Over the past decades, hemodynamics simulators have steadily evolved and have become tools of choice for studying cardiovascular systems in-silico. While such tools are routinely used to simulate whole-body hemodynamics from physiological parameters, solving the corresponding inverse problem of mapping waveforms back to plausible physiological parameters remains both promising and challenging. Motivated by advances in simulation-based inference (SBI), we cast this inverse problem as statistical inference. In contrast to alternative approaches, SBI provides \textit{posterior distributions} for the parameters of interest, providing a \textit{multi-dimensional} representation of uncertainty for \textit{individual} measurements. We showcase this ability by performing an in-silico uncertainty analysis of five biomarkers of clinical interest comparing several measurement modalities. Beyond the corroboration of known facts, such as the feasibility of estimating heart rate, our study highlights the potential of estimating new biomarkers from standard-of-care measurements. SBI reveals practically relevant findings that cannot be captured by standard sensitivity analyses, such as the existence of sub-populations for which parameter estimation exhibits distinct uncertainty regimes. Finally, we study the gap between in-vivo and in-silico with the MIMIC-III waveform database and critically discuss how cardiovascular simulations can inform real-world data analysis.

翻译：过去几十年来，血流动力学模拟器稳步发展，已成为研究心血管系统在体数值模拟的首选工具。虽然此类工具常被用于根据生理参数模拟全身血流动力学，但解决将波形映射回合理生理参数的相应逆问题仍兼具前景与挑战。受基于模拟的推理（SBI）进展的启发，我们将该逆问题表述为统计推断。与其他方法相比，SBI能为目标参数提供“后验分布”，从而为“单次”测量提供“多维”不确定性表征。我们通过对五种临床重要生物标志物进行在体不确定性分析并比较多种测量模态，展示了这一能力。除了验证已知事实（如心率估计的可行性），我们的研究还揭示了从标准护理测量中估计新型生物标志物的潜力。SBI揭示了标准敏感性分析无法捕捉的具有实际意义的发现，例如存在某些亚群，其参数估计呈现不同的不确定性模式。最后，我们利用MIMIC-III波形数据库研究了在体与在体数值模拟之间的差距，并批判性地讨论了心血管模拟如何为现实世界数据分析提供信息。