In vivo application and validation of a novel noninvasive method to estimate the end-systolic elastance.

Accurate assessment of the left ventricular (LV) systolic function is indispensable in the clinic. However, estimation of a precise index of cardiac contractility, i.e., the end-systolic elastance (), is invasive and cannot be established as clinical routine. The aim of this work was to present and validate a methodology that allows for the estimation of from simple and readily available noninvasive measurements. The method is based on a validated model of the cardiovascular system and noninvasive data from arm-cuff pressure and routine echocardiography to render the model patient-specific. Briefly, the algorithm first uses the measured aortic flow as model input and optimizes the properties of the arterial system model to achieve correct prediction of the patient's peripheral pressure. In a second step, the personalized arterial system is coupled with the cardiac model (time-varying elastance model) and the LV systolic properties, including , are tuned to predict accurately the aortic flow waveform. The algorithm was validated against invasive measurements of (multiple pressure-volume loop analysis) taken from = 10 patients with heart failure with preserved ejection fraction and = 9 patients without heart failure. Invasive measurements of (median = 2.4 mmHg/mL, range = [1.0, 5.0] mmHg/mL) agreed well with method predictions (normalized root mean square error = 9%, ρ = 0.89, bias = -0.1 mmHg/mL, and limits of agreement = [-0.9, 0.6] mmHg/mL). This is a promising first step toward the development of a valuable tool that can be used by clinicians to assess systolic performance of the LV in the critically ill. In this study, we present a novel model-based method to estimate the left ventricular (LV) end-systolic elastance () according to measurement of the patient's arm-cuff pressure and a routine echocardiography examination. The proposed method was validated in vivo against invasive multiple-loop measurements of , achieving high correlation and low bias. This tool could be most valuable for clinicians to assess the cardiovascular health of critically ill patients.