Toward Uncertainty-Aware Hemolysis Modeling: A Universal Approach to Address Experimental Variance.

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