Left Ventricular Ejection Time Measured by Echocardiography Differentiates Neurobehavioral Resilience and Vulnerability to Sleep Loss and Stress

There are substantial individual differences (resilience and vulnerability) in performance resulting from sleep loss and psychosocial stress, but predictive potential biomarkers remain elusive. Similarly, marked changes in the cardiovascular system from sleep loss and stress include an increased risk for cardiovascular disease. It remains unknown whether key hemodynamic markers, including left ventricular ejection time (LVET), stroke volume (SV), heart rate (HR), cardiac index (CI), blood pressure (BP), and systemic vascular resistance index (SVRI), differ in resilient vs. vulnerable individuals and predict differential performance resilience with sleep loss and stress. We investigated for the first time whether the combination of total sleep deprivation (TSD) and psychological stress affected a comprehensive set of hemodynamic measures in healthy adults, and whether these measures differentiated neurobehavioral performance in resilient and vulnerable individuals. Thirty-two healthy adults (ages 27–53; 14 females) participated in a 5-day experiment in the Human Exploration Research Analog (HERA), a high-fidelity National Aeronautics and Space Administration (NASA) space analog isolation facility, consisting of two baseline nights, 39 h TSD, and two recovery nights. A modified Trier Social Stress Test induced psychological stress during TSD. Cardiovascular measure collection [SV, HR, CI, LVET, BP, and SVRI] and neurobehavioral performance testing (including a behavioral attention task and a rating of subjective sleepiness) occurred at six and 11 timepoints, respectively. Individuals with longer pre-study LVET (determined by a median split on pre-study LVET) tended to have poorer performance during TSD and stress. Resilient and vulnerable groups (determined by a median split on average TSD performance) showed significantly different profiles of SV, HR, CI, and LVET. Importantly, LVET at pre-study, but not other hemodynamic measures, reliably differentiated neurobehavioral performance during TSD and stress, and therefore may be a biomarker. Future studies should investigate whether the non-invasive marker, LVET, determines risk for adverse health outcomes.

Abstract 17298: Validation of Beat-to-Beat Blood Pressure and Left Ventricular Ejection Time by the Caretaker Physiological Monitor Against Invasive Central Aortic Measurement

Objective: There is an unmet need for noninvasive continuous blood pressure (BP) monitoring technologies in various clinical settings. We examined the accuracy of noninvasive Caretaker device against invasively measured central aortic BP. Methods: Beat-to-beat BP by Caretaker was recorded simultaneously with central aortic BP measured in patients undergoing cardiac catheterization. We derived correlations and Bland-Altman comparisons, after calibrating the Caretaker with 20 seconds of the initial catheter readings, as well as trend analyses for both systolic (SBP) and diastolic BP (DBP). We also measured left ventricular ejection time (LVET) from both aortic pressure tracing and Caretaker and compared the two. Results: A total of 47 patients were included in the study. A total of 31,369 beats obtained during the diagnostic portion of coronary angiogram were used for analysis. The correlations for SBP and DBP were 0.89 and 0.78, respectively (p < 0.001 for both). The Bland-Altman comparison yielded overall mean differences of 2.11 mmHg (SD 7.40) for SBP and 1.46 mmHg (SD 6.12) for DBP respectively (p <0.001 for all comparisons). The trend analysis yielded concordances of 86% and 85% for SBP and DBP, respectively. The correlation and Bland-Altman analyses for the LVET comparison yielded 0.89 (p< 0.001) with a mean difference of 13.9 ms (SD 14.4 ms). Conclusion: Beat-to-beat BP by Caretaker showed excellent agreement and high concordance in the direction and the degree of BP change with central aortic BP. This study supports the satisfactory performance of the Caretaker device in continuous tracking of beat-to-beat BP and LVET measurements.

Wearable Cardiorespiratory Monitoring Employing a Multimodal Digital Patch Stethoscope: Estimation of ECG, PEP, LVET and Respiration Using a 55 mm Single-Lead ECG and Phonocardiogram

Cardiovascular diseases are the main cause of death worldwide, with sleep disordered breathing being a further aggravating factor. Respiratory illnesses are the third leading cause of death amongst the noncommunicable diseases. The current COVID-19 pandemic, however, also highlights the impact of communicable respiratory syndromes. In the clinical routine, prolonged postanesthetic respiratory instability worsens the patient outcome. Even though early and continuous, long-term cardiorespiratory monitoring has been proposed or even proven to be beneficial in several situations, implementations thereof are sparse. We employed our recently presented, multimodal patch stethoscope to estimate Einthoven electrocardiogram (ECG) Lead I and II from a single 55 mm ECG lead. Using the stethoscope and ECG subsystems, the pre-ejection period (PEP) and left ventricular ejection time (LVET) were estimated. ECG-derived respiration techniques were used in conjunction with a novel, phonocardiogram-derived respiration approach to extract respiratory parameters. Medical-grade references were the SOMNOmedics SOMNO HDTM and Osypka ICON-CoreTM. In a study including 10 healthy subjects, we analyzed the performances in the supine, lateral, and prone position. Einthoven I and II estimations yielded correlations exceeding 0.97. LVET and PEP estimation errors were 10% and 21%, respectively. Respiratory rates were estimated with mean absolute errors below 1.2 bpm, and the respiratory signal yielded a correlation of 0.66. We conclude that the estimation of ECG, PEP, LVET, and respiratory parameters is feasible using a wearable, multimodal acquisition device and encourage further research in multimodal signal fusion for respiratory signal estimation.

Using Smart Wearables to Monitor Cardiac Ejection

An individual’s cardiovascular state is a crucial aspect of a healthy life. However, it is not routinely assessed outside the clinical setting. Smart wearables use photoplethysmography (PPG) to monitor the arterial pulse wave (PW) and estimate heart rate. The PPG PW is strongly influenced by the ejection of blood from the heart, providing an opportunity to monitor cardiac parameters using smart wearables. The aim of this study was to investigate the feasibility of monitoring left ventricular ejection time (LVET) and left ventricular contractility (LVC) from the PPG PW at the wrist. PPG PWs were simulated under a range of cardiovascular conditions using a numerical model of PW propagation. LVET and LVC were estimated from the first and second derivatives of the PPG PWs and compared to reference values extracted from the blood pressure PW at the aortic root. There was strong agreement between the estimated and reference values of LVET, indicating that it may be feasible to assess LVET from PPG signals, including those acquired by smart watches. The correlations between the estimated and reference values of LVC were less strong, indicating that further work is required to assess contractility robustly using smart wearables. This study demonstrated the feasibility of assessing LVET using smart wearables that could allow individuals to monitor their cardiovascular state on a daily basis.

Improvement of Left Ventricular Ejection Time Measurement in the Impedance Cardiography Combined with the Reflection Photoplethysmography

Cardiac stroke volume (SV) is an essential hemodynamic indicator that can be used to assess whether the pump function of the heart is normal. Non-invasive SV measurement is currently performed using the impedance cardiography (ICG). In this technology, left ventricular ejection time (LVET) is an important parameter which can be determined from the ICG signals. However, the ICG signals are inherently susceptible to artificial noise interference, which leads to an inaccurate LVET measurement and then yields an error in the calculation of SV. Therefore, the goal of the study was to measure LVETs using both the transmission and reflection photoplethysmography (PPG), and to assess whether the measured LVET was more accurate by the PPG signal than the ICG signal. The LVET measured by the phonocardiography (PCG) was used as the standard for comparing with those by the ICG and PPG. The study recruited ten subjects whose LVETs were simultaneously measured by the ICG using four electrodes, the reflection PPG using neck sensors (PPGneck) and the transmission PPG using finger sensors (PPGfinger). In each subject, ten LVETs were obtained from ten heartbeats selected properly from one-minute recording. The differences of the measured LVETs between the PCG and one of the ICG, PPGneck and PPGfinger were −68.2 ± 148.6 ms, 4.8 ± 86.5 ms and −7.0 ± 107.5 ms, respectively. As compared with the PCG, both the ICG and PPGfinger underestimated but the PPGneck overestimated the LVETs. Furthermore, the measured LVET by the PPGneck was the closest to that by the PCG. Therefore, the PPGneck may be employed to improve the LVET measurement in applying the ICG for continuous monitoring of SV in clinical settings.