Process Automation, ABB Limited, Eaton Socon, Cambridgeshire, UK.
Comput Methods Programs Biomed. 2010 Aug;99(2):195-207. doi: 10.1016/j.cmpb.2009.09.011. Epub 2009 Oct 27.
Arterial blood gas (ABG) analyses are essential for assessing the acid-base status and guiding the adjustment of mechanical ventilation in critically ill patients. Conventional ABG sampling requires repeated arterial punctures or the insertion of an arterial catheter causing pain, haemorrhage and thrombosis to the patients. Less invasive and non-invasive blood gas analysers, with a technology still in transition, have offered some promise in the recent years. SOPAVent (Simulation of Patients under Artificial Ventilation) is a five compartment blood gas model which captures the basic features of respiratory physiology and gas exchange in the human lungs. It uses ventilator settings and routinely monitored physiological parameters as inputs to produce steady-state estimates of the patient's ABG. This paper overviews the original SOPAVent model and presents an improved data-driven hybrid model that is patient-specific and gives continuous and totally non-invasive ABG predictions. The model has been comprehensively tested in simulations and validated using recorded measurements of ABG and ventilator parameters from ICU patients.
动脉血气（ABG）分析对于评估酸碱平衡状态和指导重症患者机械通气的调整至关重要。传统的 ABG 采样需要反复进行动脉穿刺或插入动脉导管，这会给患者带来疼痛、出血和血栓形成等问题。近年来，一些微创和非侵入性血气分析仪技术提供了一些希望，这些技术仍在不断发展中。SOPAVent（人工通气下患者模拟）是一个五腔血气模型，它捕捉了人类肺部呼吸生理和气体交换的基本特征。它使用呼吸机设置和常规监测的生理参数作为输入，生成患者 ABG 的稳态估计。本文概述了原始的 SOPAVent 模型，并提出了一种改进的数据驱动混合模型，该模型是针对特定患者的，并能提供连续的、完全非侵入性的 ABG 预测。该模型已在模拟中进行了全面测试，并使用来自 ICU 患者的 ABG 和呼吸机参数的记录测量进行了验证。
