Modeling Mechanical Ventilation In Silico-Potential and Pitfalls.
Modeling Mechanical Ventilation In Silico-Potential and Pitfalls.
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DOI：
10.1055/s-0042-1744446
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发表时间：
2022-04
影响因子：
3.2
通讯作者：
David M. Hannon;Sonal Mistry;Anup Das;Sina Saffaran;J. Laffey;B. Brook;J. Hardman;D. Bates
中科院分区：
文献类型：
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作者：
David M. Hannon;Sonal Mistry;Anup Das;Sina Saffaran;J. Laffey;B. Brook;J. Hardman;D. Bates
计算机模拟为传统医学研究提供了一种新的方法，特别适合于调查与机械通气相关的问题。对接受机械通气的患者进行详细的常规监测，为模型设计和配置提供广泛的高质量数据流。基于这些数据的模型可以包含非常复杂的系统动力学，可以根据患者的反应进行验证，以用作研究替代品。至关重要的是，模拟提供了“看里面”的病人，允许不受阻碍地访问所有感兴趣的变量的潜力。与动物模型和人类患者的试验相比，计算机模拟模型是完全可配置和可再现的;例如，不同的呼吸机设置可以应用于相同的虚拟患者，或相同的设置应用于不同的患者，以了解其作用模式并定量比较其有效性。在这里，我们回顾了在机械通气的背景下，人体解剖学，生理学和病理生理学的数学建模和计算机模拟的进展，重点是这种方法在各种疾病状态的临床应用。我们提出了新的结果，强调模型的复杂性和预测能力之间的联系，使用数据的个体急性呼吸窘迫综合征患者的反应，在多种呼吸机设置的变化。从临床的角度讨论了目前的局限性和潜力，并强调了未来的挑战和研究方向。
Computer simulation offers a fresh approach to traditional medical research that is particularly well suited to investigating issues related to mechanical ventilation. Patients receiving mechanical ventilation are routinely monitored in great detail, providing extensive high-quality data-streams for model design and configuration. Models based on such data can incorporate very complex system dynamics that can be validated against patient responses for use as investigational surrogates. Crucially, simulation offers the potential to "look inside" the patient, allowing unimpeded access to all variables of interest. In contrast to trials on both animal models and human patients, in silico models are completely configurable and reproducible; for example, different ventilator settings can be applied to an identical virtual patient, or the same settings applied to different patients, to understand their mode of action and quantitatively compare their effectiveness. Here, we review progress on the mathematical modeling and computer simulation of human anatomy, physiology, and pathophysiology in the context of mechanical ventilation, with an emphasis on the clinical applications of this approach in various disease states. We present new results highlighting the link between model complexity and predictive capability, using data on the responses of individual patients with acute respiratory distress syndrome to changes in multiple ventilator settings. The current limitations and potential of in silico modeling are discussed from a clinical perspective, and future challenges and research directions highlighted.