Prognostic & Therapeutic Implications of Breathing Patterns in Acute Lung Injury

描述(由申请人提供)： 理论基础：每年20万例急性肺损伤(ALI)导致7.5万人死亡。需要机械通气来治疗这些患者中的一部分，但会造成进一步的损伤，称为呼吸机相关性肺损伤(VALI)。这些患者通常需要在重症监护病房(ICU)接受护理，这造成了沉重的经济负担，因为重症监护消耗了美国国内生产总值(GDP)的约1%。目的：我们的长期目标包括：1)开发基于呼吸模式变异性的呼吸生物标记物，以识别高危患者。这些指标将有助于对需要高级护理的患者进行适当的分诊，从而提高资源利用率，并允许更快地启动治疗，将机械通气的需要降至最低；以及2)基于优化生物可变呼吸机(BVV)参数的新的呼吸机策略的开发，以限制呼吸模式的变化，最大限度地减少VALI，并改善呼吸机脱机。在目前的应用中，我们建议在经过充分验证的动物模型中进行临床前研究。虽然测量呼吸模式变异性的分析技术适用于人体研究，但在人类身上进行更广泛的研究之前，在没有共病和其他混杂变量的疾病模型中进行初步研究是最好的方法。最终，所获得的知识将被用于开发模式可变性的测量，这些测量将为人类风险评估提供诊断和预测信息，此外还将为呼吸机管理导致呼吸衰竭的心肺疾病的新方法奠定基础。研究设计和方法：本申请解决以下两个问题：1)呼吸模式变异性的测量能否预测ALI和VALI的疾病进展或消退；以及2)在逐个呼吸的基础上增加生物变异性是否保持了正常的呼吸模式变异性，使VALI最小化，从而促进成功地脱离呼吸机？目标1中的研究将直接解决这样的假设，即呼吸模式变异性的基本结构的变化反映了肺损伤的严重程度，并预测了肺损伤的进展或缓解。将使用已建立和验证的肺损伤的啮齿动物模型(博莱霉素滴注和高潮气量呼吸机)。实验将把呼吸的时间模式与肺损伤类别联系起来，涉及机制和临床影响。研究结果将被用于开发诊断肺损伤和预测病情缓解的多变量模型。受试者工作特征(ROC)曲线分析将用于比较不同呼吸模式变异性指标的诊断准确性。目标2中的实验将确定包括生物可变性在内的机械通风策略是否可以限制呼吸衰竭的持续时间，并改善呼吸机的脱机。在VALI和先前存在的化学诱导的啮齿动物ALI的情况下，BVV将与传统的机械通气进行比较。结果衡量标准将包括撤除呼吸机支架后保持足够的自主通风的能力，以及撤机后肺损伤的严重程度和恢复基线呼吸模式的能力。意义：拟议的研究是创新的，因为人们没有意识到ALI和VALI引起的呼吸模式变异性的变化，而且以前还没有将复杂的系统分析方法应用于这些重要的临床情况。我们预计这些研究的结果将导致新的通风策略的开发，其中包括生物变异性，并将指导与疾病严重程度和预后相关的新生理学标记物的开发。虽然这些方法最初应用于ICU，但将很容易应用于其他环境，以确定需要更高水平的护理或更密集治疗的患者。

DESCRIPTION (provided by applicant): Rationale: Each year 200,000 cases of Acute Lung Injury (ALI) result in 75,000 deaths. Mechanical ventilation is needed to manage a subset of these patients but causes further injury termed ventilator- associated lung injury (VALI). Often these patients require care in an intensive care unit (ICU), contributing to a heavy economic burden as critical care consumes approximately 1% of the U.S. gross domestic product. Objectives: Our long-term goals include: 1) development of respiratory biomarkers based on breathing pattern variability that identify high-risk patients. These indices would facilitate appropriate triage of patients who need advanced care thereby improving resource utilization, as well as allow more rapid initiation of therapies that could minimize the need for mechanical ventilation; and 2) development of novel ventilatory strategies based on optimizing the parameters of biologically variable ventilation (BVV) to limit changes in breathing patterns, minimize VALI and improve ventilator weaning. In the present application we propose pre- clinical studies in well-validated animal models. While the analytical techniques for measuring breathing pattern variability are amenable to human studies, preliminary study in disease models free of co-morbidities and other confounding variables is the best approach before undertaking more extensive studies in humans. Ultimately, knowledge gained will be used to develop measures of pattern variability that will provide diagnostic and predictive information for risk assessment in humans, in addition to forming the foundation for new approaches to ventilator management of cardiopulmonary diseases leading to respiratory failure. Research Design and Methods: The present application addresses the following two questions: 1) can measurements of breathing pattern variability predict disease progression or regression in ALI and VALI; and 2) does the addition of biological variability on a breath-by-breath basis maintain normal breathing pattern variability, minimize VALI, and thereby promote successful weaning from the ventilator? Studies in Aim 1 will directly address the hypothesis that changes in the underlying structure of breathing pattern variability reflect the severity of lung injury, and are predictive of progression or resolution of lung injury. Well established and validated rodent models of lung injury (bleomycin-instillation and high tidal volume ventilation) will be used. Experiments will correlate temporal patterns of breathing with lung injury categories related to mechanism and clinical impact. Findings will be used to develop multivariate models for diagnosing lung injury and predicting resolution. Receiver operating characteristic (ROC) curve analysis will be used to compare the diagnostic accuracy of different indices of breathing pattern variability. Experiments in Aim 2 will determine if a strategy of mechanical ventilation that includes biologic variability can limit the duration of respiratory failure and improve ventilator weaning. BVV will be compared to conventional mechanical ventilation in the settings of VALI and pre-existing chemically-induced ALI in rodents. Outcome measures will include ability to maintain adequate spontaneous ventilation after removal of ventilator support, as well as lung injury severity and return of baseline breathing patterns after weaning from the ventilator. Significance: The proposed research is innovative as changes in variability of respiratory patterns with ALI and VALI are not appreciated, and a complex systems analysis approach has not previously been applied to these important clinical conditions. We expect the outcomes of these studies will lead to the development of new ventilation strategies that incorporate biological variability, and will guide the development of novel physiologic markers that correlate with illness severity and outcome. While initially applied in the ICU, these approaches will be easily applied in other settings identifying patients in need of a higher level of care or more intensive therapy.