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Identification of Asynchronous Effect via Pressure-Volume Loop Reconstruction in Mechanically Ventilated Breathing Waveforms

机译:机械通风呼吸波形压力量环重建识别异步效果

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Patient-specific lung-mechanics during mechanical ventilation (MV) can be modelled via using fully ventilated/controlled waveforms. However, patient asynchrony due to spontaneous breathing (SB) effort commonly exists in patients on full MV support, leading to variability in breathing waveforms and reducing the accuracy of identified, model-based, and patient-specific lung mechanics. This study aims to extract ventilated breathing waveforms from affected asynchronous breathing cycles using an automated virtual patient model-based approach. In particular, change of lung elastance over a pressure-volume (PV) loop is identified using hysteresis loop analysis (HLA) to detect the occurrence of asynchrony, as well as its type and pattern. The identified HLA parameters are then combined with a nonlinear mechanics hysteresis loop model (HLM) to extract and replicate the ventilated waveforms from the coupled asynchronous breaths. The magnitude of asynchrony can then be quantified using an energy dissipation metric,Easyn, comparing the area difference of PV loops between model-reconstructed and original breathing cycles. A proof-of-concept study is conducted using clinical data from 2700 breathing cycles of two patients exhibiting asynchrony during MV. The reconstruction root mean square errors are within 5-10% of the clinical data for 90% of the cycles, indicating good and robust reconstruction accuracy. Estimation ofEasynshows significant asynchrony magnitude for Patient 1 withEasyngreater than 10% for over 50% breaths, while asynchrony occurrence for Patient 2 is lower with 90% breaths atEasyn< 10%,which is a minimal asynchrony magnitude. These results match direct observation, thus validating the ability of the virtual patient model and methods presented to be used for a real-time monitoring of asynchrony with different types and magnitudes, which in turn would justify prospective clinical tests.
机译:机械通气期间的患者特异性肺部机械(MV)可以通过使用完全通风/控制的波形进行建模。然而,由于自发呼吸(SB)努力的患者患者通常存在于全部MV支撑件的患者中,导致呼吸波形的可变性以及降低鉴定,基于模型和患者特异性肺部力学的准确性。本研究旨在利用基于自动虚拟患者模型的方法从受影响的异步呼吸循环中提取通气呼吸波形。特别地,使用滞后回路分析(HLA)鉴定压力体积(PV)环上的肺弹性的变化以检测异步的发生,以及其类型和图案。然后将所识别的HLA参数与非线性力学滞后环模型(HLM)组合以从耦合的异步呼吸中提取和复制通气波形。然后可以使用能量耗散度量,易于对模型重建和原始呼吸循环之间的PV环的区域差来量化异步的大小。在MV期间使用来自2700名患者的2700名患者的呼吸循环进行概念性研究。重建根均方误差在临床数据的5-10%以内,为90%的循环,表明重建精度良好和稳健。估计患者1患者的大幅度大幅度超过10%超过50%的呼吸,而患者2的异步发生较低,令人抑制了90%的呼吸<10%,这是一个最小的异步幅度。这些结果与直接观察匹配,从而验证虚拟患者模型的能力和所提出的方法用于使用不同类型和大小的异步进行实时监测,这反过来是可以证明前瞻性临床测试。

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