Dysfunctional respiratory processing in patients with post-COVID? – An experimental-computational approach investigating the interaction between breathing behaviour and symptom perception

新冠肺炎后患者的呼吸处理功能失调？

• 批准号: 532480418
• 负责人: Professor Dr.-Ing. Stefan Glasauer
• 金额: --
• 依托单位: Klinik und Poliklinik für Psychosomatische Medizin und Psychotherapie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/532480418?language=en
• 关键词: Dysfunctional; respiratory; processing; patients; post

In the proposed study, we aim to develop objective, pathophysiological markers of breathlessness in post-COVID. We will combine experimental methods with computational modeling to investigate breathlessness and respiratory behavior, their interaction, and hypotheses about respiratory signal processing in post-COVID patients. We use an established rebreathing paradigm that allows determination of the interaction between breathlessness and physiological parameters. Previous studies have shown that in this rebreathing experiment, patients with organically unexplained breathlessness exhibit characteristic, measurable deviations in breathlessness compared to healthy individuals. These deviations occur only in specific contexts and indicate altered processing of respiratory information in the central nervous system (CNS). In the present study, we will use this paradigm to investigate whether such characteristic changes (in perceived breathlessness and/or in physiological respiratory parameters and patterns) occur in patients with post-COVID breathlessness, and whether they are altered by different contexts. The combination of an experimental approach and theoretical modeling of breathlessness at the level of individual patients allows us to test detailed hypotheses about the processing of respiratory signals in the development of breathlessness. For this purpose, we have developed a preliminary mathematical model of breathlessness processing. It already shows good results in predicting mean data from previous studies and initial pilot data from individual subjects using the rebreathing paradigm. Objective, pathophysiological markers are an important first step towards reducing stigma and individualized planning of diagnosis and therapy in patients with post-COVID.

在拟议的研究中，我们的目标是开发COVID后呼吸困难的客观病理生理学标志物。我们将联合收割机实验方法与计算建模相结合，以研究呼吸困难和呼吸行为，它们的相互作用，以及关于COVID后患者呼吸信号处理的假设。我们使用一个既定的再呼吸范例，可以确定呼吸困难和生理参数之间的相互作用。先前的研究表明，在这个重复呼吸实验中，与健康个体相比，器质性原因不明的呼吸困难患者表现出特征性的、可测量的呼吸困难偏差。这些偏差仅在特定情况下发生，表明中枢神经系统（CNS）中呼吸信息的处理发生了变化。在本研究中，我们将使用这一范式来研究COVID后呼吸困难患者是否会发生这种特征性变化（感知呼吸困难和/或生理呼吸参数和模式），以及它们是否会因不同的环境而改变。结合实验方法和理论建模的呼吸困难在个别患者的水平，使我们能够测试详细的假设呼吸信号的处理呼吸困难的发展。为此，我们已经开发了一个初步的呼吸困难处理的数学模型。它已经显示出良好的结果，预测平均数据从以前的研究和初始试点数据从个人科目使用再呼吸范例。目的，病理生理标志物是减少COVID后患者的耻辱感和个性化诊断和治疗计划的重要第一步。