A comparative study of lumped heart models for personalized medicine through sensitivity and identifiability analysis

Numerical modelling of the cardiovascular system is becoming an increasingly accepted tool for clinical applications, with the ultimate goal of personalized medicine. Lumped parameter models are attractive due to their low computational cost but often do not directly incorporate physical properties, thus requiring calibration to (often sparse) clinical data. Furthermore there exists a trade-off between physiological relevance and model complexity, making the choice of cardiac model non-trivial. For two established cardiac chamber models embedded in a haemodynamics whole circulation model, we perform sensitivity and identifiability analyses to examine the possibility of finding a unique subset of important parameters with varying levels of clinically measurable data, thereby examining their applicability in personalized medicine. To provide a concrete clinical context, the case of treatment planning for a young pulmonary arterial hypertension patient is considered. The methodology is however relevant for other pathophysiologies. The results suggest that the single-fibre model, although a priori more complex than the time-varying elastance model, is more amenable for patient-specific modelling. This was found for representing the patient state from clinical data, defining parameter ranges for sensitivity analysis (SA), and in the results of the identifiability analysis. SA also revealed the most influential parameters, which for the right (respectively left) heart chambers, mostly affect the haemodynamics of these chambers and the pulmonary (respectively systemic) circulation but also the ones of the left (respectively right) side. This highlights the importance of studying the whole circulation, especially in diseases traditionally thought to affect only one side. KEY POINTS: Two established cardiac chamber computational models are compared in the context of pulmonary hypertension, but the methodology holds for other pathophysiologies. Comprehensive patient-specific data, accurate parameter ranges and thorough model validation are essential to enhance parameter identifiability, improve model personalization and eventually lead to better prediction of haemodynamic changes after clinical intervention. Due to its inherent mathematical constraints and physiologically interpretable input parameters, the single-fibre model is easier to make patient-specific and more accurately captures the non-linear dynamics of ventricular pressure-volume loops than the simpler time-varying elastance model, making it more suitable for personalized cardiac simulations. Accurate representation of both the left and right heart chambers in cardiac modelling is important, as our results suggest that parameters of each side impact the haemodynamics of the other circulatory side, making it worthwhile to measure the characteristics of both the right and left chambers, even if the disease emerges from one side.