利用机器学习和食管压力检测呼吸机不同步和描绘呼吸机诱导肺损伤的可能性

P. Sottile, B.J. Smith, D. Albers, M. Moss
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引用次数: 0

摘要

理由:人们认识到呼吸机不同步(VD)可能传播呼吸机诱导的肺损伤(VILI)。然而,如果没有先进的监测,如测量食管压力(Pes),一些VD无法被检测出来,并且不知道哪些类型的VD会传播VILI。我们描述了在食管测压患者中使用机器学习(ML)自动检测VD,以量化VD,潮气量(VT)和跨肺驱动压之间的频率和相关性(ΔPdyn.tp)。方法:我们招募了42例伴有ARDS或ARDS危险因素的患者,包括COVID-19。XGBoost是一种机器学习算法,经过训练,它可以从3500次随机呼吸的训练集中,使用一对一的策略识别出7种类型的呼吸。我们比较了模型的敏感性和特异性,包括和不包括来自Pes的特征。最后,各VD类型与VT或ΔPdyn之间的关联。Tp采用单独的线性混合效应模型计算。时间相关的呼吸由患者嵌套并建模为随机效应,考虑到每个患者的重复测量和变化的肺力学。结果:患者中女性占37.5%,年龄52±15岁,初始P:F比为140±64,448,976次呼吸中有24.2%为不同步呼吸。正常被动呼吸(Nlp)、正常自发呼吸(Nls)、晚期反向触发呼吸(RTl)、反向触发双触发呼吸(DTr)、轻度流量受限呼吸(FLm)、重度流量受限呼吸(FLs)和早期终止呼吸(EVT)分别占所有呼吸的47.0%、28.7%、4.8%、3.7%、8.9%、4.2%和2.5%。ML培训,VT和ΔPdyn。Tp结果如表所示(∗p<0.001)。结论:使用Pes可以训练ML算法来识别传统上需要Pes测量的VD类型,尽管没有Pes可能会降低灵敏度。VD多发,DTr与VT升高有关,FLm和FLs与ΔPdyn.tp升高有关。这些数据表明,双重触发呼吸和流量受限呼吸有可能传播VILI,而其他类型的VD可能没有那么有害。(表)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Utilizing Machine Learning and Esophageal Pressure to Detect Ventilator Dyssynchrony and Delineate the Potential for Ventilator Induced Lung Injury
Rationale: It is recognized that ventilator dyssynchrony (VD) may propagate ventilator induced lung injury (VILI). Yet some VD cannot be detected without advanced monitoring like measuring esophageal pressure (Pes) and it is unknown which types of VD propagate VILI. We describe the automated detection of VD using machine learning (ML) in patients with esophageal manometry to quantify the frequency and association between VD, tidal volume (VT) and transpulmonary driving pressure (ΔPdyn.tp). Methods: We enrolled 42 patients with ARDS or ARDS risk factors, including COVID-19. XGBoost, a ML algorithm, was trained to identify 7 types of breaths using a one-vs-all strategy, from a training set of 3500 random breaths. We compared the models' sensitivity and specificity with and without features derived from Pes. Finally, the association between each VD type and VT or ΔPdyn.tp was calculated using separate linear mixed-effect models. Temporally related breaths were nested by patient and modeled as random effects, accounting for repeat measures and changing pulmonary mechanics in each patient. Breaths without an adequate Pes signal were excluded from analysis Results: Patients were 37.5% female, 52±15 years old, had an initial P:F ratio of 140±64, and 24.2% of the 480, 976 breaths were dyssynchronous. Normal passive (Nlp), normal spontaneous (Nls), late reverse triggered (RTl), reverse triggered double triggered (DTr), mild flow limited (FLm), severe flow limited (FLs), and early ventilator terminated (EVT) account for 47.0%, 28.7%, 4.8%, 3.7%, 8.9%, 4.2%, and 2.5% of all breaths, respectively. ML training, VT and ΔPdyn.tp results are show in the table (∗p<0.001). Conclusion: ML algorithms can be trained using Pes to identify types of VD that traditionally need Pes measurements, although without Pes sensitivity may decrease. VD is frequent and DTr is associated with an increase in VT, while FLm and FLs are associated with an increased in ΔPdyn.tp. These data suggest that double triggered breaths and flow limited breaths have the potential to propagate VILI, while other types of VD may not be as deleterious. (Table Presented).
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