasts for many years even in those with self-limiting sarcoid, the lack of prognostic indicators upon which to develop a management plan is not a trivial issue for both patient and physician. Thus, without prognostication tools, clinicians will not be able to improve the longitudinal care they provide for the types of outcomes that are used in the clinic. The conception of this study was motivated by the preliminary data showing that levels of blood protein and RNA transcripts related to interferon inflammation were predictive of future declines in lung function using Cox proportional hazards modeling. An important feature of the study design is the focus on clinically relevant outcomes and clinical variables so that results can be immediately translated into clinical practice. This goal is realized in Aim 1 in which we will deliver the best performing clinical prediction model that relies only on clinically available data and lab tests to predict a clinical outcome used in pulmonary clinics to define sarcoidosis disease progression. This outcome is a clinically meaningful decline in lung function. In Aim 2, we will measure the added value of incorporating novel blood-based interferon related markers into the clinical prediction models. The goal of this Aim is to identify which novel markers should be developed and moved into the clinical arena for use in sarcoidosis prognostication. Finally, in Aim 3 we will apply these models to an important clinical management problem in sarcoidosis which is identifying the optimal monitoring frequency for a given patient. Addressing this last question is the first step towards being able to better anticipate disease progression before extensive organ damage occurs. The team of sarcoidosis investigators are highly qualified to execute this study because they have a track record of performing sarcoidosis clinical research, have ongoing collaborations and have participated in prior studies that involve biospecimen collection from patients including NIH-sponsored consortium studies. To execute this study, the investigators will contribute longitudinal biospecimens and clinical data from 357 patients with sarcoidosis that have been enrolled in longitudinal cohorts at centers across the US. Two hundred additional patients will be enrolled and followed for up to 39 months leading to a total sample size of 557, of which a majority will be African American. Our team also includes a uniquely qualified biostatistician, Dr. Charles McCulloch, who has pioneered aspects of the longitudinal modeling approach we will be using. Therefore, the proposed Aims have a high likelihood of success for tackling this long-overdue clinical problem.

项目摘要 该提案的目的是开发临床预测工具，以风险分层患者， 肺结节病由于结节性炎症的病程持续多年， 在那些患有自限性结节病的患者中，缺乏预后指标， 管理计划对于患者和医生来说都不是一个微不足道的问题。因此，如果没有 如果没有精确的工具，临床医生将无法改善他们提供的纵向护理， 临床上使用的结果类型。本研究的概念是由以下因素激发的： 初步数据显示与干扰素相关的血液蛋白和RNA转录物水平 使用考克斯比例风险预测未来肺功能下降 建模研究设计的一个重要特征是关注临床相关结局 和临床变量，以便结果可以立即转化为临床实践。这一目标 在目标1中实现，我们将提供性能最佳的临床预测模型， 仅基于临床可用数据和实验室检查来预测用于肺部的临床结局 以确定结节病的进展。这一结果是临床上有意义的下降 在肺功能方面。在目标2中，我们将衡量将新型血液基础 干扰素相关标志物进入临床预测模型。本目标的目的是确定 哪些新的标记物应该被开发并进入临床竞技场用于结节病 精确化。最后，在目标3中，我们将把这些模型应用于一个重要的临床管理 结节病中的一个问题是确定给定患者的最佳监测频率。 解决最后一个问题是能够更好地预测疾病的第一步 在广泛的器官损伤发生之前进行。结节病研究小组 因为他们有结节病的跟踪记录， 临床研究，正在进行合作，并参与了先前的研究，涉及 从患者中采集生物标本，包括NIH申办的联合研究。执行 在这项研究中，研究人员将提供357份纵向生物标本和临床数据， 在美国各中心纵向队列中入组的结节病患者。 另外200例患者将入组并随访长达39个月， 总样本量为557人，其中大部分为非洲裔美国人。我们的团队还包括一个 唯一合格的生物统计学家，查尔斯麦卡洛克博士，谁开创了方面的 我们将使用的纵向建模方法。因此，拟议的目标具有很高的 解决这一拖延已久的临床问题的可能性。