OpenCRS: an open-source regulated human cardiorespiratory model with large-scale calibration and global sensitivity analysis at rest and during exercise
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Cardiopulmonary exercise testing reveals cardiovascular and respiratory limitations not apparent at rest, but similar measurements can arise from different interacting regulatory mechanisms, preventing physiological causal inference from data alone. Mechanistic computational models can separate these mechanisms in silico . However, whole-body cardiorespiratory models contain hundreds of parameters, making global sensitivity analysis and calibration challenging. We present OpenCRS, an open-source Python cardiorespiratory model coupling closed-loop 0D lumped-parameter circulation with gas exchange and integrated autonomic and respiratory control. The framework incorporates baroreflex, chemoreflex, pulmonary stretch receptors, central command, and neuromuscular drive, together with novel representations of atrial dynamics and exercise baroreflex set-point resetting. We introduce a scalable calibration pipeline that (i) applies a derivative-based global sensitivity measure (DGSM) directly to the simulator, reducing 272 parameters to 72 influential ones; (ii) trains Gaussian process emulator surrogates within iterative History Matching to exclude implausible parameter regions; and (iii) performs Bayesian calibration (MCMC), inferring a joint posterior with Hamiltonian Monte Carlo (No-U-Turn sampler) under a Gaussian copula prior. A single maximum a posteriori parameter set simultaneously reproduced 50 literature-derived rest and exercise targets (45/50 within 1 SD, all within 1.83 SD), with the rest-to-exercise transition emerging from the model’s embedded feedback rather than independent fitting. Simulator-based DGSM agreed with constrained Sobol indices (mean Spearman rank correlation 0.79). Sensitivity analysis identified influential physiological mechanisms. Only 2–16 parameters contributed > 2% of the Sobol total-effect sensitivity per target. Resting cardiovascular targets were driven by unstressed volumes and cardiac mechanics, while exercise shifted influence towards autonomic efferent regulation. Respiratory outputs remained most sensitive to chemoreflex and gas exchange parameters. These shifts capture coordinated cardiorespiratory adaptation to metabolic demand. More broadly, the framework provides a population-level prior with quantified uncertainty for cardiovascular digital twins and a reusable route for calibrating high-dimensional, regulated physiological models.
Author summary
Exercise reveals limits of the heart and lungs that resting measurements can miss, but the same result can arise from several interacting processes. We built OpenCRS, a computer model linking the heart, blood circulation, lungs, and the body’s automatic control of breathing and blood pressure, to separate these possible causes. Such models contain hundreds of adjustable parameters, making it challenging to identify which most influence measurements and which combinations fit the data. We developed an efficient workflow that screened a 272 parameter model to retain 72 influential ones, ruled out combinations that could not produce realistic results, and estimated uncertainty in the remaining values. We then identified a set of values that best reproduced 50 measurements at rest and during exercise. We found that resting heart function was shaped mainly by blood volume and how the heart fills and pumps, while nervous-system control becomes more influential during exercise. Breathing and gas exchange remained strongly affected by the body’s responses to oxygen and carbon dioxide. Our model and workflow provide a reusable basis for testing explanations of changes in heart and lung function and developing personalised computer models to study health and disease.
