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Minimal Lung Mechanics Basis-functions for a Mechanical Ventilation Virtual Patient

机译：机械通风虚拟病人的最小肺部力学基础功能

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Mechanical ventilation (MV) is used in the intensive care unit (ICU) to treat patients with respiratory failure. However, MV settings are not standardized due to significant inter- and intra- patient variability in response to care, leading to variability in care, outcome, and cost. There is thus a need to personalize MV. This research extends a single compartment lung mechanics model with physiologically relevant basis functions, to identify patient-specific lung mechanics and predict response to changes in MV care. The nonlinear evolution of pulmonary elastance as positive-end-expiratory pressure (PEEP) changes is captured by a physiologically relevant, simplified compensatory equation as a function of PEEP and pressure identification error at the baseline PEEP level. It allows both patient-specific and general prediction of lung elastance of higher PEEP. The prediction outcome is validated with data from two volume-controlled ventilation (VCV) trials and one pressure-controlled ventilation (PCV) trial, where the biggest PEEP prediction interval is a clinically unrealistic 20cmH2O, comprising 210 prediction cases over 36 patients (22 VCV; 14 PCV). Predicted absolute peak inspiratory pressure (PIP) errors are within 1.0cmH2O and 3.3cmH2O for 90% cases in the two VCV trials, while predicted peak inspiratory tidal volume (PIV) errors are within 0.073L for 85% cases in studied PCV trial. The model presented provides a highly accurate, predictive virtual patient model across multiple MV modes and delivery methods, and over clinically unrealistically large changes. Low computational cost, and fast, easy parameterization enable model-based, predictive decision support in real-time to safely personalize and optimize MV care.
机译：机械通风（MV）用于重症监护室（ICU），治疗呼吸衰竭患者。然而，由于响应护理的显着和患者的内部变异性而导致MV设置没有标准化，导致护理，结果和成本的可变性。因此需要个性化MV。该研究延伸了一种具有生理相关基础功能的单个隔间肺部力学模型，识别患者特异性肺部力学并预测对MV护理变化的反应。作为正端呼气压力（PEEP）变化的肺弹性的非线性演变是通过生理学相关的简化补偿方程来捕获的，作为基线窥视水平的PEEP和压力识别误差的函数。它允许较高窥视肺弹性的患者特异性和一般预测。预测结果与来自两个体积控制通气（VCV）试验的数据和一个压力控制的通气（PCV）试验，其中最大的窥视预测间隔是临床上不切实际的20cmH2O，包括超过36例患者的210例预测案例（22个VCV ; 14 pcv）。预测的绝对峰值吸气压力（PIP）误差在两个VCV试验中为90％和3.3CMH2O，而预测的峰值吸气潮气体积（PIV）误差是在0.073L的情况下进行的85％的PCV试验。呈现的模型在多种MV模式和递送方法以及临床上不切实际的变化中提供了高度准确的预测虚拟患者模型。低计算成本，快速，易于参数化，可以实时实现基于模型的预测决策支持，以安全地个性化和优化MV护理。

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