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SUMMARY:Cell to Whole Organ Global Sensitivity Analysis and Parameter Infe
 rence on a Four-chamber Heart Electromechanics Model Using Gaussian Proces
 ses Emulators - Marina Strocchi (Imperial College London)
DTSTART:20240604T111000Z
DTEND:20240604T113000Z
UID:TALK214552@talks.cam.ac.uk
DESCRIPTION:Cardiac pump function arises from a series of highly orchestra
 ted events across multiple scales. Computational electromechanics can enco
 de these events in physics-constrained models. However\, the large number 
 of parameters in these models has made the systematic study of the link be
 tween cellular\, tissue\, and organ scale parameters to whole heart physio
 logy challenging. A patient-specific anatomical heart model\, or digital t
 win\, was created. Cellular ionic dynamics and contraction were simulated 
 with the Courtemanche-Land and the ToR-ORd-Land models for the atria and t
 he ventricles\, respectively. Whole heart contraction was coupled with the
  circulatory system\, simulated with CircAdapt\, while accounting for the 
 effect of the pericardium on cardiac motion. The four-chamber electromecha
 nics framework resulted in 117 parameters of interest. The model was broke
 n into five hierarchical sub-models: tissue electrophysiology\, ToR-ORd-La
 nd model\, Courtemanche-Land model\, passive mechanics and CircAdapt. For 
 each sub-model\, we trained Gaussian processes emulators (GPEs) that were 
 then used to perform a global sensitivity analysis (GSA) to retain paramet
 ers explaining 90% of the total sensitivity for subsequent analysis. We id
 entified 45 out of 117 parameters that were important for whole heart func
 tion. We performed a GSA over these 45 parameters and identified the syste
 mic and pulmonary peripheral resistance as being critical parameters for a
  wide range of volumetric and hemodynamic cardiac indexes across all four 
 chambers. Finally\, we used Bayesian history matching in combination with 
 GPEs to restrict the parameter ranges to where the model behaved in agreem
 ent with the available clinical data for the patient. We have shown that G
 PEs provide a robust method for mapping between cellular properties and cl
 inical measurements. This framework can be applied to identify parameters 
 that can be calibrated in patient-specific models or digital twins\, and t
 o link cellular function to clinical indexes.
LOCATION:Seminar Room 1\, Newton Institute
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