The Detection, Quantification, and Management of Ventilator Dyssynchrony

呼吸机不同步的检测、量化和管理

• 批准号: 10545038
• 负责人: PETER D SOTTILE
• 金额: $ 16.59万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10545038
• 关键词: Accounting; Acute Respiratory Distress Syndrome; Admission activity; Algorithms; Alveolar; Biological Markers; Clinical; Clinical Data; Clinical Research; Clinical Sciences; Complex; Computers; Critical Care; Data; Detection; Dysbarism; Educational Curriculum; Endothelium; Epithelium; Esophagus; Foundations; Functional disorder; Individual; Induction of neuromuscular blockade; Injury; Lung; Machine Learning; Manometry; Mathematics; Measures; Mechanical Ventilators; Mechanical ventilation; Mechanics; Mentors; Modeling; Monitor; Nature; Outcome; Outcomes Research; Patient-Focused Outcomes; Patients; Physiology; Positioning Attribute; Prone Position; Research; Research Infrastructure; Research Personnel; Risk; Role; Sedation procedure; Signal Transduction; Structure; Surrogate Markers; System; Techniques; Technology; Tidal Volume; Time; Time Series Analysis; Training; Ventilator; Ventilator-induced lung injury; biological systems; career; computerized; dynamic system; esophagus pressure; evidence base; experience; improved; improved outcome; individual patient; interest; lung injury; lung pressure; machine learning algorithm; mathematical model; mortality; multidisciplinary; optimal treatments; personalized predictions; personalized strategies; pressure; programs; randomized, clinical trials; response; standard of care; tool; treatment strategy; trial comparing; ventilation

Project Summary/Abstract Despite significant advances in ventilator management, mortality from the acute respiratory distress syn- drome (ARDS) remains unacceptably high. Mechanical ventilation with large tidal volume, high pressure ven- tilation, and repeated alveolar collapse can injure the lung, called ventilator induced lung injury (VILI). Defined as the inappropriate timing and delivery of a breath in response to a patient effort, ventilator dyssynchrony (VD) may potentiate VILI. This proposal outlines a 5-year training programing including mentoring, formal didactics, and practical research experiences which positions Dr. Sottile for a successful clinical research career examining ventilator dyssynchrony (VD), its impact on ventilator induced lung injury (VILI), and its optimal management. An integrated curriculum will optimize an automated VD detection algorithm, delineate which types of VD are deleterious, and determine the ideal ventilator and sedation strategies to minimize VD and improve patient outcomes. This experience will provide Dr. Sottile with the necessary tools to be a leader in signal analysis, ventilator dyssynchrony, advanced mathematical modeling, and critical care research. This program will consist of formal mentoring from four renowned experts in machine learning, mechanical ventilation, critical care research, and modeling of dynamic systems. In addition, coursework in clinical sciences, machine learning, and advanced mathematical modeling will build the theoretical foundation to apply these techniques. This structured curriculum will help Dr. Sottile gain expertise in the computerized detection of VD from real-time ventilator data, the pathophysiology of VD, and the modeling of complex, temporally dynamic, biological systems. The formal curriculum will coincide with three practical experiences. First, Dr. Sottile will optimize his already developed VD identification algorithm to detect additional types of VD that may be injurious to the lung. Second, he will identify which types of VD are associated with deleterious ventilator mechanics. Finally, he will deter- mine the personalized ventilator and sedation strategies to minimize VD, VILI, and the negative consequences of over sedation using linear optimization and lagged linear correlation to account for the dynamic nature of patient physiology. The result of the proposed studies will develop a computerized algorithm to detect seven types of VD, identify which types of VD are most likely to propagate VILI, and determine the optimal ventilator and sedation strategies to improve individual patient outcomes. This will leave Dr. Sottile and the study team positioned to conduct a randomized clinical trial comparing computer-predicted individualized ventilator and se- dation strategies compared to the current standard of care. If successful, this will potentially to revolutionize the use of mechanical ventilation by tailoring evidenced-based therapies to the individual patient.

项目概要/摘要 尽管呼吸机管理取得了显着进步，但急性呼吸窘迫综合症导致的死亡率 drome (ARDS) 仍然高得令人无法接受。大潮气量、高压机械通气 呼吸机收缩和反复的肺泡塌陷会损伤肺部，称为呼吸机诱发性肺损伤 (VILI)。定义 由于呼吸机呼吸不同步（VD）对患者努力的反应不恰当 可能会增强 VILI。该提案概述了为期 5 年的培训计划，包括指导、正式教学、 和实践研究经验，使 Sottile 博士能够取得成功的临床研究职业生涯 呼吸机不同步（VD）、其对呼吸机诱发性肺损伤（VILI）的影响及其最佳管理。 综合课程将优化自动 VD 检测算法，描述哪些类型的 VD 是有害的，并确定理想的呼吸机和镇静策略，以最大限度地减少 VD 并改善 患者的结果。这一经验将为 Sottile 博士提供成为信号领域领导者所需的工具 分析、呼吸机不同步、高级数学建模和重症监护研究。该计划将 由四位机器学习、机械通气、重症监护领域知名专家的正式指导组成 动态系统的研究和建模。此外，还有临床科学、机器学习和 先进的数学建模将为应用这些技术奠定理论基础。这种结构化的 课程将帮助 Sottile 博士获得从实时呼吸机数据计算机化检测 VD 的专业知识， VD 的病理生理学，以及复杂的、时间动态的生物系统的建模。 正式课程将与三个实践经验相一致。首先，索蒂尔博士将优化他已经 开发了 VD 识别算法来检测可能损害肺部的其他类型的 VD。第二， 他将确定哪些类型的 VD 与有害的呼吸机机制相关。最后，他会阻止—— 制定个性化的呼吸机和镇静策略，以尽量减少 VD、VILI 以及以下疾病的负面后果 使用线性优化和滞后线性相关性来考虑患者的动态性质的过度镇静 生理。 拟议研究的结果将开发一种计算机算法来检测七种类型的 VD， 确定哪些类型的 VD 最有可能传播 VILI，并确定最佳的呼吸机和 改善个体患者治疗效果的镇静策略。这将使 Sottile 博士和研究团队离开 旨在进行一项随机临床试验，比较计算机预测的个体化呼吸机和自动呼吸机 与当前护理标准相比的数据策略。如果成功的话，这将有可能彻底改变 通过针对个体患者定制循证疗法来使用机械通气。

项目成果

Ventilator dyssynchrony - Detection, pathophysiology, and clinical relevance: A Narrative review.

• DOI: 10.4103/atm.atm_63_20
• 发表时间: 2020-10
• 期刊: Annals of thoracic medicine
• 影响因子: 2.3
• 作者: Sottile PD;Albers D;Smith BJ;Moss MM
• 通讯作者: Moss MM

Myorelaxants in ARDS patients.

• DOI: 10.1007/s00134-020-06297-8
• 发表时间: 2020-12
• 期刊: Intensive care medicine
• 影响因子: 38.9
• 作者: Hraiech S;Yoshida T;Annane D;Duggal A;Fanelli V;Gacouin A;Heunks L;Jaber S;Sottile PD;Papazian L
• 通讯作者: Papazian L

Real-time electronic health record mortality prediction during the COVID-19 pandemic: a prospective cohort study.

• DOI: 10.1093/jamia/ocab100
• 发表时间: 2021-10-12
• 期刊: Journal of the American Medical Informatics Association : JAMIA
• 作者: Sottile PD;Albers D;DeWitt PE;Russell S;Stroh JN;Kao DP;Adrian B;Levine ME;Mooney R;Larchick L;Kutner JS;Wynia MK;Glasheen JJ;Bennett TD
• 通讯作者: Bennett TD