Estimation of Simulated Left Ventricle Elastance Using Lumped Parameter Modelling and Gradient-Based Optimization With Forward-Mode Automatic Differentiation Based on Synthetically Generated Noninvasive Data.

The present study evaluates a parameter discovery approach based on a lumped parameter model of the cardiovascular system in conjunction with optimization to approximate important cardiac parameters, including simulated left ventricle elastances. Important parameters pertaining to ventricular function were estimated using gradient optimization and synthetically generated measurements. Forward-mode automatic differentiation was used to estimate the cost function-parameter matrices and compared to the common finite differences approach. Synthetic data of healthy and diseased hearts were generated as proxies for noninvasive clinical measurements and used to evaluate the algorithm. Twelve parameters including left ventricle elastances were selected for optimization based on 99% explained variation in mean left ventricle pressure and volume. The hybrid optimization strategy yielded the best overall results compared to 1st order optimization with automatic differentiation and finite difference approaches, with mean absolute percentage errors ranging from 6.67% to 14.14%. Errors in left ventricle elastance estimates for simulated aortic stenosis and mitral regurgitation were smallest when including synthetic measurements for arterial pressure and valvular flow rate at approximately 2% and degraded to roughly 5% when including volume trends as well. However, the latter resulted in better tracking of the left ventricle pressure waveforms and may be considered when the necessary equipment is available.