Computational cardiovascular models are promising tools for clinical decision support, particularly in complex conditions, such as intraoperative hypotension (IOH). IOH is characterised by a significant decrease in mean arterial pressure (MAP), which may result from severe vasodilation, hypovolaemia due to blood loss, or reduced ventricular contractility. Treatment selection is non-trivial and may include vasopressors, inotropes and intravenous fluid boluses [1]. Although much research has focused on algorithms to predict IOH, a tool to guide decision-making regarding the most promising intervention strategy is still lacking [2]. Patient-specific predictions require calibration, typically performed using classical optimization 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. This study aims to use Bayesian inference and sensitivity analyses based on derived posteriors to quantify uncertainties and assess their impact on patients’ systemic blood pressure in response to different IOH treatments across various pathological scenarios. We utilize Bayesian Markov chain Monte Carlo (MCMC) to estimate parameter distributions of a cardiovascular lumped parameter model (LPM) across different IOH scenarios. We improve parameter reliability by incorporating clinical knowledge and measurement uncertainties. Continual learning of the model is achieved by sequential parameter updating as new patient data become available. We introduce an uncertainty-aware sensitivity analysis and compare it with a classical approach. Fig. 1 shows an overview of the implemented pipeline. MCMC was able to distinguish different IOH scenarios, such as those induced by impaired contractility or hypovolemia. Parameter uncertainty decreased by about 70% with additional measurement data, and by up to 94% with sequential updating. Propagating uncertainties from MCMC through sensitivity analysis provided tighter credible intervals, resulting in more stable parameter rankings than the classical approach. The Bayesian approach revealed differences in model sensitivity and treatment suggestions across patient conditions, highlighting the potential to inform therapy planning. The next step is to validate it using real patient data.