Blum Christopher, Steinseifer Ulrich, Neidlin Michael
Department of Cardiovascular Engineering, Institute of Applied Medical Engineering, Medical Faculty, RWTH Aachen University, Aachen, Germany.
Int J Numer Method Biomed Eng. 2025 May;41(5):e70040. doi: 10.1002/cnm.70040.
The purpose of this study is to address the lack of uncertainty quantification in numerical hemolysis models, which are critical for medical device evaluations. Specifically, we aim to develop a probabilistic hemolysis model, which incorporates experimental variability using the Markov Chain Monte Carlo (MCMC) method to enhance predictive accuracy and robustness. Initially, we examined the objective function landscape for fitting a Power Law hemolysis model, whose parameters are derived from inherently uncertain experimental data, by employing a grid search approach. Building on this, we applied MCMC to derive detailed stochastic distributions for the hemolysis Power Law model parameters C, α, and β. These distributions were then propagated through a reduced order model of the FDA benchmark pump to quantify the experimental uncertainty in hemolysis measurements with respect to the predicted pump hemolysis. Our analysis revealed a global flat minimum in the objective function landscape of the multi-parameter power law model, a phenomenon attributable to fundamental mathematical limitations in the fitting process. The probabilistic hemolysis model converged to a constant optimal C = 3.515 × 10 and log normal distributions of α and β with means of 0.614 and 1.795, respectively. This probabilistic approach successfully captured both the mean and variance observed in the experimental FDA benchmark pump data. In comparison, conventional deterministic models are not able to describe experimental variation. Incorporating uncertainty quantification through MCMC enhances the robustness and predictive accuracy of hemolysis models. This method allows for better comparison of simulated hemolysis outcomes with in vitro experiments and can integrate additional datasets, potentially setting a new standard in hemolysis modeling.
本研究的目的是解决数值溶血模型中缺乏不确定性量化的问题，而数值溶血模型对医疗器械评估至关重要。具体而言，我们旨在开发一种概率溶血模型，该模型使用马尔可夫链蒙特卡罗（MCMC）方法纳入实验变异性，以提高预测准确性和稳健性。最初，我们通过采用网格搜索方法，研究了用于拟合幂律溶血模型的目标函数景观，该模型的参数源自本质上不确定的实验数据。在此基础上，我们应用MCMC来推导溶血幂律模型参数C、α和β的详细随机分布。然后，这些分布通过FDA基准泵的降阶模型进行传播，以量化溶血测量中相对于预测泵溶血的实验不确定性。我们的分析揭示了多参数幂律模型的目标函数景观中存在全局平坦最小值，这一现象可归因于拟合过程中的基本数学限制。概率溶血模型收敛到常数最优C = 3.515×10，以及α和β的对数正态分布，其均值分别为0.614和1.795。这种概率方法成功地捕捉了在FDA基准泵实验数据中观察到的均值和方差。相比之下，传统的确定性模型无法描述实验变化。通过MCMC纳入不确定性量化可提高溶血模型的稳健性和预测准确性。这种方法能够更好地将模拟溶血结果与体外实验进行比较，并可以整合额外的数据集，有可能为溶血建模设定新的标准。
