Tlałka Karolina, Saxton Harry, Halliday Ian, Xu Xu, Narracott Andrew, Taylor Daniel, Malawski Maciej
Sano Centre for Computational Medicine, Cracow, Poland.
Division of Clinical Medicine, University of Sheffield, Sheffield, United Kingdom.
PLoS Comput Biol. 2024 Dec 23;20(12):e1012377. doi: 10.1371/journal.pcbi.1012377. eCollection 2024 Dec.
The baroreflex is one of the most important control mechanisms in the human cardiovascular system. This work utilises a closed-loop in silico model of baroreflex regulation, coupled to pulsatile mechanical models with (i) one heart chamber and 36-parameters and (ii) four chambers and 51 parameters. We perform the first global sensitivity analysis of these closed-loop systems which considers both cardiovascular and baroreflex parameters, and compare the models with their respective unregulated equivalents. Results show the reduced influence of regulated parameters compared to unregulated equivalents and that, in the physiological resting state, model outputs (pressures, heart rate, cardiac output etc.) are most sensitive to parasympathetic arc parameters. This work provides insight into the effects of regulation and model input parameter influence on clinical metrics, and constitutes a first step to understanding the role of regulation in models for personalised healthcare.
压力反射是人体心血管系统中最重要的控制机制之一。这项工作利用了压力反射调节的闭环计算机模型,该模型与具有(i)一个心腔和36个参数以及(ii)四个心腔和51个参数的搏动力学模型相耦合。我们对这些闭环系统进行了首次全局敏感性分析,该分析考虑了心血管和压力反射参数,并将这些模型与其各自未调节的等效模型进行了比较。结果表明,与未调节的等效模型相比,调节参数的影响有所降低,并且在生理静息状态下,模型输出(压力、心率、心输出量等)对副交感神经弧参数最为敏感。这项工作深入了解了调节作用以及模型输入参数对临床指标的影响,并构成了理解调节在个性化医疗模型中的作用的第一步。