Centre for Bioengineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand.
Centre for Bioengineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand; School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, Subang Jaya, Selangor 47500, Malaysia.
Comput Methods Programs Biomed. 2019 Apr;171:67-79. doi: 10.1016/j.cmpb.2016.09.011. Epub 2016 Sep 23.
Monitoring of respiratory mechanics is required for guiding patient-specific mechanical ventilation settings in critical care. Many models of respiratory mechanics perform poorly in the presence of variable patient effort. Typical modelling approaches either attempt to mitigate the effect of the patient effort on the airway pressure waveforms, or attempt to capture the size and shape of the patient effort. This work analyses a range of methods to identify respiratory mechanics in volume controlled ventilation modes when there is patient effort. The models are compared using 4 Datasets, each with a sample of 30 breaths before, and 2-3 minutes after sedation has been administered. The sedation will reduce patient efforts, but the underlying pulmonary mechanical properties are unlikely to change during this short time. Model identified parameters from breathing cycles with patient effort are compared to breathing cycles that do not have patient effort. All models have advantages and disadvantages, so model selection may be specific to the respiratory mechanics application. However, in general, the combined method of iterative interpolative pressure reconstruction, and stacking multiple consecutive breaths together has the best performance over the Dataset. The variability of identified elastance when there is patient effort is the lowest with this method, and there is little systematic offset in identified mechanics when sedation is administered.
在重症监护中，监测呼吸力学对于指导患者特定的机械通气设置至关重要。许多呼吸力学模型在存在可变患者努力的情况下表现不佳。典型的建模方法要么试图减轻患者努力对气道压力波形的影响，要么试图捕捉患者努力的大小和形状。这项工作分析了在存在患者努力的情况下，在容量控制通气模式下识别呼吸力学的一系列方法。使用 4 个数据集对模型进行了比较，每个数据集都有镇静前 30 次呼吸的样本，以及镇静后 2-3 分钟的样本。镇静会降低患者的努力，但在这段短时间内，肺部的机械特性不太可能发生变化。将有患者努力的呼吸循环和没有患者努力的呼吸循环的模型识别参数进行了比较。所有模型都有优点和缺点，因此模型选择可能特定于呼吸力学应用。然而，一般来说，迭代插值压力重建的组合方法，以及将多个连续呼吸叠加在一起，在数据集上的性能最佳。使用这种方法，当有患者努力时，识别出的弹性的可变性最低，并且当给予镇静时，识别出的力学几乎没有系统偏移。
