Wallbank Alison, Sosa Alexander, Colson Andrew, Farooqi Huda, Kaye Elizabeth, Warner Katharine, Albers David J, Sottile Peter D, Smith Bradford J
Department of Bioengineering, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colorado, United States.
Department of Biomedical Informatics, University of Colorado, Anschutz Medical Campus, Aurora, Colorado, United States.
Am J Physiol Lung Cell Mol Physiol. 2025 Jan 1;328(1):L159-L175. doi: 10.1152/ajplung.00176.2024. Epub 2024 Nov 27.
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 (PD), mechanical power calculated by integration during inspiration or the entire respiratory cycle, and power calculated according to Gattinoni' s equation. Of these, PD 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. 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.
机械通气(MV)是急性呼吸窘迫综合征(ARDS)患者必要的挽救生命的干预措施,但它可导致呼吸机诱导的肺损伤(VILI),这是ARDS高死亡率(约40%)的一个原因。在床边确定最佳的肺保护性通气设置具有挑战性,因为VILI的演变并未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源自通气波形的参数,这些参数代表正在发生的VILI的程度,并可作为呼吸机调整的目标。在小鼠炎症介导(脂多糖,LPS)急性肺损伤模型和初始健康的对照中,在三个不同的呼气末正压水平下产生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,使肺在结构、机械功能和炎症方面易于发生VILI。肺功能的变化被用作弹性网络回归模型中的响应变量,该模型根据潮气量、动态驱动压力(PD)、吸气期间或整个呼吸周期积分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严重程度。其中,PD使用整个数据集或5分钟时间窗的数据,对损伤的功能结果预测最佳。加窗数据显示,在约1小时的“磨合”期后预测准确性更高,而在肺复张操作后立即准确性更差。该分析表明,低驱动压力是一种与更好的实验性VILI结果相关的计算生物标志物,并支持使用驱动压力来指导呼吸机调整以预防VILI。对初始健康和肺损伤小鼠机械通气期间记录的通气波形进行弹性网络回归分析表明,低驱动压力是一种与更好的呼吸机诱导的肺损伤(VILI)结果相关的计算生物标志物,并支持使用驱动压力来指导呼吸机调整以预防VILI。