Towards Precise Phenotype Discovery of Obstructive Sleep Apnea with a Data-Inclusive Multi-Study Analysis Using the National Sleep Research Resource (NSRR)

使用国家睡眠研究资源 (NSRR) 通过包含数据的多项研究分析来精确发现阻塞性睡眠呼吸暂停的表型

• 批准号: 10675011
• 负责人: Bing Si
• 金额: $ 11.43万
• 依托单位: STATE UNIVERSITY OF NY,BINGHAMTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10675011
• 关键词: Address; Affect; African American population; Algorithms; Apnea; Asian Americans; Benefits and Risks; Black American; Black Populations; Cardiac health; Cardiovascular Diseases; Cardiovascular system; Categories; Cessation of life; Classification; Clinical; Clinical Research; Clinical Treatment; Clinical Trials; Cohort Studies; Collection; Common Data Element; Data; Data Element; Data Set; Deposition; Drowsiness; Enrollment; Event; Foundations; Funding; Goals; Habits; Health; Heterogeneity; Hispanic Americans; Hispanic Community Health Study; Individual; Machine Learning; Maps; Measures; Medical History; Modeling; Multi-Ethnic Study of Atherosclerosis; Obstructive Sleep Apnea; Outcome; Patient-Focused Outcomes; Patients; Phenotype; Polysomnography; Population; Population Heterogeneity; Prognosis; Prognostic Factor; Quality of life; Reproducibility; Research; Research Personnel; Resources; Risk; Severities; Sleep; Sleep Apnea Syndromes; Stratification; Testing; Treatment Efficacy; United States National Institutes of Health; Woman; cardiovascular disorder risk; cardiovascular risk factor; caucasian American; clinical application; clinical phenotype; clinical practice; cohort; community health study; comorbidity; cost; demographics; design; empowerment; epidemiology study; improved; inclusion criteria; indexing; individual patient; insight; men; middle age; mortality; novel; outcome prediction; patient stratification; phenotypic data; pre-clinical; predict clinical outcome; prognostication; prospective; risk stratification; secondary analysis; sociodemographics; targeted treatment; treatment planning; treatment strategy; validation studies

Project Summary/Abstract Obstructive sleep apnea (OSA) is highly prevalent and associated with a spectrum of cardiovascular (CV) diseases and adverse health outcomes. However, OSA treatment strategies tend to show inconsistent treatment efficacy across individuals and little or no reduction in risk of CV diseases, events, or death. Phenotype discovery is critical for precise risk stratification and targeted treatment of OSA. Substantial heterogeneity among OSA patients is likely an important contributor to the suboptimal results of clinical trials. Thus, it is critical to delineate the OSA heterogeneity and stratify patients into high-vs low-risk clusters (i.e., “phenotypes”) associated with markedly different outcomes for precise risk stratification and targeted treatment. OSA data hold great promise to facilitate OSA phenotype discovery. Rigor of Prior Research: (1) We and others identified new prognostic factors in OSA data that are associated with one or more adverse CV outcomes. (2) Emerging OSA phenotypes were defined by machine learning and clustering algorithms from multi-faceted OSA data. (3) Newly identified OSA phenotypes, predictive of patients’ benefit from OSA treatments and risk for adverse CV outcomes, laid the foundation for OSA phenotypes’ clinical utility in targeted treatment and precise prognosis. However, significant gaps exist in fully leveraging the OSA data for phenotype discovery: There is a lack of “outcome-predictive”, “clinically-interpretable”, and “reproducible” phenotypes, defined from multi-domain OSA data in a large diverse U.S. population. To address these gaps, we propose a secondary multi-study analysis that seeks to develop new classification criteria and identify phenotypes in OSA by integrating multi-domain OSA-related sleep common data elements, including but not limited to patient socio-demographics, health habits, medical history, anthropometrics, polysomnography measures, daytime sleepiness, quality of life, and cardiovascular comorbidities and mortalities, combined across three of the largest epidemiological study cohorts deposited in the NIH-funded National Sleep Research Resource (NSRR). This includes Sleep Heart Health Study, Hispanic Community Health Study, and Multi-Ethnic Study of Atherosclerosis, with at least 5,336 OSA patients from a diverse population of African American, Caucasian, Hispanic, and Asian American men and women. Aim 1: Develop a novel sparse, outcome-predictive multi-domain Factor Mixture Model for OSA phenotype identification from multi-domain mixed-typed patient pre-clinical features and clinical features. Aim 2: Apply the developed model in Aim 1 to individual and pooled NSRR datasets to: (1) identify, characterize, and validate OSA phenotypes; (2) evaluate consistency and reproducibility in findings supported by individual and pooled analyses. Impact: We will identify, characterize, and validate OSA phenotypes that assist clinicians with determining how aggressive to be with the treatment plans and assist researchers with selecting appropriate patients to enroll in clinical trials of OSA treatment, eventually leading to precise prognosis and treatment of OSA.

项目摘要/摘要 阻塞性睡眠呼吸暂停(OSA)非常普遍，并与一系列心血管疾病相关 (Cv)疾病和不良健康后果。然而，阻塞性睡眠呼吸暂停综合征的治疗策略往往表现出不一致 治疗对个人有效，心血管疾病、事件或死亡的风险很少或根本没有降低。 表型发现对于OSA的精确风险分层和靶向治疗至关重要。相当可观 阻塞性睡眠呼吸暂停综合征患者的异质性可能是临床试验结果不佳的重要原因。 因此，关键是要描述OSA的异质性，并将患者分成高风险与低风险组(即， “表型”)与精确风险分层和靶向治疗的显著不同结果相关。 OSA数据在促进OSA表型发现方面大有可为。前期研究的严谨性：(1)我们 其他人在OSA数据中发现了与一个或多个不良简历相关的新的预后因素 结果。(2)通过机器学习和聚类算法对OSA表型进行了定义 多方面的OSA数据。(3)新发现的OSA表型，预测患者受益于OSA 治疗和不良心血管结局的风险，为OSA表型在靶向的临床应用奠定了基础 治疗和准确的预后。然而，在充分利用OSA数据方面存在着重大差距 表型发现：缺乏“结果预测性”、“临床可解释性”和“可重复性”。 表型，根据大量不同美国人群的多域OSA数据定义。 为了解决这些差距，我们提出了一个次要的多项研究分析，试图开发新的 通过整合多领域OSA相关睡眠共同特征来确定OSA的分类标准和表型 数据元素，包括但不限于患者的社会人口统计、健康习惯、病史、 人体测量学、多导睡眠图测量、日间嗜睡、生活质量和心血管 三个最大的流行病学研究队列的合并症和死亡率，存放在 NIH资助的国家睡眠研究资源(NSRR)。这包括睡眠心脏健康研究，西班牙裔 社区健康研究和动脉粥样硬化的多种族研究，至少有5336名OSA患者来自 非裔美国人、高加索人、西班牙人和亚裔美国人的不同人群。 目的1：建立一种新的OSA表型稀疏、预测结果的多域因素混合模型 从多个领域辨别混合型患者的临床前特征和临床特征。目标2：应用 在目标1中为单独和汇集的NSRR数据集开发了模型，以：(1)识别、表征和验证 OSA表型；(2)评估由个人和集合支持的研究结果的一致性和重复性 分析。影响：我们将识别、表征和验证OSA表型，以帮助临床医生 确定治疗计划的积极程度，并协助研究人员选择合适的治疗方案 患者参加OSA治疗的临床试验，最终导致OSA的准确预后和治疗。