An AI-based model using arm cuff pressure, pre-ejection period, and ejection time accurately estimated end-systolic elastance with a normalized RMSE of 9.15% (r = 0.92).
Can an AI-based model accurately estimate left ventricular end-systolic elastance using arm-pressure and systolic time intervals?
An AI-based model using arm cuff pressure and systolic time intervals can accurately estimate left ventricular end-systolic elastance, offering a potential noninvasive method for assessing systolic function.
Effect estimate: r = 0.92
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.
Bikia et al. (Wed,) conducted a other in Left ventricular systolic function assessment (n=4,645). Extreme Gradient Boosting regressor using arm cuff pressure, pre-ejection period, and ejection time was evaluated on Prediction accuracy of left ventricular end-systolic elastance (normalized RMSE) (r = 0.92). An AI-based model using arm cuff pressure, pre-ejection period, and ejection time accurately estimated end-systolic elastance with a normalized RMSE of 9.15% (r = 0.92).