Figures and data
Initial parameter values and literature basis for the choice of variability ranges for the model inputs.
Unless stated explicitly, quoted values from the literature have come from previous modelling choices.
Experimental and clinical datasets for calibration of healthy ventricular electromechanical models, with summary of why each biomarker was important due to their implications in cardiac diseases.
Data were presented in the forms: [x, y]95% were 95% confidence intervals, x±ystd were mean and standard deviations, x±zsem were mean and standard error of the mean, [x, y]range were the minimum (x) and maximum (y), x [y, z]IQR were median (x) and first (y) and third (z) quartiles , x (y)IQR were median (x) and interquartile range (y). UKBB indicates values extracted from the UK Biobank. Unit conversions from original source has been done where appropriate to preserve consistency across the entire table. SCD: sudden cardiac death, HCM: hypertrophic cardiomyopathy, DCM: dilated cardiomyopathy, CAD: coronary artery disease, HF: heart failure, HFpEF: HF with preserved ejection fraction, PAH: pulmonary artery hypertension, MI: myocardial infarction, HHD: hypertensive heart disease, LV: left ventricle, RV: right ventricle, EDV: end diastolic volume, ESV: end systolic volume, SVL: stroke volume in left ventricle, SVR: stroke volume in right ventricle, ESP: end systolic pressure, CMR: cardiac magnetic resonance imaging.
Comparison of (A) simulated ECG, (B) pressure-volume, (C) flow rate, (D) pressure upstroke, and (E) cellular action potential and calcium transient biomarkers of the calibrated baseline electromechanical model with healthy population data ranges shown in green, except for (B, C, D) where the pressure volume ranges were shown in blue. The calibrated model parameters were show in the table (F).
Comparison of simulated baseline left ventricular strains with non-invasive DENSE+cDTI MRI (in vivo) measurements.
(A) Comparison of strain along the myofiber direction measured in the mid-short-axis of the ventricles. (B) Comparison of strains in the circumferential and radial directions measured in a mid-short-axis slice and longitudinal strains measured in a four-chamber long axis slice. Median trace is plotted in dotted lines, while upper and lower quartiles in solid lines.
The effect of variability in single model parameters in cellular ionic conductances (red), passive and active biomechanical parameters (green), and circulatory model parameters (yellow) on simulated biomarkers of the ECG (A), pressure-volume (B), deformations (C), and strains (D).
Only relationships with p-value <0.05 and r-values higher than 0.6 were plotted. Positive correlations are shown in red, and negative correlations shown in blue. The thickness of each line indicates the normalised magnitude of effect and the width of each line indicates the r-value of the relationship.
Comparison of simulated validation outputs against reference ranges for deformation and strain biomarkers not used in model calibration.
Reference ranges are as reported in Table 2. AVPD: atrioventricular plane displacement, E_ff: fibre strain, E_cc: circumferential strain, E_rr: radial strain, E_ll: longitudinal strain.
Multi-scale effect of GCaL on active tension, action potential duration (A), ventricular deformation and strains (B), the pressure-volume loop (C) and precordial ECG leads (D).
ECG characteristics were explained by the activation and repolarization maps (E), with dotted lines highlighting increased longitudinal shortening with increasing GCaL by the basal plane moving towards the apex while the apical position remains unchanged, due to the presence of the pericardial constraint. Scaling factor of 0.93 was the closest scaling factor to 1 when uniformly sampling eight datapoints in range [0.5, 2].
Multi-scale effect of SERCA conductance on active tension, calcium transient, action potential duration (A), ventricular deformation and strains (B), the pressure-volume loop (C) and precordial ECG leads (D).
ECG characteristics were explained by the activation and repolarization maps (E). Scaling factor of 0.93 was the closest scaling factor to 1 when uniformly sampling eight datapoints in range [0.5, 2].
Comparison of mid-line between Alya simulation (grey reference, blue deformed) and Sander Land et al. 2015 (red) passive inflation.
Global sensitivity analysis (C) using cellular model of ventricular electromechanics show the effect of the top five model parameters on active tension (A) and action potential duration (A and B) biomarkers.
Uncertainties in simulated pressure volume dynamics were influenced by uncertainties in mechanical (green), circulatory (yellow) and ionic conductance (red) parameters of the model.
Of the parameters included in the analysis, the QRS section of the ECG showed minor sensitivity to variability in mechanical parameters (green labels) (A) while the ST and T wave segments of the ECG were strongly sensitivity to uncertainties in ionic conductances (red labels), and only showed minor sensitivity to some mechanical parameters (green labels) (B).
Pressure (A) and volume (B) transients in response to parameter variability in mechanical properties (green), circulation (yellow), ionic conductances (red).
Key aspects of the ventricular deformation, including atrioventricular plane displacement, wall thickness changes, and myocardium volume changes were affected predominantly by mechanical parameters (green) and ionic conductance parameters (red), with weaker effects from circulatory parameters (yellow).
