Dynamic driving pressure predicts ventilator-induced lung injury in mice with and without endotoxin-induced acute lung injury.

IF 3.6 2区 医学 Q1 PHYSIOLOGY American journal of physiology. Lung cellular and molecular physiology Pub Date : 2025-01-01 Epub Date: 2024-11-27 DOI:10.1152/ajplung.00176.2024
Alison Wallbank, Alexander Sosa, Andrew Colson, Huda Farooqi, Elizabeth Kaye, Katharine Warner, David J Albers, Peter D Sottile, Bradford J Smith
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Abstract

Mechanical ventilation (MV) is a necessary lifesaving intervention for patients with acute respiratory distress syndrome (ARDS) but it can cause ventilator-induced lung injury (VILI), which contributes to the high ARDS mortality rate (∼40%). Bedside determination of optimally lung-protective ventilation settings is challenging because the evolution of VILI is not immediately reflected in clinically available, patient-level, data. The goal of this work was therefore to test ventilation waveform-derived parameters that represent the degree of ongoing VILI and can serve as targets for ventilator adjustments. VILI was generated at three different positive end-expiratory pressures in a murine inflammation-mediated (lipopolysaccharide, LPS) acute lung injury model and in initially healthy controls. LPS injury increased the expression of proinflammatory cytokines and caused widespread atelectasis, predisposing the lungs to VILI as measured in structure, mechanical function, and inflammation. Changes in lung function were used as response variables in an elastic net regression model that predicted VILI severity from tidal volume, dynamic driving pressure (PDDyn), mechanical power calculated by integration during inspiration or the entire respiratory cycle, and power calculated according to Gattinoni' s equation. Of these, PDDyn best predicted functional outcomes of injury using either data from the entire dataset or from 5-min time windows. The windowed data show higher predictive accuracy after an ∼1-h "run in" period and worse accuracy immediately following recruitment maneuvers. This analysis shows that low driving pressure is a computational biomarker associated with better experimental VILI outcomes and supports the use of driving pressure to guide ventilator adjustments to prevent VILI.NEW & NOTEWORTHY Elastic net regression analysis of ventilation waveforms recorded during mechanical ventilation of initially healthy and lung-injured mice shows that low driving pressure is a computational biomarker associated with better ventilator-induced lung injury (VILI) outcomes and supports the use of driving pressure to guide ventilator adjustments to prevent VILI.

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动态驱动压力可预测有内毒素诱发和无内毒素诱发急性肺损伤的小鼠呼吸机诱发的肺损伤。
机械通气是拯救急性呼吸窘迫综合征(ARDS)患者生命的必要干预措施,但它可能导致呼吸机诱发肺损伤(VILI),从而导致 ARDS 死亡率居高不下(≈40%)。床旁确定最佳肺保护通气设置具有挑战性,因为 VILI 的演变并不能立即反映在临床可用的患者层面数据中。因此,这项工作的目标是测试通气波形衍生参数,这些参数可代表正在发生的 VILI 的程度,并可作为呼吸机调整的目标。在小鼠炎症介导的(脂多糖,LPS)急性肺损伤模型和初始健康对照组中,在三种不同的呼气末正压下产生了 VILI。LPS 损伤增加了促炎细胞因子的表达,并导致广泛的肺不张,从而使肺部结构、机械功能和炎症更易发生 VILI。肺功能的变化被用作弹性网回归模型中的响应变量,该模型通过潮气量、动态驱动压力(PDDyn)、吸气或整个呼吸周期中整合计算的机械功率以及根据加蒂诺尼方程计算的功率来预测 VILI 的严重程度。其中,PDDyn 使用整个数据集或 5 分钟时间窗的数据对损伤功能结果的预测效果最佳。窗口数据显示,经过≈1 小时的 "磨合期 "后,预测准确性更高,而在招募动作后立即进行预测的准确性则更低。该分析表明,低驱动压力是一种计算生物标志物,与较好的 VILI 实验结果相关,并支持使用驱动压力来指导呼吸机调整以预防 VILI。
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来源期刊
CiteScore
9.20
自引率
4.10%
发文量
146
审稿时长
2 months
期刊介绍: The American Journal of Physiology-Lung Cellular and Molecular Physiology publishes original research covering the broad scope of molecular, cellular, and integrative aspects of normal and abnormal function of cells and components of the respiratory system. Areas of interest include conducting airways, pulmonary circulation, lung endothelial and epithelial cells, the pleura, neuroendocrine and immunologic cells in the lung, neural cells involved in control of breathing, and cells of the diaphragm and thoracic muscles. The processes to be covered in the Journal include gas-exchange, metabolic control at the cellular level, intracellular signaling, gene expression, genomics, macromolecules and their turnover, cell-cell and cell-matrix interactions, cell motility, secretory mechanisms, membrane function, surfactant, matrix components, mucus and lining materials, lung defenses, macrophage function, transport of salt, water and protein, development and differentiation of the respiratory system, and response to the environment.
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