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Predicting Pulmonary Distension in a Virtual Patient Model for Mechanical Ventilation

机译：预测机械通气虚拟患者模型中的肺部差异

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Recruitment maneuvers (RMs) following with positive-end-expiratory-pressure (PEEP) have proved effective in recruiting lung volume and preventing alveoli collapse. To date, standards for optimal patient-specific PEEP are unknown, resulting in variability in care and reduced outcomes, both indicating the need for personalized care. This research extends a well-validated virtual patient model by adding novel elements to model, which is able to utilize bedside available respiratory data, without increasing modelling complexity, to predict patient-specific lung distension and thus to minimise barotrauma risk. Prediction accuracy and robustness are validated against clinical data from 18 volume controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0 to 12cmH2O), where predictions were made up to 12cmH2O of PEEP ahead. Using an exponential basis function set for prediction yields an absolute median peak inspiratory pressure prediction error of 1.50cmH2O for 623 prediction cases. Comparing predicted and clinically measured distension prediction in VCV demonstrated consistent, robust high accuracy with R2=0.90 (623 predictions), which is a measurable improvement in prediction error compared to predictions without using the proposed distension function (R2=0.82). Moreover, the R2value increases to 0.93-0.95 if only clinically relevant ΔPEEP steps (2-6cmH2O) are considered with an overall median absolute error in peak pressure prediction of 1.04cmH2O. Overall, the results demonstrate the potential and significance for accurately capturing distension mechanics, allowing better risk assessment, as well as extending and more fully validating this virtual mechanical ventilation patient model.
机译：患有正终端呼气压力（PEEP）的招聘机动（RMS）已经证明有效地募集肺部体积并防止肺泡塌陷。迄今为止，最佳患者特异性窥视标准是未知的，导致谨慎和降低结果，这两者都表明需要个性化护理。该研究通过向模型添加新颖的元素来扩展良好的虚拟患者模型，该模型能够利用床侧可用的呼吸系统数据，而不会增加模型复杂性，以预测患者特异性的肺部，因此可以最大限度地减少巴罗拉姆风险。预测准确性和鲁棒性与来自18个卷控制通气（VCV）患者的临床数据验证，7种不同的基线窥视水平（0到12cmH2O），其中预测到前方的偷窥12CMH2O。使用指数基函数设置用于预测，产生1.50cmH2O的绝对中值峰值吸气压力预测误差为623个预测情况。与R2 = 0.90（623个预测）的VCV中的预测和临床测量的扩散预测相一致，稳健的高精度，其是与预测相比预测误差的可测量改善，而不使用所提出的扩散函数（R2 = 0.82）。此外，如果仅考虑临床相关的ΔPeep步骤（2-6cmH2O），则R2Value增加到0.93-0.95。在1.04cmh2o的峰值压力预测中的整体中值绝对误差中考虑了临床相关的Δpeep步骤（2-6cmh2o）。总体而言，结果表明了准确地捕获扩散力学的潜在和意义，允许更好的风险评估，以及扩展和更全面地验证这种虚拟机械通风患者模型。

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