Representing Sex in Cardiovascular Models: Calibrating Reference Parameters from Healthy Cohorts

Reduced-order models are increasingly used to study cardiac physiology and inform patient-specific therapies. However, a model's prediction is only as reliable as its underlying parameters: representative model parameterization is essential to reflect the physiology of the populations these models are meant to represent, including biological sex. Most current models are parameterized from male or sex-agnostic data and/or focus on specific pathologies. Therefore, the goal of this work is to establish a formal parameter estimation pipeline for deriving reduced-order cardiovascular model parameter ranges that are physiologically representative of healthy women and men. We calibrated a closed-loop reduced order model of the heart and circulation separately for healthy female and male populations, using data pooled from eleven healthy cohorts. To account for parameter sensitivity and identifiability, we employed a three-stage parameter subset reduction pipeline: global sensitivity analysis (Sobol's method), collinearity screening (Fisher information matrix), and profile-likelihood identifiability analysis. Sex-specific distributions of parameters that were deemed sensitive and identifiable for each sex, ten for women and 9 for men, were obtained by Hamiltonian Monte Carlo. All the calibrated parameters showed less than 80% overlap between sexes, with the smallest overlap observed in some of the most influential parameters, such as stressed blood volume. Comparing simulations of the calibrated models against allometrically size-matched simulations showed that body size explained some, but not all, of the sex differences. The resulting parameter distributions provide reference ranges usable in future mechanistic and patient-specific simulations to contribute to more inclusive cardiovascular modeling.