Key Hemodynamic Parameters Research Articles

Direct comparison of mechanical circulatory support devices in porcine model of acute myocardial infarction complicated by ventricular septal defect and cardiogenic shock

Abstract Introduction Despite urgent clinical need, until now optimal strategy for mechanical circulatory support in acute myocardial infarction (AMI) complicated by ventricular septal defect (VSD) and cardiogenic shock has not been determined. Purpose The aim of our study was to compare hemodynamic effects of intra-aortic balloon pump (IABP), extracorporeal membrane oxygenation (ECMO) and Impella CP in the animal model of AMI complicated by VSD and cardiogenic shock. Methods Under general anesthesia and mechanical ventilation in pigs (sus scrofa domestica), using partial sternotomy, AMI was induced by ligation of left anterior descending. VSD was simulated by insertion of 25F cannulas into the left and right ventricles and their extracorporeal connection by 3/8 tubes generating left-to-right shunt. The hemodynamic effect of ECMO (2 L/min), IABP, and Impella CP (2 L/min) was evaluated by the measurement of the left ventricular end-diastolic pressure (LVEDP), mean arterial pressure (MAP), carotid flow (CAR) as a surrogate of total circulatory output, and VSD flow. Data are presented as mean (SD), P < 0.05 was considered statistically significant. Results Eleven animals were used, one died after induction of AMI, ten pigs completed all study procedures. Baseline (no mechanical support) LVEDP was 13.8 (3.0) mmHg, MAP 54.5 (11.0) mmHg, CAR 290.9 (37.3) mL/min, and VSD 1174.0 (499.3) mL/min (Table 1). IABP significantly improved all parameters in comparison to Baseline (Table 1). ECMO significantly improved MAP and CAR not only in comparison to Baseline but also in comparison to IABP and Impella; on the other hand, ECMO increased VSD flow (Table 1). Impella significantly improved all parameters with the largest effect on LVEDP and VSD flow (Table 1). Conclusions All tested devices improved the key hemodynamic parameters of MAP and CAR, however each to different extent. Individual devices influenced differently LVEDP and VSD flow. The results of this study may help in selection of optimal therapeutic strategy in AMI complicated by VSD and cardiogenic shock.Table 1.Hemodynamic outcomes.

Computational Modeling Approach to Profile Hemodynamical Behavior in a Healthy Aorta.

Cardiovascular diseases (CVD) remain the leading cause of mortality among older adults. Early detection is critical as the prognosis for advanced-stage CVD is often poor. Consequently, non-invasive diagnostic tools that can assess hemodynamic function, particularly of the aorta, are essential. Computational fluid dynamics (CFD) has emerged as a promising method for simulating cardiovascular dynamics efficiently and cost-effectively, using increasingly accessible computational resources. This study developed a CFD model to assess the aorta geometry using tetrahedral and polyhedral meshes. A healthy aorta was modeled with mesh sizes ranging from 0.2 to 1 mm. Key hemodynamic parameters, including blood pressure waveform, pressure difference, wall shear stress (WSS), and associated wall parameters like relative residence time (RRT), oscillatory shear index (OSI), and endothelial cell activation potential (ECAP) were evaluated. The performance of the CFD simulations, focusing on accuracy and processing time, was assessed to determine clinical viability. The CFD model demonstrated clinically acceptable results, achieving over 95% accuracy while reducing simulation time by up to 54%. The entire simulation process, from image construction to the post-processing of results, was completed in under 120 min. Both mesh types (tetrahedral and polyhedral) provided reliable outputs for hemodynamic analysis. This study provides a novel demonstration of the impact of mesh type in obtaining accurate hemodynamic data, quickly and efficiently, using CFD simulations for non-invasive aortic assessments. The method is particularly beneficial for routine check-ups, offering improved diagnostics for populations with limited healthcare access or higher cardiovascular disease risk.

Cardiac Magnetic Resonance Imaging for the Evaluation of Pulmonary Hypertension

Abstract Purpose Evaluate the utility of cardiac magnetic resonance imaging for noninvasive estimation of the key hemodynamic parameters that are measured by the right heart catheterization i.e. mean pulmonary artery pressure, pulmonary artery wedge pressure and pulmonary vascular resistance through cardiac magnetic resonance based numerical models. Materials and Methods 29 pulmonary hypertension patients, fitting the inclusion criteria were randomly selected and included in the study. CMR Imaging and right- sided heart catheterization (RHC) were performed within 1 month. 3 Cardiac MRI based models in literature that showed high accuracy were tested. Two equations for mPAP calculation; mPAP= - 231.423 + loge inter-ventricular septal angle X 53.8 + log10 ventricular mass index (right ventricular mass/left ventricular mass) X 8.708 + diastolic pulmonary artery area X 0.009 and mPAP = –4.6 + (interventricular septal angle × 0.23) + (ventricular mass index × 16.3). One equation for PAWP; PAWP = 6.43 + LA volume index x 0.22. Results The Pearson correlation test correlation between invasively quantified and CMR-estimated mPAP had good correlation with r = 0.594 and r = 0.599 (p < 0.001) for CMR based mPAP model 1 and 2, respectively. While, Altman and Bland test showed that The mean bias between the RHC-derived and CMR-estimated mPAP was 7.9 (agreement interval -24.8 to 40.6 mmHg) and mean bias -3 (agreement interval -34.8 to 28.2 mmHg) for CMR based mPAP model 1 and 2, respectively. There was no correlation between invasively quantified and CMR-estimated PAWP with (p = 0.092) for CMR based PAWP model. The mean bias between the RHC- derived and CMR-estimated PAWP was 2.4 (agreement interval –13.5 to 18.2 mmHg). The correlation between invasively calculated and CMR-estimated PVR had good correlation with r = 0.703 and r = 0.704 (p < 0.001) for CMR based PVR model 1 and 2, respectively. The mean bias between the RHC-derived and CMR- estimated mPAP was 0.6 (agreement interval -11.6 to 12.8 mmHg) and mean bias -1.3 (agreement interval -12.1 to 9.5 mmHg) for CMR based mPAP model 1 and 2, respectively. Conclusion our results showed good correlations between CMR findings and RHC as regard mPAP and PVR. Thus Non-invasive estimation of mPAP, PAWP and PVR using CMR is feasible but needs further studies to improve accuracy.

Evaluating the Efficacy of Interatrial Shunt Devices in Heart Failure With Preserved Ejection Fraction: A Systematic Review and Meta-Analysis.

Heart failure with preserved ejection fraction (HFpEF) is a prevalent and challenging condition with limited therapeutic options. This meta-analysis aims to assess the feasibility and effectiveness of interatrial shunt devices (IASD) in the treatment of HFpEF, focusing on key hemodynamic parameters and clinical outcomes. Six clinical trials, encompassing 324 patients, were included in this analysis. The results showed a significant reduction in pulmonary capillary wedge pressure (PCWP) at rest after IASD implantation, with a mean difference of 1.55 mm Hg. PCWP during exercise also exhibited a decrease, indicating improved exercise tolerance. However, there was an increase in mean right atrial pressure following IASD implantation. These findings suggest that IASD implementation can effectively lower left atrial pressure, a critical target in HFpEF management. This results in substantial clinical improvements, including enhanced New York Heart Association class, quality of life, and 6-minute walk distance. Echocardiographic assessments revealed a reduction in left ventricular end-diastolic volume index and stable right ventricular changes. The meta-analysis underscores the potential benefits of IASD in ameliorating the symptoms and clinical outcomes of HFpEF patients. The increase in mean right atrial pressure warrants further investigation into its effects on right heart function. Additionally, this analysis emphasizes the need for larger, randomized clinical trials to validate these findings and determine optimal patient selection criteria. IASD implantation holds promise as a therapeutic option for HFpEF, offering the potential to improve the quality of life and functional status of affected patients. However, further research is imperative to confirm its efficacy relative to existing treatments and to address concerns regarding its impact on right heart function. This meta-analysis contributes to a deeper understanding of IASD's role in HFpEF management.

Heart rate complexity helps mortality prediction in the intensive care unit: A pilot study using artificial intelligence

BackgroundIn intensive care units (ICUs), accurate mortality prediction is crucial for effective patient management and resource allocation. The Simplified Acute Physiology Score II (SAPS-2), though commonly used, relies heavily on comprehensive clinical data and blood samples. This study sought to develop an artificial intelligence (AI) model utilizing key hemodynamic parameters to predict ICU mortality within the first 24 h and assess its performance relative to SAPS-2. MethodsWe conducted an analysis of select hemodynamic parameters and the structure of heart rate curves to identify potential predictors of ICU mortality. A machine-learning model was subsequently trained and validated on distinct patient cohorts. The AI algorithm’s performance was then compared to the SAPS-2, focusing on classification accuracy, calibration, and generalizability. Measurements and main resultsThe study included 1298 ICU admissions from March 27th, 2015, to March 27th, 2017. An additional cohort from 2022 to 2023 comprised 590 patients, resulting in a total dataset of 1888 patients. The observed mortality rate stood at 24.0%. Key determinants of mortality were the Glasgow Coma Scale score, heart rate complexity, patient age, duration of diastolic blood pressure below 50 mmHg, heart rate variability, and specific mean and systolic blood pressure thresholds. The AI model, informed by these determinants, exhibited a performance profile in predicting mortality that was comparable, if not superior, to the SAPS-2. ConclusionsThe AI model, which integrates heart rate and blood pressure curve analyses with basic clinical parameters, provides a methodological approach to predict in-hospital mortality in ICU patients. This model offers an alternative to existing tools that depend on extensive clinical data and laboratory inputs. Its potential integration into ICU monitoring systems may facilitate more streamlined mortality prediction processes.

Abstract 17527: Plasma Proteomic Representations of Central Hemodynamics Over Time in Heart Failure

Introduction: Perturbation of central hemodynamic parameters are defining features in heart failure (HF) and strong prognostic factors, including increased filling pressures, reduced cardiac output (CI), extended blood circulation times and increased peripheral oxygen extraction (ΔAVO2). Invasive catheterization is used for precise assessment of central hemodynamics. Hypothesis: We postulated that plasma proteins may serve as non-invasive representations of central hemodynamics in HF patients. Methods: Affinity-based plasma proteomic profiling of 1305 proteins was applied to a cohort of patients with advanced HF undergoing invasive cardiac catheterization before and 6 months after heart transplantation. A total of 30 central hemodynamic parameters were measured and calculated. Protein profiles associated with central hemodynamics over time were identified from repeated-measures models. Results: We identified 1304 associations for 201 unique proteins meeting our Bonferroni-adjusted significance threshold (p<1x10-6) for at least one hemodynamic parameter. Most of the 201 proteins were associated with more than one parameter. Most proteins (70%) associated with parameters in more than one of five domains (filling pressures, left ventricular (LV) function, right ventricular (RV) function, systemic arterial function, pulmonary arterial function). No proteins were associated with 6 of the 30 hemodynamic parameters, which changed little after transplantation and related to arterial function or RV function. The largest number of protein associations were observed for RAP, cardiac power index, left ventricular stroke work index, and ΔAVO2 followed by systolic blood pressure, CI, and PAWP (>80). These include expected associations of natriuretic peptides, ST2 with LV function, filling pressures and of angiotensinogen with vascular function parameters. We further describe the discriminatory ability of identified proteins for clinically relevant differences in key hemodynamic parameters. Conclusions: We have identified circulating biomarkers to non-invasively monitor central hemodynamics in HF over time. Our findings may also inform understanding of pathways involved in HF pathophysiology and circulatory regulation.

Real-Time Seismocardiogram Feature Extraction Using Adaptive Gaussian Mixture Models.

Accurate and timely cardiovascular health evaluations can be provided by wearable systems that estimate key hemodynamic indices in real-time. A number of these hemodynamic parameters can be estimated non-invasively using the seismocardiogram (SCG), a cardiomechanical signal whose features can be linked to cardiac events such as aortic valve opening (AO) and aortic valve closing (AC). However, tracking a single SCG feature is often unreliable due to physiological state changes, motion artifacts, and external vibrations. In this work, an adaptable Gaussian Mixture Model (GMM) framework is proposed to concurrently track multiple AO or AC features in quasi-real-time from the measured SCG signal. For all extrema in a SCG beat, the GMM calculates the likelihood that an extremum is an AO/AC correlated feature. The Dijkstra algorithm is then used to isolate tracked heartbeat related extrema. Finally, a Kalman filter updates the GMM parameters, while filtering the features. Tracking accuracy is tested on a porcine hypovolemia dataset with various noise levels added. In addition, blood volume decompensation status estimation accuracy is evaluated using the tracked features on a previously developed model. Experimental results showed a 4.5 ms tracking latency per beat and an average AO and AC root mean square error (RMSE) of 1.47ms and 7.67ms respectively at 10dB noise and 6.18ms and 15.3ms at -10dB noise. When analyzing the tracking accuracy of all AO or AC correlated features, combined AO and AC RMSE remained in similar ranges at 2.70ms and 11.91ms respectively at 10dB noise and 7.50 and 16.35ms at - 10dB. The low latency and RMSE of all tracked features make the proposed algorithm suitable for real-time processing. Such systems would enable accurate and timely extraction of important hemodynamic indices for a multitude of cardiovascular monitoring applications, including trauma care in field settings.

Fully Automated Artificial Intelligence Assessment of Aortic Stenosis by Echocardiography

Aortic stenosis (AS) is a common form of valvular heart disease, present in over 12% of the population age 75years and above. Transthoracic echocardiography (TTE) is the first line of imaging in the adjudication of AS severity but is time-consuming and requires expert sonographic and interpretation capabilities to yield accurate results. Artificial intelligence (AI) technology has emerged as a useful tool to address these limitations but has not yet been applied in a fully hands-off manner to evaluate AS. Here, we correlate artificial neural network measurements of key hemodynamic AS parameters to experienced human reader assessment. Two-dimensional and Doppler echocardiographic images from patients with normal aortic valves and all degrees of AS were analyzed by an artificial neural network (Us2.ai) with no human input to measure key variables in AS assessment. Trained echocardiographers blinded to AI data performed manual measurements of these variables, and correlation analyses were performed. Our cohort included 256 patients with an average age of 67.6±9.5years. Across all AS severities, AI closely matched human measurement of aortic valve peak velocity (r=0.97, P<.001), mean pressure gradient (r=0.94, P<.001), aortic valve area by continuity equation (r=0.88, P<.001), stroke volume index (r=0.79, P<.001), left ventricular outflow tract velocity-time integral (r=0.89, P<.001), aortic valve velocity-time integral (r=0.96, P<.001), and left ventricular outflow tract diameter (r=0.76, P<.001). Artificial neural networks have the capacity to closely mimic human measurement of all relevant parameters in the adjudication of AS severity. Application of this AI technology may minimize interscan variability, improve interpretation and diagnosis of AS, and allow for precise and reproducible identification and management of patients with AS.

Development of an app-based bedside clinical decision-making tool for mechanical cardiocirculatory support in patients with cardiogenic shock: the MCS-Aid app

Abstract Background Patients presenting with cardiogenic shock remain at high risk of morbidity and mortality. Several mechanical cardiocirculatory support (MCS) devices have been developed and their use is rapidly increase in clinical practice. However, there is significant heterogeneity in patient selection, timing of implantation, and post-implantation management across centers and operators. Purpose We sought to develop and smartphone app-based clinical decision-making tool to help bedside selection and post-implantation management of MCS devices in patients presenting with cardiogenic shock or cardiac arrest. Methods The MCS-Aid app will consistent of 3 major sections: (i) initial device selection based on clinical presentation (patients with cardiogenic shock or cardiac arrest post-ROSC); (ii) guide for escalation or addition of MCS based on the individual hemodynamic scenario; (iii) guide for weaning after implantation of MCS device. The app will have an interactive interface that will allow the user to select the most appropriate next step in management based on the clinical information being entered. A calculator to derive key hemodynamic parameters (e.g. cardiac power output or pulmonary artery pulsatility index) will be incorporated in the App to inform clinical decision-making when appropriate. An example of an algorithm that will be part of the MCS-Aid app is illustrated in the figure. Conclusions The MCS-Aid app is an user-friendly bedside clinical decision tool that could help fellows-in-training, early-career interventionalist and interventional cardiologist to select the appropriate MCS device according to the individual clinical and hemodynamic scenario. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): Abiomed

The thromboxane receptor antagonist NTP42 promotes beneficial adaptation and preserves cardiac function in experimental models of right heart overload.

Pulmonary arterial hypertension (PAH) is a progressive disease characterized by increased pulmonary artery pressure leading to right ventricular (RV) failure. While current PAH therapies improve patient outlook, they show limited benefit in attenuating RV dysfunction. Recent investigations demonstrated that the thromboxane (TX) A2 receptor (TP) antagonist NTP42 attenuates experimental PAH across key hemodynamic parameters in the lungs and heart. This study aimed to validate the efficacy of NTP42:KVA4, a novel oral formulation of NTP42 in clinical development, in preclinical models of PAH while also, critically, investigating its direct effects on RV dysfunction. The effects of NTP42:KVA4 were evaluated in the monocrotaline (MCT) and pulmonary artery banding (PAB) models of PAH and RV dysfunction, respectively, and when compared with leading standard-of-care (SOC) PAH drugs. In addition, the expression of the TP, the target for NTP42, was investigated in cardiac tissue from several other related disease models, and from subjects with PAH and dilated cardiomyopathy (DCM). In the MCT-PAH model, NTP42:KVA4 alleviated disease-induced changes in cardiopulmonary hemodynamics, pulmonary vascular remodeling, inflammation, and fibrosis, to a similar or greater extent than the PAH SOCs tested. In the PAB model, NTP42:KVA4 improved RV geometries and contractility, normalized RV stiffness, and significantly increased RV ejection fraction. In both models, NTP42:KVA4 promoted beneficial RV adaptation, decreasing cellular hypertrophy, and increasing vascularization. Notably, elevated expression of the TP target was observed both in RV tissue from these and related disease models, and in clinical RV specimens of PAH and DCM. This study shows that, through antagonism of TP signaling, NTP42:KVA4 attenuates experimental PAH pathophysiology, not only alleviating pulmonary pathologies but also reducing RV remodeling, promoting beneficial hypertrophy, and improving cardiac function. The findings suggest a direct cardioprotective effect for NTP42:KVA4, and its potential to be a disease-modifying therapy in PAH and other cardiac conditions.

Development of an app-based bedside clinical decision-making tool for mechanical cardiocirculatory support in patients with cardiogenic shock: the MCS-Aid app

Abstract Background Patients presenting with cardiogenic shock remain at high risk of morbidity and mortality. Several mechanical cardiocirculatory support (MCS) devices have been developed and their use is rapidly increase in clinical practice. However, there is significant heterogeneity in patient selection, timing of implantation, and post-implantation management across centers and operators. Purpose We sought to develop and smartphone app-based clinical decision-making tool to help bedside selection and post-implantation management of MCS devices in patients presenting with cardiogenic shock or cardiac arrest. Methods The MCS-Aid app will consistent of 3 major sections: (i) initial device selection based on clinical presentation (patients with cardiogenic shock or cardiac arrest post-ROSC); (ii) guide for escalation or addition of MCS based on the individual hemodynamic scenario; (iii) guide for weaning after implantation of MCS device. The app will have an interactive interface that will allow the user to select the most appropriate next step in management based on the clinical information being entered. A calculator to derive key hemodynamic parameters (e.g. cardiac power output or pulmonary artery pulsatility index) will be incorporated in the App to inform clinical decision-making when appropriate. An example of an algorithm that will be part of the MCS-Aid app is illustrated in the figure. Conclusions The MCS-Aid app is an user-friendly bedside clinical decision tool that could help fellows-in-training, early-career interventionalist and interventional cardiologist to select the appropriate MCS device according to the individual clinical and hemodynamic scenario. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): Abiomed

Computational modeling of orthostatic intolerance for travel to Mars

Astronauts in a microgravity environment will experience significant changes in their cardiopulmonary system. Up until now, there has always been the reassurance that they have real-time contact with experts on Earth. Mars crew however will have gaps in their communication of 20 min or more. In silico experiments are therefore needed to assess fitness to fly for those on future space flights to Mars. In this study, we present an open-source controlled lumped mathematical model of the cardiopulmonary system that is able simulate the short-term adaptations of key hemodynamic parameters to an active stand test after being exposed to microgravity. The presented model is capable of adequately simulating key cardiovascular hemodynamic changes—over a short time frame—during a stand test after prolonged spaceflight under different gravitational conditions and fluid loading conditions. This model can form the basis for further exploration of the ability of the human cardiovascular system to withstand long-duration space flight and life on Mars.

Isolating the Effect of Arch Architecture on Aortic Hemodynamics Late After Coarctation Repair: A Computational Study.

ObjectivesEffective management of aortic coarctation (CoA) affects long-term cardiovascular outcomes. Full appreciation of CoA hemodynamics is important. This study aimed to analyze the relationship between aortic shape and hemodynamic parameters by means of computational simulations, purposely isolating the morphological variable.MethodsComputational simulations were run in three aortic models. MRI-derived aortic geometries were generated using a statistical shape modeling methodology. Starting from n = 108 patients, the mean aortic configuration was derived in patients without CoA (n = 37, “no-CoA”), with surgically repaired CoA (n = 58, “r-CoA”) and with unrepaired CoA (n = 13, “CoA”). As such, the aortic models represented average configurations for each scenario. Key hemodynamic parameters (i.e., pressure drop, aortic velocity, vorticity, wall shear stress WSS, and length and number of strong flow separations in the descending aorta) were measured in the three models at three time points (peak systole, end systole, end diastole).ResultsComparing no-CoA and CoA revealed substantial differences in all hemodynamic parameters. However, simulations revealed significant increases in vorticity at the site of CoA repair, higher WSS in the descending aorta and a 12% increase in power loss, in r-CoA compared to no-CoA, despite no clinically significant narrowing (CoA index >0.8) in the r-CoA model.ConclusionsSmall alterations in aortic morphology impact on key hemodynamic indices. This may contribute to explaining phenomena such as persistent hypertension in the absence of any clinically significant narrowing. Whilst cardiovascular events in these patients may be related to hypertension, the role of arch geometry may be a contributory factor.

Reducing the Impact of External Vibrations on Fiducial Point Detection in Seismocardiogram Signals.

Wearable systems that enable continuous non-invasive monitoring of hemodynamic parameters can aid in cardiac health evaluation in non-hospital settings. The seismocardiogram (SCG) is a non-invasively acquired cardiovascular biosignal for which timings of fiducial points, like aortic valve opening (AO) and aortic valve closing (AC), can enable estimation of key hemodynamic parameters. However, SCG is susceptible to motion artifacts, making accurate estimation of these points difficult when corrupted by high-g or in-band vibration artifacts. In this paper, a novel denoising pipeline is proposed that removes vehicle-vibration artifacts from corrupted SCG beats for accurate fiducial point detection. The noisy SCG signal is decomposed with ensemble empirical mode decomposition (EEMD). Corrupted segments of the decomposed signal are then identified and removed using the quasi-periodicity of the SCG. Signal quality assessment of the reconstructed SCG beats then removes unreliable beats before feature extraction. The overall approach is validated on simulated vehicle-corrupted SCG generated by adding real subway collected vibration signals onto clean SCG. SNR increased by 8.1dB in the AO complex and 11.5dB in the AC complex of the SCG signal. Hemodynamic timing estimation errors reduced by 16.5% for pre-ejection period (PEP), 67.2% for left ventricular ejection time (LVET), and 57.7% for PEP/LVET-a feature previously determined in prior work to be of great importance for assessing blood volume status during hemorrhage. These findings suggest that usable SCG signals can be recovered from vehicle-corrupted SCG signals using the presented denoising framework, allowing for accurate hemodynamic timing estimation. Reliable hemodynamic estimates from vehicle-corrupted SCG signals will enable the adoption of the SCG in outside-of-hospital settings.

Clinical Relevance of Right Atrial Functional Response to Treatment in Pulmonary Arterial Hypertension

Background: Right atrial (RA) function has emerged as an important determinant of outcome in pulmonary arterial hypertension (PAH). However, studies exploring RA function after initiation of specific pulmonary vascular treatment and its association with outcome in patients with incident PAH are lacking.Methods: RA peak longitudinal strain (PLS), passive strain (PS), and peak active contraction strain (PACS) were retrospectively assessed in 56 treatment-naïve patients with PAH at baseline and during follow-up after initiation of specific monotherapy or combination therapy. Patients were grouped according to their individual RA functional response to treatment, based on change from baseline (Δ): worsened (first Δ-tertile), stable (second Δ-tertile), and improved (third Δ-tertile). The Spearman's rho correlation and linear regression analysis were used to determine associations. Time to clinical worsening (defined as deterioration of functional class or 6-min walking distance, disease-related hospital admission, or death) was measured from the follow-up assessment. The association of RA functional treatment response with time to clinical worsening was assessed using the Kaplan–Meier and the Cox regression analyses.Results: Median (interquartile range) time to echocardiographic follow-up was 11 (9–12) months. Of the 56 patients, 37 patients (66%) received specific dual or triple combination therapy. Δ RA PLS during follow-up was significantly associated with changes in key hemodynamic and echocardiographic parameters. The change of pulmonary vascular resistance, right ventricular (RV) end-systolic area, and global longitudinal strain were independently associated with Δ RA PLS. The median time to clinical worsening after echocardiographic follow-up was 6 (2–14) months [17 events (30%)]. In the multivariate Cox regression analysis, worsening of RA PLS was significantly associated with clinical deterioration (hazard ratio: 4.87; 95% CI: 1.26–18.76; p = 0.022). Patients with worsened RA PLS had a significantly poorer prognosis than those with stable or improved RA PLS (log-rank p = 0.012). By contrast, PS and PACS did not yield significant prognostic information.Conclusion: Treatment-naïve patients with PAH may show different RA functional response patterns to PAH therapy. These functional patterns are significantly associated with clinically relevant outcome measures. Improvements of RA function are driven by reductions of afterload, RV remodeling, and RV dysfunction.

Continuous monitoring of advanced hemodynamic parameters shows early cardiovascular changes in a cohort of 492 COVID-19 hospitalized patients

Abstract Background COVID-19 exerts deleterious cardiopulmonary effects, leading to worse prognosis in the most effected. Purpose The aim of this retrospective multi-center observational cohort study was to analyze the trajectories of key advanced hemodynamic parameters amongst hospitalized COVID-19 patients according to different risk populations using a chest-patch wearable providing continuous remote patient monitoring. Methods The study was conducted in five COVID-19 isolation units. Patients admitted to the units were connected to a photoplethysmography based noninvasive remote advanced hemodynamic monitor after completing a basic risk factor survey. Physiological parameters were measured every 15 minutes during the hospitalization, including cardiac output (CO), cardiac index (CI), systemic vascular resistance (SVR), heart rate, blood pressure (BP), respiratory rate, blood oxygen saturation (SpO2), and body temperature. Results 492 COVID-19 patients (179 females, average age 58.7 years) were included in the final analysis, with more than 3 million measurements collected during an average of 75.3 hours. Overall, within the first five days of hospitalizations we found a significant increase in SVR, and a significant decrease in SpO2, DBP, CO and CI (p&amp;lt;0.01 for all). The changes were more prominent in high risk populations- males, older age and obesity and had a temporal correspondence to changes in respiratory parameters. Conclusions This is the first comprehensive continuous advanced hemodynamic profiling of COVID-19 patients. Worse hemodynamic status was prominent in high risk populations. Funding Acknowledgement Type of funding sources: None.

Mildly Reduced Doses of Adrenaline Do Not Affect Key Hemodynamic Parameters during Cardio-Pulmonary Resuscitation in a Pig Model of Cardiac Arrest.

Adrenaline is recommended for cardiac arrest resuscitation, but its effectiveness has been questioned recently. Achieving return of spontaneous circulation (ROSC) is essential and is obtained by increasing coronary perfusion pressure (CPP) after adrenaline injection. A threshold as high as 35 mmHg of CPP may be necessary to obtain ROSC, but increasing doses of adrenaline might be harmful to the brain. Our study aimed to compare the increase in CPP with reduced doses of adrenaline to the recommended 1 mg dose in a pig model of cardiac arrest. Fifteen domestic pigs were randomized into three groups according to the adrenaline doses: 1 mg, 0.5 mg, or 0.25 mg administered every 5 min. Cardiac arrest was induced by ventricular fibrillation; after 5 min of no-flow, mechanical chest compression was resumed. The Wilcoxon test and Kruskal–Wallis exact test were used for the comparison of groups. Fisher’s exact test was used to compare categorical variables. CPP, EtCO2 level, cerebral, and tissue near-infrared spectroscopy (NIRS) were measured. CPP was significantly lower in the 0.25 mg group 90 s after the first adrenaline injection: 28.9 (21.2; 35.4) vs. 53.8 (37.8; 58.2) in the 1 mg group (p = 0.008), while there was no significant difference with 0.5 mg 39.6 (32.7; 52.5) (p = 0.056). Overall, 0.25 mg did not achieve the threshold of 35 mmHg. EtCO2 levels were higher at T12 and T14 in the 0.5 mg than in the standard group: 32 (23; 35) vs. 19 (16; 26) and 26 (20; 34) vs. 19 (12; 22) (p < 0.05). Cerebral and tissue NIRS did not show a significant difference between the three groups. CPP after 0.5 mg boluses of adrenaline was not significantly different from the recommended 1 mg in our model of cardiac arrest.

Continuous cardiovascular hemodynamics monitoring with pressure recording analytical method in patients under spinal anesthesia for elective cesarean section: a pilot study.

Pressure recording analytical method (PRAM) of MOSTCARE is a minimally invasive system based on mathematical analysis of arterial pressure profile changes and allows continuous recording of hemodynamic measurements. Herein, we aimed to use the MostCare system to investigate the hemodynamics of patients receiving spinal anesthesia during elective cesarean section. In this observational study, we recruited 17 patients scheduled for elective cesarean section. For each patient, we acquired continuous recordings of hemodynamic parameters at 14 key timepoints: surgical admission (two baseline parameters), immediately after the administration of spinal anesthesia, six subsequent time points (2, 4, 6, 8, 10, and 12 min after anesthesia), during peritoneum and uterine incision, during delivery, following the administration of oxytocin, and after surgery had been completed. Statistical analysis was carried out by repeated measures analysis of variance (ANOVA). During a twelve min period after spinal anesthesia, we observed significant reductions in mean arterial pressure (MAP), dicrotic pressure (Pdic), cardiac index (CI), stroke volume index (SVI), dP/dtmax, and cardiac cycle efficiency (CCE) (P<0.05 for all). However, systemic vascular resistance index (SVRI) was reversely correlated to CI and increased significantly after spinal anesthesia (P<0.05). These hemodynamic parameters return to near basal values after peritoneum incision. Furthermore, there were significant fluctuations in MAP, Pdic and CI after oxytocin administration (P<0.05). PRAM of MostCare system revealed significant changes in key hemodynamic parameters undergoing cesarean section with spinal anesthesia. It enables clinicians gain a much better understanding of hemodynamics of parturients and optimize clinical management strategies. This study was registered at http://www.chictr.org.cn on 16, July, 2019. No. ChiCTR1900024566.

P3670Efficacy of the novel thromboxane receptor antagonist NTP42 alone, or in combination with Sildenafil, in the sugen/hypoxia-induced model of pulmonary arterial hypertension

Abstract Pulmonary arterial hypertension (PAH) is characterized by an elevated pulmonary vascular resistance resulting from excessive pulmonary arterial vasoconstriction and vascular remodelling, ultimately leading to right ventricular hypertrophy and right heart failure. NTP42 is a novel antagonist of the thromboxane (TX)A2 receptor (TP), currently in development for the treatment of PAH. Signalling through the TP, TXA2 is a potent vasoconstrictor, a driver of platelet aggregation and a pro-mitogenic and pro-inflammatory mediator. Moreover, the TP also mediates the actions of the isoprostane 8-iso-prostaglandin F2 alpha, a free-radical-derived product of arachidonic acid produced in abundance during oxidative injury. Mechanistically, TP antagonists should treat many of the hallmarks of PAH, including inhibiting the excessive vasoconstriction and pulmonary artery remodelling, in situ thrombosis, fibrosis and inflammation. The aim of the study was to evaluate the effects of NTP42 when used alone, or as a dual-therapy in combination with a PAH standard-of-care (Sildenafil). Alongside control animals maintained in normoxia, PAH was induced in rats by injection of Sugen5416 (20 mg/kg) and exposure to hypoxia (10% O2) for 21 days. Thereafter, animals were returned to normoxia and treated for 28 days with either vehicle, NTP42 (0.05 mg/kg BID), Sildenafil (50 mg/kg BID), or NTP42+Sildenafil (0.05 mg/kg + 50 mg/kg BID, respectively). While the standard-of-care PAH drug Sildenafil or NTP42 when used in mono-therapy led to non-significant reductions in the SuHx-induced rises in mean pulmonary arterial pressure (mPAP) or right ventricular systolic pressure (RSVP), combined use of NTP42+Sildenafil in dual-therapy significantly reduced these increases in mPAP and RVSP. Moreover, NTP42+Sildenafil also significantly reduced the SuHx-induced increase in cardiac hypertrophy. Detailed morphometric analysis of vessel remodelling confirmed that while both NTP42 and Sildenafil mono-therapy resulted in significant benefits, combined use of NTP42+Sildenafil showed an even greater benefit over either drug used in mono-therapy. Moreover, a multiparameter score of key PAH cardiac and pulmonary disease indices show that NTP42+Sildenafil when used in combination results in a highly significant treatment benefit, indicating a potential synergistic effect. Assessment of key hemodynamic, cardiac hypertrophy and pulmonary vascular remodelling parameters shows equivalent or greater efficacy of the TP antagonist NTP42 compared with the standard-of-care Sildenafil when used as monotherapies in the SuHx-induced preclinical model of PAH. Combined use of both drugs in dual therapy form confirms an even greater benefit in treating or offsetting the key aetiologies underlying PAH. These findings suggest that NTP42 and antagonism of TP signalling may alleviate PAH pathophysiology, representing a novel therapeutic for use in mono- dual- or triple-therapy regimens. Acknowledgement/Funding H2020 SME-Instrument Grant 822258

Neural Network of the gestosis diagnosis system

Introduction. The work is devoted to the increase of the information content of the methods of express diagnostics of the state of the cardiovascular system of pregnant women by developing a diagnostic monitoring system and assessment of the state of hemodynamics of pregnant women due to the use of neural network technologies. Currently, gestosis is one of the most urgent problems of modern obstetrics due to the prevalence and complexity of etiopathogenesis, the absence of early and reliable diagnostic criteria, effective prevention and treatment measures, high maternal and perinatal morbidity and mortality, as well as large economic costs of intensive care for patients. The proposed approach to optimization of the diagnosis of preeclampsia using registration key hemodynamic parameters, which allows to objectively evaluate the hemodynamics in pregnant women with preeclampsia, to determine the type of hemodynamics in pregnant women and to monitor the effectiveness of the therapy. Neural network technologies for the analysis and processing of medical data. The synthetic structure of the artificial neural network has shown the effectiveness of its use for diagnosis of gestation, using real clinical data. The basis of the diagnostic system was the artificial neural network, using the application package Statistica. Hybrid network as an adaptive system of neuro-fuzzy output system. In order to diagnose gestosis, we synthesized a neural network that allowed us to classify the pathologies of pregnant women on the basis of oscillometric data, using neural-fuzzy modeling in the Matlab environment. In the framework of this work, the ANFIS editor was used to build the model that would help create or load a specific model of the adaptive neuro-fuzzy inference system, perform its training, visualize its structure, change and adjust its parameters, and use the configured network to obtain the results of fuzzy inference. The findings of the tests, show that with the introduction of 7 basic parameters characterizing the state of a pregnant woman, the produced result is in the form of a definition of the type of hemodynamics and the degree of gestosis. The determination of the type of hemodynamics and the degree of gestosis allows the diagnostician and the obstetrician-gynecologist to assess the critical state of the pregnant woman at the time of the study and to take the necessary treatment measures. Conclusion. In order to diagnose gestosis, a neural network was synthesized, which allowed classifying the pathology of pregnant women using neuro-fuzzy simulation in a Matlab environment. The application of the proposed system helps choose the tactics of treating a patient diagnosed with gestosis according to individually selected therapy, monitoring its effectiveness, which will have a positive effect on the course and outcome of pregnancy; most importantly, controlling the state of maternal hemodynamics will eliminate the unreasonable use of medications.