A Novel Method for Measuring Myocardial Performance Index using Non-contact Ballistocardiogram System

Aim: The aim was to validate the Systolic Time Intervals (STI) measured by Ballistocardiography (BCG) with STI derived from simultaneously performed Transthoracic Echocardiogram (TTE) and attempt to create an AI algorithm that automatically calculates Tei Index from BCG tracings. Study design: Cross-sectional study. Place and Duration of Study: Department of Cardiology and Department of Electrophysiology of Sri Jayadeva Institute of Cardiovascular Sciences & Research, Bangalore, India, between January 2020 and January 2021. Methodology: Two hundred seventy-four patients with clinically indicated TTE were enrolled in the study, average age was 52. Simultaneous recordings on BCG and TTE were done. 150 patients had clinically usable TTE images for accurate calculations. STI was calculated independently by operators experienced in TTE and BCG. Results were compared using Pearson’s R. A proprietary AI algorithm for automatically calculating the MPI, was trained over a subset of patients. Its accuracy in detecting STI was compared to that of TTE and manually calculated STI from BCG. Results: There was a strong positive correlation (r=0.766, P<0.00, 99%CI [0.691,0.824]) between the TTE and BCG derived MPI values. The result was validated over predetermined subgroups of subjects with reduced EF (EF<50) and subjects with normal EF (EF>=50). The AI algorithm had correlation of 0.54(p<0.01) with the MPI calculated by TTE and 0.34(P<0.10) with the manually calculated MPI on the BCG. Conclusion: BCG derived manual and automated MPI correlates well with TTE derived MPI in a variety of EF fraction subgroups. Automated calculation algorithms for MPI derived from BCG remain a work under progress.

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

Left ventricular end-systolic elastance (Ees) is a major determinant of cardiac systolic function and ventricular-arterial interaction. Previous methods for the Ees 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 Ees. This study proposes a novel artificial intelligence—based approach to estimate Ees 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 Ees using as inputs arm cuff pressure, PEP, and ET. Results showed that Ees can be predicted with high accuracy achieving a normalized RMSE equal to 9.15% (r = 0.92) for a wide range of Ees 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 Ees 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.

Abstract 13341: Systolic Time Intervals and Alternative Echocardiographic Parameters to Evaluate Bioprosthetic Aortic Valve Function

Background: Bioprosthesis (BP) used for aortic valve replacement (AVR) in aortic valve disease are subject to structural valve deterioration (SVD) after 20 years or less which leads to reintervention. The diagnostic of SVD can be challenging in the presence of patient-prosthesis mismatch (PPM) which occurs when the effective orifice area (EOA) of the BP is too small for the metabolic needs of a patient. Both SVD and PPM are mainly diagnosed with echocardiography and are characterized by high transprosthetic mean and peak pressure gradients (MG and PG) and maximal velocity (Vmax) as well as reduced EOA. Easily accessible parameters are needed to distinguish SVD from PPM. Acceleration time (AT), ejection time (ET) and AT/ET have been proposed as accurate to identify SVD in presence of PPM. However, these parameters need to be validated. Objectives: The main goal of the present study is to determine the accuracy of AT and AT/ET and to validate new echocardiographic parameters that can distinguish SVD from PPM. New parameters proposed for this study are MG/EOA and PG over mean flow rate (PG/MFR). Methods: In a retrospective study, we analyzed echocardiographic data of 653 patients who underwent AVR. PPM was defined as a projected EOA to body surface area inferior to 0.85 cm 2 /m 2 . SVD was defined using a multiparametric integrative approach including morphological (fibrocalcific remodeling, thickening and stiffening of valve leaflets) criteria and hemodynamic changes according to American Society of Echocardiography/European Association for Cardiovascular Imaging recommendations. Results: EOA was lower and MG higher for patients with SVD compared to those with pure PPM. PG/Q and MG/EOA were significantly higher in patients with SVD compared to patients presenting pure PPM (PG/MFR: 0.22 mm Hg/ml/s ± 0.01 vs 0.15 mm Hg/ml/s ± 0.01; MG/EOA: 31 mm Hg/cm 2 ±2 vs 16 mm Hg/cm 2 ±2, p<0.001) respectively. ROC analysis showed that PG/Q and MG/EOA were the best echocardiographic parameters to distinguish pure PPM from SVD with AUC of 0.872 and 0.876 respectively. AT and AT/ET achieved AUC of 0.682 and 0.663. Conclusion: AT and AT/ET are not accurate parameters to identify SVD in presence of PPM. MG/EOA and PG/MFR represent interesting alternatives to conventional parameters.

P1460Seismocardiographic measurement of systolic time intervals in left ventricular dysfunction: validation with transthoracic echo

Background Systolic time intervals (STI) measured using Transthoracic Echo (TTE) have been shown to be sensitive indicators of contractile performance, but TTE is limited to a hospital setting. Recent advances in technology have enabled the simultaneous measurement of Electrocardiograms (ECG) and Seismocardiograms (SCG) using wearable devices. However, the use SCG to measure STIs has not been validated in a mobile setting, or on pathological subjects. Purpose To test the validity of an automated algorithm for measuring STIs using a wearable device recording ECG and SCG, in subjects with varying degrees of left ventricular dysfunction. Methods 179 Patients with suspected left ventricular dysfunction were assessed using TTE performed by a Cardiologist. Patients were simultaneously fitted with a novel wearable device worn on a chest strap, recording SCG and single-lead ECG data, while the cardiologist measured the pre-ejection period (PEP) and left ventricular ejection time (LVET) with the patient in a supine position. Of these subjects 29 (16.2%) were diagnosed with Dilated cardiomyopathy (DCM), and 109 (60.8%) had Ischemic Heart disease. The SCG and ECG data recorded on the wearable device was then analysed using a peak-detection algorithm, which detected the Q,R,S points on the ECG, and then determined the 4 most prominent peaks in the SCG signal corresponding to each R-peak. Heart rate, patient's age, gender and SCG time intervals were then used in a DecisionTree algorithm to determine the values of PEP and LVET, which were were then compared against those determined by the cardiologist using TTE. Results The correlation coefficient (r2) between PEP calculated using TTE, and the values obtained from the algorithm analyzing SCG data was 0.92 while the mean error was 7.47%. The r2 between the LVET calculated using the TTE and the algorithm was 0.75, while the mean error was 8.53% (p-value<0.001 for all cases). Results All Subjects With IHD Without IHD With DCM Without DCM Number of Subjects 178 109 69 29 149 PEP (r2) 0.92 0.89 0.94 0.88 0.91 PEP (% age error) 7.47 7.50 7.42 6.99 7.56 LVET (r2) 0.75 0.81 0.66 0.55 0.83 LVET (% age error) 8.53 6.69 11.4 20.42 6.16 DCM, Dilate Cardiomyopathy; IHD, Ischemic Heart Disease. ECG + SCG signals for a DCM patient Conclusion The algorithm-derived STIs measured by SCG correlate well with those measured by TTE across most patient groups, including those with Dilated Cardiomyopathy and Ischemic Heart Disease, opening prospects for continuous remote monitoring of STIs in a mobile setting. Acknowledgement/Funding Fourth Frontier Technologies