Saxton Harry, Taylor Daniel J, Faulkner Grace, Halliday Ian, Newman Tom, Schenkel Torsten, Morris Paul D, Clayton Richard H, Xu Xu
School of Computer Science, University of Sheffield, Sheffield, United Kingdom.
Insigneo Institute for in-silico Medicine, University of Sheffield, Sheffield, United Kingdom.
PLoS One. 2025 Jun 24;20(6):e0326112. doi: 10.1371/journal.pone.0326112. eCollection 2025.
To employ a reduced-order cardiovascular model as a digital twin for personalised medicine, it is essential to understand how uncertainties in the model's input parameters affect its outputs. The aim is to identify a set of input parameters that can serve as clinical biomarkers, providing insight into a patient's physiological state. Given the challenge of finding useful clinical data, careful consideration must be given to the experimental design used to acquire patient-specific input parameters. Model sloppiness-where numerous parameter combinations have minimal impact on model predictions, whilst only a few parameters significantly influence outcomes-is a critical concept in this context. In this paper, we conduct the first quantification of a cardiovascular system's sloppiness to elucidate the structure of the input parameter space. By utilising Sobol indices and examining various synthetic cardiovascular measures with increasing invasiveness, we uncover how the personalisation process and the cardiovascular system's sloppiness are contingent upon the chosen experimental design. Our findings reveal that continuous clinical measures induce system sloppiness and increase the number of personalisable biomarkers, whereas discrete clinical measurements produce a non-sloppy system with a reduced number of biomarkers. This study underscores the necessity for careful consideration of available clinical data as differing measurement sets can significantly impact model personalisation.
为了将降阶心血管模型用作个性化医疗的数字孪生，必须了解模型输入参数的不确定性如何影响其输出。目的是确定一组可作为临床生物标志物的输入参数，从而深入了解患者的生理状态。鉴于获取有用临床数据的挑战，必须仔细考虑用于获取患者特定输入参数的实验设计。在这种情况下，模型松散性（即众多参数组合对模型预测影响极小，而只有少数参数对结果有显著影响）是一个关键概念。在本文中，我们首次对心血管系统的松散性进行量化，以阐明输入参数空间的结构。通过利用索伯尔指数并检查具有不同侵入性的各种合成心血管测量指标，我们揭示了个性化过程和心血管系统的松散性如何取决于所选的实验设计。我们的研究结果表明，连续临床测量会导致系统松散性并增加可个性化生物标志物的数量，而离散临床测量会产生一个生物标志物数量减少的非松散系统。这项研究强调了仔细考虑可用临床数据的必要性，因为不同的测量集可能会对模型个性化产生重大影响。
