Abstract P116: VARIATION IN MEAN ARTERIAL PRESSURE (MAP) AND ITS HEMODYNAMIC COMPONENTS DURING 24-HOUR AMBULATORY PULSE WAVE ANALYSIS: IMPACT OF AGE, RACE, GENDER AND BODY SIZE

BP is highly variable within and between individuals but the impact of variation in underlying hemodynamic components is unknown. We tested the feasibility and clinical associations of quantitated variances in MAP and its hemodynamic components [heart rate (HR), stroke volume (SV) and total vascular resistance (TVR)] obtained by 24-hr ambulatory pulse wave analysis (PWA, Mobil-O-Graph, IEM, Stolzberg, DE). BP and PWA were measured every 20 min for 24 hrs. Indexed to body surface area, MAP = HR*[SV index (SVI)]*[TVR index (TVRI)]; ln(MAP) = ln(HR) + ln(SVI) + ln(TVRI); and total MAP variability = var [ln(MAP)] = covariance (cov)[ln(HR), ln(MAP)] + cov[ln(SVI), ln(MAP)] + cov[ln(TVRI), ln(MAP)]. Relative contributions to var[ln(MAP)] for each hemodynamic component (as %) were calculated and associations with demographic characteristics were analyzed by correlations and t-tests. We studied 152 people (49% women, 23% black); mean(SD): # readings 57(11), age 59(16) yr, BMI 29.9(6.5) kg/m 2 , systolic BP 135(18) and MAP 106(14) mmHg. Mean(SD) 24-hr values were: ln(MAP) 4.64 (0.13), ln(HR) 4.20 (0.15), ln(SVI) -3.32 (0.15), and ln(TVRI) 3.75 (0.18). Relative contributions of hemodynamic components to total 24-hr ln(MAP) variation were: TVRI 54(36)%, HR 33(38)%, and SVI 13(40)%. The large SDs of these relative contributions led to analysis of potential contributing factors: TVRI contribution was correlated with 24-hr mean MAP (r=0.24, p=0.003) and was higher (>54%) in males (p=0.03) and blacks (p=0.04); HR contribution was inversely related to MAP (r=-0.26, p=0.001), age (r=-0.29, p=0.0003) and BMI (r=-0.173 p=0.05) and was lower (<33%) in blacks (p=0.008); SVI contribution was correlated with age (r=0.31, p<0.0001) and BMI (r=0.23, p=0.005) and was higher (>13%) in women (p=0.03). We conclude that 24-hr ambulatory PWA can identify components of MAP variation within individuals and their associations with demographic factors. The relative contributions of hemodynamic components (HR, SV, TVR) to 24-hr variability in ln(MAP) varies systematically with 24-hr mean MAP, age, race, gender, and BMI. Theoretical clinical implications may include therapeutic adjustments for extremes of variation in HR (beta-blockers), TVR (vasodilators) or SV (diuretics).

Evaluation of arterial stiffness and central blood pressure by oscillometric method in normotensive offspring of hypertensive parents

Children with a family history of hypertension have higher blood pressure and hypertensive pathophysiological changes begin before clinical findings. Here, the presence of arterial stiffness was investigated using central blood pressure measurement and pulse wave analysis in normotensive children with at least one parent with essential hypertension. Twenty-four-hour ambulatory pulse wave analysis monitoring was performed by oscillometric method in a study group of 112 normotensive children of hypertensive parents aged between 7 and 18 comparing with a control group of 101 age- and gender-matched normotensive children of normotensive parents. Pulse wave velocity, central systolic and diastolic blood pressure, systolic, diastolic and mean arterial blood pressure values were higher in the study group than the control group (p < 0.001, p = 0.002, p = 0.008, p = 0.001, p = 0.005, p = 0.001, p = 0.001, respectively). In all age groups (7–10, 11–14, and 15–18 years), pulse wave velocity was higher in the study group than the control group (p < 0.001). Pulse wave velocity was higher in children whose both parents are hypertensive compared to the children whose only mothers are hypertensive (p = 0.011). Pulse wave velocity values were positively correlated with age, weight, height, and body mass index (p < 0.05). Higher pulse wave velocity, central systolic and diastolic blood pressure values detected in the study group can be considered as early signs of hypertensive vascular changes. Pulse wave analysis can be a reliable, non-invasive, and reproducible method that can allow taking necessary precautions regarding lifestyle to prevent disease and target organ damage by detecting early hypertensive changes in genetically risky children.

Non-invasive Oscillometry-Based Estimation of Cardiac Output – Can We Use It in Clinical Practice?

While invasive thermodilution techniques remain the reference methods for cardiac output (CO) measurement, there is a currently unmet need for non-invasive techniques to simplify CO determination, reduce complications related to invasive procedures required for indicator dilution CO measurement, and expand the application field toward emergency room, non-intensive care, or outpatient settings. We evaluated the performance of a non-invasive oscillometry-based CO estimation method compared to transpulmonary thermodilution. To assess agreement between the devices, we used Bland–Altman analysis. Four-quadrant plot analysis was used to visualize the ability of Mobil-O-Graph (MG) to track CO changes after a fluid challenge. Trending analysis of CO trajectories was used to compare MG and PiCCO® calibrated pulse wave analysis over time (6 h). We included 40 patients from the medical intensive care unit at the Charité – Universitätsmedizin Berlin, Campus Benjamin Franklin between November 2019 and June 2020. The median age was 73 years. Forty percent of the study population was male; 98% was ventilator-dependent and 75% vasopressor-dependent at study entry. The mean of the observed differences for the cardiac output index (COI) was 0.7 l∗min–1*m–2 and the lower, and upper 95% limits of agreement (LOA) were -1.9 and 3.3 l∗min–1*m–2, respectively. The 95% confidence interval for the LOA was ± 0.26 l∗min–1*m–2, the percentage error 83.6%. We observed concordant changes in CO with MG and PiCCO® in 50% of the measurements after a fluid challenge and over the course of 6 h. Cardiac output calculation with a novel oscillometry-based pulse wave analysis method is feasible and replicable in critically ill patients. However, we did not find clinically applicable agreement between MG and thermodilution or calibrated pulse wave analysis, respectively, assessed with established evaluation routine using the Bland–Altman approach and with trending analysis methods. In summary, we do not recommend the use of this method in critically ill patients at this time. As the basic approach is promising and the CO determination with MG very simple to perform, further studies should be undertaken both in hemodynamically stable patients, and in the critical care setting to allow additional adjustments of the underlying algorithm for CO estimation with MG.

Predicting arterial age using carotid ultrasound images, pulse wave analysis records, cardiovascular biomarkers and deep learning

Cardiovascular disease --an age-related disease-- is the leading cause of death worldwide. We built an arterial age predictor by training deep learning models to predict age from 233,388 pulse wave analysis records, 8,279 carotid ultrasound images and arterial health biomarkers (e.g blood pressure) collected from 502,000 UKB participants. We predicted age with a R-Squared of 67.1+/-0.6% and a root mean squared error of 4.29+/-0.04 years. Attention maps for carotid ultrasound images suggest that the predictions are driven by vascular features, for the largest part. Accelerated arterial aging is 32.6+/-7.3% GWAS-heritable, and we identified 192 single nucleotide polymorphisms in 109 genes (e.g NPR3, involved in blood volume and pressure) significantly associated with this phenotype. Similarly, we identified biomarkers (e.g electrocardiogram features), clinical phenotypes (e.g chest pain), diseases (e.g hypertension), environmental (e.g smoking) and socioeconomic (e.g income and education) variables associated with accelerated arterial aging. Finally, carotid ultrasound images, pulse wave analysis records and blood pressure biomarkers capture different facets of arterial aging. For example, carotid ultrasound-measured and pulse wave analysis-measured accelerated arterial aging phenotypes are only .164+/-.009 correlated. In conclusion, our predictor suggests potential lifestyle and therapeutic interventions to slow arterial aging, and could be used to assess the efficiency of emerging rejuvenating therapies on the arterial system.