Towards Robust Hemolysis Modeling with Uncertainty Quantification: A Universal Approach to Address Experimental Variance

TL;DR

This paper introduces a universal approach using Markov Chain Monte Carlo to incorporate experimental uncertainty into hemolysis models, improving their robustness and predictive accuracy for medical device evaluation.

Contribution

It applies MCMC to quantify uncertainty in hemolysis model parameters, addressing non-uniqueness and variability issues in traditional deterministic models.

Findings

MCMC reveals multiple local minima in model fitting.

Distributions of parameters better match experimental data.

Enhanced model robustness over deterministic approaches.

Abstract

Purpose: 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 incorporate experimental variability into these models using the Markov Chain Monte Carlo (MCMC) method to enhance predictive accuracy and robustness. Methods: We applied the MCMC method to an experimental hemolysis dataset to derive detailed stochastic distributions for the hemolysis Power Law model parameters $C$, $\alpha$ and $\beta$. 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. Results: The MCMC analysis revealed multiple local minima in the sum of squared errors, highlighting the non-uniqueness of traditional Power Law model fitting. The MCMC results showed a constant optimal $C=3.515x10-5$ and log normal distributions of $\alpha$ and $\beta$ with means of 0.614 and 1.795, respectively. The MCMC model closely matched the mean and variance of experimental data. In comparison, conventional deterministic models are not able to describe experimental variation. Conclusion: 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-vivo experiments and can integrate additional datasets, potentially setting a new standard in hemolysis modeling.