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.