Breathing patterning in obstructive sleep apnea

阻塞性睡眠呼吸暂停的呼吸模式

• 批准号: 10662393
• 负责人: Anna Michelle May
• 金额: --
• 依托单位: LOUIS STOKES CLEVELAND VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10662393
• 关键词: Adherence; Affect; Age; Air Movements; Award; Behavioral; Biomedical Engineering; Breathing; Characteristics; Clinical; Clinical Data; Clinical Informatics; Communities; Complement; Complex; Computerized Medical Record; Cost Savings; Data; Data Collection; Data Science; Databases; Development; Devices; Diagnosis; Discrimination; Equipment; Evaluation; Foundations; Functional disorder; Funding; Goals; Health; Healthcare; Healthcare Systems; Home; Individual; Instruction; Intervention; Leadership; Life; Link; Location; Machine Learning; Measures; Medical History; Medicine; Mental Health; Mentors; Methods; Modeling; Modernization; Monitor; Obstructive Sleep Apnea; Outpatients; Patients; Pattern; Performance; Persons; Physiological; Plethysmography; Polysomnography; Procedures; Provider; Public Health Informatics; Qualifying; Records; Research; Research Personnel; Respiratory physiology; Risk; Signal Transduction; Sleep; Sleep Apnea Syndromes; Sleep Disorders; Sleep Disorders Therapy; Sleep Stages; Solid; Staging; System; Techniques; Testing; Tidal Volume; Time; Training; Translating; Translations; Validation; Variant; Veterans; age effect; awake; career; career development; clinical application; clinical care; clinical practice; comorbidity; cost effective; data management; demographics; design; experience; follow-up; health care service utilization; high risk; improved; indexing; innovation; interest; machine learning algorithm; meetings; model development; mortality; multidisciplinary; multimodality; patient oriented; physical conditioning; positive airway pressure; predictive modeling; research and development; respiratory; responsible research conduct; signal processing; skills; therapy adherence; translational study

Obstructive sleep apnea (OSA), the most common sleep disorder, is a major problem in the Veteran community that poses risks for decreased physical and mental health. The predominant treatment for OSA is positive airway pressure (PAP) therapy. While PAP therapy is highly effective, adherence is suboptimal with approximately 50% stopping therapy by year 2. Our group has shown that breathing patterning is predictive of long-term adherence, but the method used requires a 15-minute procedure while awake. The goal of this project is to refine breathing patterning metrics and enhance clinical integration by repurposing clinical data obtained as part of routine OSA evaluation (sleep studies) and treatment (PAP machine data). This retrospective analysis will first apply an already-developed breathing pattering metric to be more applicable for both in-lab polysomnography and home sleep apnea testing. Comparison of the breathing patterning metric in sleep and wake will determine applicability for home sleep apnea testing, which does not gather sleep staging data. Because PAP machines only record flow, we will also evaluate whether the breathing patterning metric has similar performance characteristics in different respiratory signals (plethysmography and flow) for predicting adherence by comparing concordance index. We will then evaluate for the optimal breathing patterning metric and machine learning algorithm based on accuracy in predicting PAP adherence. Breathing patterning can be measured in multiple ways; to optimize adherence, a panel of breathing patterning metrics will be evaluated. Multiple machine learning algorithms will be compared to determine which has the best discrimination for predicting PAP adherence. Breathing patterning changes with age will be evaluated. In addition, we will evaluate the added utility of breathing patterning to a model that predicts adherence using patient demographics, past medical history, and sleep study summary data. This project could provide a practical, cost-effective method to identify patients that are likely to become nonadherent. My short-term career goal is to develop a foundation of focused research in optimizing obstructive sleep apnea (OSA) treatment via patient-centered solutions that will inform my clinical practice. To fulfill this goal, the developed research and career development goals of this CDA-2 project complement each other. Additional instruction in modern machine learning techniques will allow for adherence model development and validation. Coursework in clinical informatics will assist me in optimizing the design and data collection from the VA electronic medical record. Instruction on managing research records will help with managing the large databases necessary for this project. Proposed training in research team leadership and responsible conduct of research is particularly relevant to a transdisciplinary project such as this and will have long-term career benefits. Over the course of this award, I will develop skills that will further my long-term career goal to transition to an independent investigator with national funding. Both research and career development goals will be overseen by my highly-qualified mentors. Dr. Strohl, an expert in respiratory physiology and OSA, will be my primarily research mentor. Dr. Strohl has successfully mentored numerous researchers to independence. Dr. Jacono studies breathing pattern variability and will act as my primary career mentor. Regular meeting with my mentors will allow us to plan, troubleshoot, and assess progress towards an independent research career. This CDA-2 will provide me with the necessary support for a solid start towards my long-term research goal of exploring relationships between sleep disorders, therapy, and co-morbidities through multidisciplinary translational studies.

阻塞性睡眠呼吸暂停（OSA）是最常见的睡眠障碍，是退伍军人的主要问题 构成降低身心健康的风险的社区。 OSA的主要治疗是 阳性气道压力（PAP）治疗。虽然PAP疗法非常有效，但依从性 到2年级，大约有50％的停止治疗。我们的小组表明呼吸模式是预测的 长期依从性，但是所使用的方法需要在清醒时进行15分钟的程序。目标的目标 项目是通过重新利用临床数据来完善呼吸模式指标并增强临床整合 作为常规OSA评估（睡眠研究）和治疗（PAP机数据）的一部分获得。 这种回顾性分析将首先应用已发达的呼吸模式指标 适用于LAB中的多个术语和家庭睡眠呼吸暂停测试。呼吸的比较 睡眠和唤醒中的图案指标将确定家庭睡眠呼吸暂停测试的适用性，这不是 收集睡眠分期数据。由于PAP机只记录流量，我们还将评估是否 呼吸模式指标在不同的呼吸信号中具有相似的性能特征 通过比较一致性指数来预测依从性，以预测依从性。然后我们将评估 最佳呼吸图案度量和机器学习算法基于预测的准确性 PAP依从性。呼吸模式可以通过多种方式进行测量；为了优化依从性，一个面板 将评估呼吸模式指标。将多个机器学习算法与 确定哪些具有预测PAP依从性的最佳歧视。呼吸模式变化 年龄将进行评估。此外，我们将评估呼吸模式的附加功能 使用患者人口统计学，过去的病史和睡眠研究摘要数据来预测依从性。这 项目可以提供一种实用，成本效益的方法，以识别可能成为可能成为的患者 不遵守。 我的短期职业目标是为优化阻塞性睡眠而建立重点研究的基础 呼吸暂停（OSA）通过以患者为中心的解决方案进行治疗，这将为我的临床实践提供信息。为了实现这一目标， 这项CDA-2项目的研究和职业发展目标相互补充。额外的 现代机器学习技术中的指导将允许依从性模型开发和验证。 临床信息学课程将有助于我优化VA的设计和数据收集 电子病历。管理研究记录的指导将有助于管理大型 该项目所需的数据库。在研究团队领导和负责任的行为方面提议的培训 研究的尤其与这样的跨学科项目特别相关，并且将具有长期职业 好处。在这个奖项的过程中，我将发展技能，使我的长期职业目标进一步 过渡到国家资助的独立调查员。 研究和职业发展目标都将由我的高度合格的导师监督。斯特罗尔博士， 呼吸生理学和OSA专家将是我的主要研究导师。斯特罗尔博士成功 指导了许多研究人员独立。 Jacono博士研究呼吸模式的变异性，并将行动 作为我的主要职业导师。与我的导师的定期会面将使我们能够计划，故障排除和评估 迈向独立研究职业。此CDA-2将为我提供必要的支持 我的长期研究目标是探索睡眠障碍，治疗， 和通过多学科翻译研究的合并症。

项目成果

Sleep-disordered Breathing and Inpatient Outcomes in Nonsurgical Patients: Analysis of the Nationwide Inpatient Cohort.

非手术患者的睡眠呼吸障碍和住院结果：全国住院患者队列分析。

• DOI: 10.1513/annalsats.202305-469oc
• 发表时间: 2023
• 期刊: Annals of the American Thoracic Society
• 影响因子: 8.3
• 作者: May,AnnaM

Racial Differences in Positive Airway Pressure Adherence in the Treatment of Sleep Apnea.

• DOI: 10.1016/j.jsmc.2022.07.002
• 发表时间: 2022-12
• 期刊: SLEEP MEDICINE CLINICS
• 影响因子: 2.8
• 作者: May, Anna M.;Billings, Martha E.
• 通讯作者: Billings, Martha E.

Improving Referral Patterns for Bronchoscopic Lung Volume Reduction: A Quality Improvement Initiative.

改善支气管镜肺减容术的转诊模式：一项质量改进计划。

• DOI: 10.15326/jcopdf.2023.0397
• 发表时间: 2024
• 期刊: Chronic obstructive pulmonary diseases (Miami, Fla.)
• 作者: DiFelice,Christopher;Strumpf,ZacharyB;Edmiston,ElizabethA;CuvillierPadilla,ChristianF;Ellis-Jones,LeahC;McKell,JoanneL;Shatat,MohammadA;Williams,SherrieD;May,AnnaM
• 通讯作者: May,AnnaM

Do Interviews Really Matter in Generating Programs and Applicants' Rank Lists for the Match?

面试对于生成比赛项目和申请人排名列表真的很重要吗？

• DOI: 10.14423/smj.0000000000001384
• 发表时间: 2022-04
• 期刊: Southern medical journal
• 影响因子: 1.1