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.

项目摘要/摘要 尽管呼吸机管理取得了重大进展，但急性呼吸窘迫的死亡率合成 Drome（Ards）仍然无法接受。机械通气，具有较大的潮汐体积，高压 倾斜度和反复的肺泡塌陷会损伤肺部，称为呼吸机诱导的肺损伤（VILI）。定义 由于响应患者的努力，呼吸器异同（VD）是不适当的时机和呼吸的交付 可能会增强Vili。该提案概述了一项为期5年的培训编程，包括指导，正式教学法， 和实践研究经验，使Sottile博士为成功的临床研究工作定位 呼吸机异步（VD），其对呼吸机诱导的肺损伤（VILI）的影响及其最佳管理。 集成的课程将优化自动化VD检测算法，描述哪种类型的VD 有害，并确定理想的呼吸机和镇静策略，以最大程度地减少VD并改善 患者的结果。这种经验将为Sottile博士提供信号领导者的必要工具 分析，呼吸机异步，高级数学建模和重症监护研究。这个程序将 包括来自四名机器学习，机械通风，重症监护专家的正式指导 研究和动态系统的建模。此外，临床科学，机器学习和 高级数学建模将建立理论基础以应用这些技术。这个结构化 课程将帮助Sottile博士在实时呼吸机数据中对VD的计算机检测中获得专业知识， VD的病理生理以及复杂，时间动态的生物系统的建模。 正式课程将与三种实践经验相吻合。首先，Sottile博士将优化他的已经 开发的VD识别算法以检测可能对肺有害的其他类型的VD。第二， 他将确定哪种类型的VD与有害呼吸机的力学有关。最后，他会阻止 开采个性化呼吸机和镇静策略，以最大程度地减少VD，VILI和负面后果 使用线性优化和滞后线性相关性过度镇静，以说明患者的动态性质 生理。 拟议研究的结果将开发一种计算机化算法，以检测七种类型的VD， 确定哪种类型的VD最有可能传播Vili，并确定最佳呼吸机和 镇静策略，以改善个体患者的结果。这将使Sottile博士和研究团队 定位进行随机临床试验，比较计算机预测的个性化呼吸机和SE- 与当前的护理标准相比，实施策略。如果成功的话，这将有可能改变 通过为个体患者调整基于基于的疗法的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