📝 SummarXiv

Abstract

Computational cardiovascular models are promising tools for clinical decision support, particularly in complex conditions, such as intraoperative hypotension (IOH). IOH arises from different mechanisms, making treatment selection non-trivial. Patient-specific predictions require calibration, typically performed using deterministic approaches prone to parameter non-identifiability and lacking uncertainty quantification, hindering clinical translation. Consequently, Bayesian approaches are needed that facilitate parameter inference, sensitivity analysis, and uncertainty quantification in cardiovascular models. We utilize Bayesian Markov chain Monte Carlo (MCMC) to estimate parameter distributions of a cardiovascular lumped parameter model (LPM) across different IOH scenarios. We demonstrate parameter non-uniqueness and its impact on sensitivity indices. We improve parameter reliability by incorporating clinical knowledge and measurement uncertainties. We enable continual learning of the model using sequential parameter updating as new patient data become available. We introduce an uncertainty-aware sensitivity analysis and compare it with a deterministic approach. Deterministic calibration yielded many local solutions for IOH, with notably different sensitivities. MCMC distinguished different hypotension scenarios, such as those induced by impaired contractility or hypovolemia. Propagating uncertainties from MCMC through sensitivity analysis provided tighter credible intervals, resulting in more stable parameter rankings than the deterministic approach. The Bayesian approach revealed differences in model behavior and treatment suggestions across patient conditions. Combining Bayesian inference with sequential updating and sensitivity analysis improves the reliability and identifiability of parameter estimates, enhancing the clinical utility of LPMs for therapy guidance.