AI-Based Estimation of End-Systolic Elastance From Arm-Pressure and Systolic Time Intervals

Left ventricular end-systolic elastance (E_es) is a major determinant of cardiac systolic function and ventricular-arterial interaction. Previous methods for the E_es estimation require the use of the echocardiographic ejection fraction (EF). However, given that EF expresses the stroke volume as a fraction of end-diastolic volume (EDV), accurate interpretation of EF is attainable only with the additional measurement of EDV. Hence, there is still need for a simple, reliable, noninvasive method to estimate E_es. This study proposes a novel artificial intelligence-based approach to estimate E_es using the information embedded in clinically relevant systolic time intervals, namely the pre-ejection period (PEP) and ejection time (ET). We developed a training/testing scheme using virtual subjects (n = 4,645) from a previously validated in-silico model. Extreme Gradient Boosting regressor was employed to model E_es using as inputs arm cuff pressure, PEP, and ET. Results showed that E_es can be predicted with high accuracy achieving a normalized RMSE equal to 9.15% (r = 0.92) for a wide range of E_es values from 1.2 to 4.5 mmHg/ml. The proposed model was found to be less sensitive to measurement errors (±10-30% of the actual value) in blood pressure, presenting low test errors for the different levels of noise (RMSE did not exceed 0.32 mmHg/ml). In contrast, a high sensitivity was reported for measurements errors in the systolic timing features. It was demonstrated that E_es can be reliably estimated from the traditional arm-pressure and echocardiographic PEP and ET. This approach constitutes a step towards the development of an easy and clinically applicable method for assessing left ventricular systolic function.