描述使用压力支持通气的患者吸气努力动态模型的普遍性

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS
Jennifer L. Knopp , Yeong Shiong Chiew , Dimitrios Georgopoulos , Geoffrey M. Shaw , J. Geoffrey Chase
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引用次数: 0

摘要

机械通气(MV)是重症监护病房(ICU)的重要治疗手段。辅助自主呼吸(ASB)机械通气模式是机械通气护理的一个重要组成部分,并且在不断发展壮大,因为这种模式所需的镇静剂较少,而且有助于避免肌肉萎缩。同样,由于缺乏标准化的 MV 护理方法,基于模型的方法也随之兴起,但这些方法通常无法估计自主呼吸(SB)的力度,因此无法用于辅助自主呼吸 MV。为解决这一问题,已创建了多个自主呼吸强度模型,但这些模型需要专门添加传感器和/或操作。这项研究利用了一个独特的临床观察数据集,其中包括食管和胃压测量数据,这些数据通常不在重症监护室对 N=6 名患者进行测量。通过这些测量数据,可以更直接地根据模型估算 SB MV 患者的肌肉压力。对所有 6 名患者的数据进行了分析,并建立了 3 个模型,其中包括当前模型中常见的动态模型。根据模型拟合度对模型进行评估。结果显示,所有 3 个模型都无法捕捉到 2 名患者的动态变化,原因是他们的呼吸中增加了肌肉动态变化,违反了模型动态变化或约束条件中的假设。这些结果表明需要更灵活的模型和相关识别方法来更好地捕捉这些动态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ubiquity of models describing inspiratory effort dynamics in patients on pressure support ventilation

Mechanical Ventilation (MV) is an important therapy in the intensive care unit (ICU). Assisted spontaneous breathing (ASB) MV modes are a key and growing part of MV care, as they require less sedation and help avoid muscle atrophy. Equally, a lack of standardised approaches to MV care has led to the rise of model-based methods, which typically cannot estimate spontaneous breathing (SB) efforts, and are thus not able to be used for ASB MV. To address this issue, several models of SB effort have been created, though they require specialised added sensors and/or maneuvers. ►This research utilises a unique observational clinical dataset, which includes esophageal and gastric pressure measurements not typically taken in the ICU for N=6 patients. These measurements enable more direct model-based estimation of muscular pressure effort in SB MV patients. The data is analysed for all 6 patients for 3 models which include dynamics common to the current models. Models are assessed based on model fit. ►The results show all 3 models are unable to capture dynamics in 2 patients due to added muscular dynamics in their breathing, violating assumptions in the model dynamics or constraints. These results indicate the need for more flexible models and associated identification methods to better capture these dynamics.

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来源期刊
IFAC Journal of Systems and Control
IFAC Journal of Systems and Control AUTOMATION & CONTROL SYSTEMS-
CiteScore
3.70
自引率
5.30%
发文量
17
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