Sun Qianhui, Chase J Geoffrey, Zhou Cong, Tawhai Merryn H, Knopp Jennifer L, Möller Knut, Shaw Geoffrey M
Department of Mechanical Engineering, Dept of Mechanical Eng, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand.
Department of Mechanical Engineering, Dept of Mechanical Eng, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand; School of Civil Aviation, Northwestern Polytechnical University, China.
Comput Biol Med. 2022 Feb;141:105022. doi: 10.1016/j.compbiomed.2021.105022. Epub 2021 Nov 11.
Recruitment maneuvers (RMs) with subsequent positive-end-expiratory-pressure (PEEP) have proven effective in recruiting lung volume and preventing alveolar collapse. However, a suboptimal PEEP could induce undesired injury in lungs by insufficient or excessive breath support. Thus, a predictive model for patient response under PEEP changes could improve clinical care and lower risks.
This research adds novel elements to a virtual patient model to identify and predict patient-specific lung distension to optimise and personalise care. Model validity and accuracy are validated using data from 18 volume-controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0-12cmHO), yielding 623 prediction cases. Predictions were made up to ΔPEEP = 12cmHO ahead covering 6x2cmHO PEEP steps.
Using the proposed lung distension model, 90% of absolute peak inspiratory pressure (PIP) prediction errors compared to clinical measurement are within 3.95cmHO, compared with 4.76cmHO without this distension term. Comparing model-predicted and clinically measured distension had high correlation increasing to R = 0.93-0.95 if maximum ΔPEEP ≤ 6cmHO. Predicted dynamic functional residual capacity (V) changes as PEEP rises yield 0.013L median prediction error for both prediction groups and overall R of 0.84.
Overall results demonstrate nonlinear distension mechanics are accurately captured in virtual lung mechanics patients for mechanical ventilation, for the first time. This result can minimise the risk of lung injury by predicting its potential occurrence of distension before changing ventilator settings. The overall outcomes significantly extend and more fully validate this virtual mechanical ventilation patient model.
采用后续呼气末正压（PEEP）的肺复张手法（RM）已被证明在增加肺容积和防止肺泡塌陷方面有效。然而，PEEP设置不当可能因呼吸支持不足或过度而对肺部造成不良损伤。因此，一个能预测患者在PEEP变化时反应的模型可以改善临床护理并降低风险。
本研究在虚拟患者模型中加入新元素，以识别和预测患者特异性的肺扩张情况，从而优化和个性化护理。使用18名接受容量控制通气（VCV）的患者在7种不同基线PEEP水平（0 - 12cmH₂O）下的数据对模型的有效性和准确性进行验证，共产生623个预测案例。预测提前至ΔPEEP = 12cmH₂O，涵盖6个2cmH₂O的PEEP步长。
使用所提出的肺扩张模型，与临床测量相比，90%的绝对吸气峰压（PIP）预测误差在3.95cmH₂O以内；若没有这个扩张项，该误差为4.76cmH₂O。比较模型预测和临床测量的扩张情况，相关性较高，若最大ΔPEEP≤6cmH₂O，相关性增加到R = 0.93 - 0.95。随着PEEP升高，预测的动态功能残气量（V）变化在两个预测组中的中位预测误差为0.013L，总体R为0.84。
总体结果首次表明，虚拟肺力学患者模型能够准确捕捉机械通气时的非线性扩张力学。这一结果可通过在改变呼吸机设置前预测潜在的扩张发生情况，将肺损伤风险降至最低。总体结果显著扩展并更全面地验证了这个虚拟机械通气患者模型。
