Key result
Density-based calibration of electrophysiology models improves capture of AF variability over traditional range-based methods.
A novel methodology for calibrating populations of models to data density improves the capture of biological variability in atrial electrophysiology models.
The understanding of complex physical or biological systems nearly always requires a characterization of the variability that underpins these processes. In addition, the data used to calibrate these models may also often exhibit considerable variability. A recent approach to deal with these issues has been to calibrate populations of models (POMs), multiple copies of a single mathematical model but with different parameter values, in response to experimental data. To date, this calibration has been largely limited to selecting models that produce outputs that fall within the ranges of the data set, ignoring any trends that might be present in the data. We present here a novel and general methodology for calibrating POMs to the distributions of a set of measured values in a data set. We demonstrate our technique using a data set from a cardiac electrophysiology study based on the differences in atrial action potential readings between patients exhibiting sinus rhythm (SR) or chronic atrial fibrillation (cAF) and the Courtemanche-Ramirez-Nattel model for human atrial action potentials. Not only does our approach accurately capture the variability inherent in the experimental population, but we also demonstrate how the POMs that it produces may be used to extract additional information from the data used for calibration, including improved identification of the differences underlying stratified data. We also show how our approach allows different hypotheses regarding the variability in complex systems to be quantitatively compared.
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Lawson et al. (2018) studied Atrial fibrillation and sinus rhythm (n=363). Data density calibration (Sequential Monte Carlo) vs. Range-based calibration (Latin Hypercube Sampling) was evaluated on Localization to data-dense regions in biomarker space and capture of biomarker distributions. Calibrating populations of computational electrophysiology models to data density accurately captured the morphological differences and biomarker variability between sinus rhythm and chronic atrial fibrillation better than range-based calibration.
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