Central Pressure Waveform Research Articles

Assessment of the relationship between central venous pressure waveform and the severity of tricuspid valve regurgitation using data science

Purpose The relationship between the height of the V wave in the central venous pressure (CVP) waveform and the severity of tricuspid regurgitation (TR) is well known. Their diagnostic ability is unconfirmed. This study explored CVP waveform variations with TR. Methods All patients who underwent preoperative echocardiography and CVP waveform measurements before surgery at our institution were included. Indices were created to capture each feature of the CVP waveform. The median value for each case was obtained and statistically analyzed according to the severity of TR. A deep learning technique, Transformer, was used to handle the complex features of CVP waveforms. Results This study included 436 cases. The values for C wave – Y descent, X descent – Y descent, and V wave – Y descent differed significantly in the Jonckheere–Terpstra test (p = 0.0018, 0.027, and 0.077, respectively). The area under the receiver operating characteristic (ROC) curve (AUC) for X descent – Y descent in two groups, none to moderate TR and severe TR, was 0.83 (95% confidence interval (CI) [0.68, 0.98]). For Transformer, the accuracy of the validation dataset was 0.97. Conclusions The shape of the CVP waveform varied with the severity of TR in a large dataset.

Acute Cardiovascular Responses to the 100-Mile Western States Endurance Run.

To determine the acute effects of trail ultramarathon participation on central artery stiffness and hemodynamics. Forty-one participants (9F, 32M) participating in the 2023 Western States Endurance Run underwent measures of carotid-femoral pulse wave velocity (cf-PWV) and pulse wave analysis pre- and <1h post-race. Subendocardial viability ratio (SEVR) was calculated from central blood pressure (BP) waveforms. Serum was analyzed for creatine kinase (CK) activity as a measure of muscle damage. Normally distributed data are presented as mean±SD and non-normally distributed data are presented as median (interquartile range). Runners were middle-aged and generally lean (age=44±9 y, BMI=22.7±1.8 kg●m-2). There was no difference in cf-PWV from pre- to post-race (pre=6.4±1.0, post=6.2±0.85 m/s, p=0.104), a finding that persisted after adjusting for mean arterial pressure (p=0.563). Systolic and diastolic BP were lower post-race (pre=129/77±9/7, post=122/74±10/8 mmHg, ps<0.001). Augmentation index (AIx; pre=17.3±12.2, post=6.0±13.7%, p<0.001), AIx normalized to a heart rate of 75bpm (p=0.043), reflection magnitude (pre=55.5(49.0-60.8), post=45.5(41.8-48.8) %, p<0.001), and SEVR (pre=173.0(158.0-190.0), post=127.5(116.5-145.8) %, p<0.001) were reduced post-race. CK increased markedly from pre- to post-race (pre=111(85-162), post=11,973(5,049-17,954) U/L, p<0.001). Completing a 161-km trail ultramarathon does not affect central arterial stiffness and acutely reduces BP despite eliciting profound muscle damage. However, the reduced post-race SEVR suggests a short-term mismatch between myocardial work and coronary artery perfusion.

Predicting stroke volume variation using central venous pressure waveform: a deep learning approach

Objective. This study evaluated the predictive performance of a deep learning approach to predict stroke volume variation (SVV) from central venous pressure (CVP) waveforms.Approach. Long short-term memory (LSTM) and the feed-forward neural network were sequenced to predict SVV using CVP waveforms obtained from the VitalDB database, an open-source registry. The input for the LSTM consisted of 10 s CVP waveforms sampled at 2 s intervals throughout the anesthesia duration. Inputs of the feed-forward network were the outputs of LSTM and demographic data such as age, sex, weight, and height. The final output of the feed-forward network was the SVV. The performance of SVV predicted by the deep learning model was compared to SVV estimated derived from arterial pulse waveform analysis using a commercialized model, EV1000.Main results. The model hyperparameters consisted of 12 memory cells in the LSTM layer and 32 nodes in the hidden layer of the feed-forward network. A total of 224 cases comprising 1717 978 CVP waveforms and EV1000/SVV data were used to construct and test the deep learning models. The concordance correlation coefficient between estimated SVV from the deep learning model were 0.993 (95% confidence interval, 0.992-0.993) for SVV measured by EV1000.Significance. Using a deep learning approach, CVP waveforms can accurately approximate SVV values close to those estimated using commercial arterial pulse waveform analysis.

Central venous pressure waveform analysis during sleep/rest: a novel approach to enhance intensive care unit post-extubation monitoring of extubation failure.

This pilot study aimed to investigate the relation between cardio-respiratory parameters derived from Central Venous Pressure (CVP) waveform and Extubation Failure (EF) in mechanically ventilated ICU patients during post-extubation period. This study also proposes a new methodology for analysing these parameters during rest/sleep periods to try to improve the identification of EF. We conducted a prospective observational study, computing CVP-derived parameters including breathing effort, spectral analyses, and entropy in twenty critically ill patients post-extubation. The Dynamic Warping Index (DWi) was calculated from the respiratory component extracted from the CVP signal to identify rest/sleep states. The obtained parameters from EF patients and patients without EF were compared both during arbitrary periods and during reduced DWi (rest/sleep). We have analysed data from twenty patients of which nine experienced EF. Our findings may suggest significantly increased respiratory effort in EF patients compared to those successfully extubated. Our study also suggests the occurrence of significant change in the frequency dispersion of the cardiac signal component. We also identified a possible improvement in the differentiation between the two groups of patients when assessed during rest/sleep states. Although with caveats regarding the sample size, the results of this pilot study may suggest that CVP-derived cardio-respiratory parameters are valuable for monitoring respiratory failure during post-extubation, which could aid in managing non-invasive interventions and possibly reduce the incidence of EF. Our findings also indicate the possible importance of considering sleep/rest state when assessing cardio-respiratory parameters, which could enhance respiratory failure detection/monitoring.

A new methodology for determining the central pressure waveform from peripheral measurement using Fourier-based machine learning

Radial applanation tonometry is a well-established technique for hemodynamic monitoring and is becoming popular in affordable non-invasive wearable healthcare electronics. To assess the central aortic pressure using radial-based measurements, there is an essential need to develop mathematical approaches to estimate the central pressure waveform. In this study, we propose a new Fourier-based machine learning (F-ML) methodology to transfer non-invasive radial pressure measurements to the central pressure waveform. To test the method, collection of tonometry recordings of the radial and carotid pressure measurements are used from the Framingham Heart Study (2640 individuals, 55 % women) with mean (range) age of 66 (40–91) years. Method-derived estimates are significantly correlated with the measured ones for three major features of the pressure waveform (systolic blood pressure, r=0.97, p < 0.001; diastolic blood pressure, r=0.99, p < 0.001; and mean blood pressure, r=0.99, p < 0.001). In all cases, the Bland-Altman analysis shows negligible bias in the estimations and error is bounded to 5.4 mmHg. Findings also suggest that the F-ML approach reconstructs the shape of the central pressure waveform accurately with the average normalized root mean square error of 5.5 % in the testing population which is blinded to all stages of machine learning development. The results show that the F-ML transfer function outperforms the conventional generalized transfer function, particularly in terms of reconstructing the shape of the central pressure waveform morphology. The proposed F-ML transfer function can provide accurate estimates for the central pressure waveform, and ultimately expand the usage of non-invasive devices for central hemodynamic assessment.

Age and Sex Differences in the Contribution of Mean Arterial Pressure to Pulse Pressure Before Middle Age.

A lower ability to buffer pulse pressure (PP) in the face of increasing mean arterial pressure (MAP) may underlie the disproportionate increase in systolic blood pressure (SBP) in women from young adulthood through middle-aged relative to men. To evaluate the contribution of MAP to the change in PP and pressure wave contour in men and women from young adulthood to middle age. Central pressure waveform was obtained from radial artery applanation tonometry in 312 hypertensive patients between 16 to 49 years (134 women, mean age 35 ± 9 years), 185 of whom were on antihypertensive treatment. Higher MAP levels (≥ 100 mmHg) were significantly associated with higher brachial and central SBP (P < 0.001), PP (P < 0.001), incident wave (P = 0.005), AP (P < 0.001), and PWV (P < 0.001) compared to lower MAP levels. The relationship between MAP and brachial PP (P < 0.001), central PP (P < 0.001), incident wave (P < 0.001), and AP (P < 0.01), but not PWV, strengthens with age. The age-related increase in the contribution of MAP to brachial PP (P < 0.001), central PP (P < 0.001), and incident wave (P < 0.001) was more prominent in women than in men beginning in the fourth decade. In multiple regression analyses, MAP remained a significantly stronger predictor of central PP and incident wave in women than in men, independent of age, heart rate, and antihypertensive treatment. In turn, age remained a significantly stronger predictor of central PP and incident wave in women than in men, independent of MAP, heart rate, and antihypertensive treatment. Women of reproductive age showed a steeper increase in PP with increasing MAP, despite comparable increases in arterial stiffness in both sexes. The difference was driven by a greater contribution of MAP to the forward component of the pressure wave in women.

The influence of perceived discrimination on aortic hemodynamics in young Black adults

Black adults experience the greatest prevalence of cardiovascular disease (CVD) risk factors and higher CVD mortality of any racial or ethnic group in the United States. The chronic stress from experiencing racial discrimination may exert a weathering effect that contributes to cardiovascular health inequities observed among Black adults. Prior evidence has suggested that Black adults experience greater aortic stiffness as compared with white adults. However, there is limited information regarding perceived racial discrimination and aortic hemodynamics in young adults. Purpose/Hypothesis: The purpose of this study was to determine whether perceived racial discrimination was associated with worse aortic hemodynamics in young Black adults. Methods: Black adults aged 18-25 (n=18, 44% female) volunteered to participate in this study. Participants arrived at the lab at 7:00AM in a fasted state. Following 30 minutes of rest, central pressure waveforms were derived from the radial artery waveform acquired via applanation tonometry and the use of the generalized transfer function. We report aortic augmentation pressure (AP, mmHg), augmentation index (AIx, %), augmentation index normalized to a heart rate of 75bpm (AIx75), round trip transit time (Tr, ms), and the subendocardial viability ratio (SEVR, %), also known as the Buckberg Index. Peak oxygen consumption (VO2 peak) was measured via Indirect calorimetry during a graded treadmill test. Participants completed a questionnaire that assessed personal-level and group-level discrimination. Partial correlations (Pearson’s r controlled for VO2 peak) were used to assess the association between perceived racial discrimination and aortic hemodynamics. Results: Perceived group-level discrimination was positively associated with AP (r = 0.571, p = 0.026), AIx (r = 0.580, p = 0.023), and AIx75 (r = 0.571, p = 0.026), signifying worse central hemodynamics that are indicative of increased left ventricular systolic load. However, perceived personal-level discrimination was not associated with aortic hemodynamics (ps&gt;0.05). VO2peak was positively associated with Tr (r = 0.511, p = 0.043), signifying a later return of the reflected wave (i.e, better) to the heart to facilitate coronary perfusion during diastole. VO2 peak was also inversely associated with AIx75 (r = -0.657, p = 0.006). Conclusion: These preliminary data indicate that young Black adults who report higher group-level discrimination have an increased left ventricular systolic afterload in the aorta. Future studies investigating coping strategies or the effects of increased fitness to improve central hemodynamics in Black adults are warranted. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

A multimodal deep learning tool for detection of junctional ectopic tachycardia in children with congenital heart disease

Junctional ectopic tachycardia (JET) is a prevalent life-threatening arrhythmia in children with congenital heart disease. It has a marked resemblance to normal sinus rhythm, often leading to delay in diagnosis and management. The study sought to develop a novel multimodal automated arrhythmia detection tool that outperforms existing JET detection tools. This is a cohort study performed on 40 patients with congenital heart disease at Texas Children's Hospital. Electrocardiogram and central venous pressure waveform data produced by bedside monitors are captured by the Sickbay platform. Convolutional neural networks (CNNs) were trained to classify each heartbeat as either normal sinus rhythm or JET based only on raw electrocardiogram signals. Our best model improved the area under the curve from 0.948 to 0.952 and the true positive rate at 5% false positive rate from 71.8% to 80.6%. Using a 3-model ensemble further improved the area under the curve to 0.953 and the true positive rate at 5% false positive rate to 85.2%. Results on a subset of data show that adding central venous pressure can significantly improve area under the receiver-operating characteristic curve from 0.646 to 0.825. This study validates the efficacy of deep neural networks to notably improve JET detection accuracy. We have built a performant and reliable model that can be used to create a bedside alarm that diagnoses JET, allowing for precise diagnosis of this life-threatening postoperative arrhythmia and prompt intervention. Future validation of the model in a larger cohort is needed.

Validation of a Suprasystolic Cuff System for Static and Dynamic Representation of the Central Pressure Waveform.

Noninvasive pulse waveform analysis is valuable for central cardiovascular assessment, yet controversies persist over its validity in peripheral measurements. Our objective was to compare waveform features from a cuff system with suprasystolic blood pressure hold with an invasive aortic measurement. This study analyzed data from 88 subjects undergoing concurrent aortic catheterization and brachial pulse waveform acquisition using a suprasystolic blood pressure cuff system. Oscillometric blood pressure (BP) was compared with invasive aortic systolic BP and diastolic BP. Association between cuff and catheter waveform features was performed on a set of 15 parameters inclusive of magnitudes, time intervals, pressure-time integrals, and slopes of the pulsations. The evaluation covered both static (subject-averaged values) and dynamic (breathing-induced fluctuations) behaviors. Peripheral BP values from the cuff device were higher than catheter values (systolic BP-residual, 6.5 mm Hg; diastolic BP-residual, 12.4 mm Hg). Physiological correction for pressure amplification in the arterial system improved systolic BP prediction (r2=0.83). Dynamic calibration generated noninvasive BP fluctuations that reflect those invasively measured (systolic BP Pearson R=0.73, P<0.001; diastolic BP Pearson R=0.53, P<0.001). Static and dynamic analyses revealed a set of parameters with strong associations between catheter and cuff (Pearson R>0.5, P<0.001), encompassing magnitudes, timings, and pressure-time integrals but not slope-based parameters. This study demonstrated that the device and methods for peripheral waveform measurements presented here can be used for noninvasive estimation of central BP and a subset of aortic waveform features. These results serve as a benchmark for central cardiovascular assessment using suprasystolic BP cuff-based devices and contribute to preserving system dynamics in noninvasive measurements.

Using central venous pressure waveform to confirm the placement of an internal jugular central venous catheter in the intensive care unit

BackgroundA mobile chest X-ray is traditionally performed to confirm the position of an internal jugular central venous catheter (CVC) after placement in the intensive care unit (ICU). Using chest radiography to confirm CVC position often results in delays in authorising the use of the CVC, requires the deployment of additional human resources, and is costly. ObjectiveThis study aimed to determine the feasibility and accuracy of using the central venous pressure (CVP) waveform to confirm the placement of internal jugular CVCs. MethodsThis retrospective study was conducted in a single quaternary ICU over a 6-month period. We included adult patients who had internal jugular CVC inserted and CVP transduced as part of their routine care in the ICU. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of CVP waveform analysis in confirming the position of internal jugular CVC relative to chest radiography were calculated. ResultsA total of 241 internal jugular CVCs were inserted (in 219 patients, 35.6% female), and the CVP waveform was assessed. In 231 cases, this suggested adequate placement in a central vein, which corresponded with a correct position on subsequent chest X-ray. On six occasions, the CVP waveforms were interpreted as suboptimal; however, on chest X-rays, the CVCs were noted to be in a suitable position (sensitivity: 97.5%). Four suboptimal CVP waveforms were obtained, and they correctly identified CVC malposition on subsequent chest X-ray (specificity: 100%). The average time from CVC insertion to radiological completion was 118 minutes. ConclusionCVP waveform analysis provides a feasible and reliable method for confirming adequate internal jugular CVC position. The use of chest radiography can be limited to cases where suboptimal CVP waveforms are obtained.

Estimation of central blood pressure waveform from femoral blood pressure waveform by blind sources separation.

Central blood pressure (cBP) is a better indicator of cardiovascular morbidity and mortality than peripheral BP (pBP). However, direct cBP measurement requires invasive techniques and indirect cBP measurement is based on rigid and empirical transfer functions applied to pBP. Thus, development of a personalized and well-validated method for non-invasive derivation of cBP from pBP is necessary to facilitate the clinical routine. The purpose of the present study was to develop a novel blind source separation tool to separate a single recording of pBP into their pressure waveforms composing its dynamics, to identify the compounds that lead to pressure waveform distortion at the periphery, and to estimate the cBP. The approach is patient-specific and extracts the underlying blind pressure waveforms in pBP without additional brachial cuff calibration or any a priori assumption on the arterial model. The intra-arterial femoral BPfe and intra-aortic pressure BPao were anonymized digital recordings from previous routine cardiac catheterizations of eight patients at the German Heart Centre Berlin. The underlying pressure waveforms in BPfe were extracted by the single-channel independent component analysis (SCICA). The accuracy of the SCICA model to estimate the whole cBP waveform was evaluated by the mean absolute error (MAE), the root mean square error (RMSE), the relative RMSE (RRMSE), and the intraclass correlation coefficient (ICC). The agreement between the intra-aortic and estimated parameters including systolic (SBP), diastolic (DBP), mean arterial pressure (MAP), and pulse pressure (PP) was evaluated by the regression and Bland-Altman analyses. The SCICA tool estimated the cBP waveform non-invasively from the intra-arterial BPfe with an MAE of 0.159 ± 1.629, an RMSE of 5.153 ± 0.957 mmHg, an RRMSE of 5.424 ± 1.304%, and an ICC of 0.94, as well as two waveforms contributing to morphological distortion at the femoral artery. The regression analysis showed a strong linear trend between the estimated and intra-aortic SBP, DBP, MAP, and PP with high coefficient of determination R2 of 0.98, 0.99, 0.99, and 0.97 respectively. The Bland-Altman plots demonstrated good agreement between estimated and intra-aortic parameters with a mean error and a standard deviation of difference of -0.54 ± 2.42 mmHg [95% confidence interval (CI): -5.28 to 4.20] for SBP, -1.97 ± 1.62 mmHg (95% CI: -5.14 to 1.20) for DBP, -1.49 ± 1.40 mmHg (95% CI: -4.25 to 1.26) for MAP, and 1.43 ± 2.79 mmHg (95% CI: -4.03 to 6.90) for PP. The SCICA approach is a powerful tool that identifies sources contributing to morphological distortion at peripheral arteries and estimates cBP.

Superiority of a Representative MRI Flow Waveform over Doppler Ultrasound for Aortic Wave Reflection Assessment in Children and Adolescents With/Without a History of Heart Disease

Wave separation analysis (WSA) reveals the impact of forward- and backward-running waves on the arterial pressure pulse, but the calculations require a flow waveform. This study investigated (1) the variability of the ascending aortic flow waveform in children and adolescents with/without a childhood heart disease history (CHD); (2) the accuracy of WSA obtained with a representative flow waveform (RepFlow), compared with the triangulation method and published ultrasound-derived adult representative flow; (3) the impact of limitations in Doppler ultrasound on WSA; and (4) generalizability of results to adults with a history of CHD. Phase contrast MRI was performed in youth without (n = 45, Group 1, 10–19 years) and with CHD (n = 79, Group 2, 7–18 years), and adults with CHD history (n = 29, Group 3, 19–59 years). Segmented aortic cross-sectional area was used as a surrogate for the central pressure waveform in WSA. A subject-specific virtual Doppler ultrasound was performed on MRI data by extracting velocities from a sample volume. Time/amplitude-normalized ascending aortic flow waveforms were highly consistent amongst all groups. WSA with RepFlow therefore yielded errors < 10% in all groups for reflected wave magnitude and return time. Absolute errors were typically 1.5–3 times greater with other methods, including subject-specific (best-case/virtual) Doppler ultrasound, for which velocity profile skewing introduced waveform errors. Our data suggest that RepFlow is the optimal approach for pressure-only WSA in children and adolescents with/without CHD, as well as adults with CHD history, and may even be more accurate than subject-specific Doppler ultrasound in the ascending aorta.

From peripheral finger-derived pulse waveforms to aortic pressure waveform features: an application of a generalized transfer function.

Aortic (central) pressure features are associated with cardiovascular complications and can be algorithmically derived from non-invasive peripheral arterial waveforms. This has conventionally been performed with a pressure waveform (i.e., tonometry or oscillometry) rather than with the optical-based sensor (photoplethysmography (PPG)) that is predominantly used in wearable health devices. Extraction of aortic features from a peripheral PPG waveform has yet to be investigated. This study aims to compare aortic features extracted from peripheral arterial waveforms acquired with different sensor modalities using the same transfer function. Radial tonometry (reference), finger volume-clamped PPG (Peňáz) and fingertip PPG waveforms were measured in participants (n=29, 36±16 years, 15 female) under baseline conditions. Waveforms were converted into an aortic pressure waveform using the transfer function. Waveform features were extracted from the converted waveform. Extracted features were compared with correlation plots and a Bland-Altman analysis. Aortic pressure features extracted from a finger using the Peňáz technique were comparable to radial tonometry derived features. Aortic features extracted from a fingertip waveform were more variable in comparison to radial tonometry-derived features. Aortic (central) pressure waveform features contain valuable haemodynamic information and have the capacity to be easily and conveniently implemented in wearable health devices. Future use of these features in wearable health devices incorporating PPG requires the development, and/or, optimization of a unique transfer function to more accurately represent the aortic pressure waveform for cardiovascular assessment.Clinical Relevance- Aortic pressure features might be used in wearable health devices following the development of a unique transfer function for optical-transduced peripheral vascular signals.

SEX DIFFERENCES IN PULSE WAVE AND SUBENDOCARDIAL VIABILITY RATIO IN HYPERTENSIVE PATIENTS

Objective: Myocardial ischemia represents the main cause of morbidity and mortality in hypertensive patients in both women and men, although some difference in pathophysiology and in the exposure to risk factors may explain the lower global cardiovascular risk of women. Sex differences in the structure and function of the cardiovascular system may affect the aortic pressure wave and the functional balance in myocardial perfusion determined by central aortic pressure, which may lead to myocardial ischemia and type 2 myocardial infarction. Design and method: In a population of 318 treated hypertensive patients (162 females, 156 males) referring to a single Hypertension centre (University of Trieste, Italy), we measured peripheral blood pressure and performed arterial applanation tonometry (PulsePen, DiaTecne, Italy) to determine central blood pressure waveform, aortic pulse wave velocity (PWV), augmentation index (AIx), pulse wave separation analysis with timing and amplitude of forward (fT, fP) and backward reflected (bT, bP) waves, and the subendocardial viability ratio (SEVR), i.e. the ratio between diastolic and systolic pressure-time index, as a risk marker of myocardial ischemia. Results: Despite similar peripheral systolic and diastolic blood pressure values, heart rate and PWV between sexes, females showed a longer forward pressure wave duration (119.8±38.6 vs 105.0±30.4 ms, p&lt;0.0001) and lower amplitude (52.7±19,4 vs 57.1±19.3 mmHg, p = 0.039) compared to males. Backward pressure waved were similar in timing and amplitude between males and females. The difference in forward wave caused a marked higher AIx (19.5±12.4 vs 11.9±14.9, p&lt;0.0001) and a lower SEVR (101.6±24.4 vs 108.5±27.7, p = 0.018) in women compared to men. Conclusions: Hypertensive women and men showed significant differences in forward pressure wave, causing a significant increase in AIx and a decreased SEVR in women compared to men. This may have important implications in sex-specific risk and myocardial ischemia.

Sex-specific associations of reservoir-excess pressure parameters with age and subclinical vascular remodeling.

Central artery reservoir pressure and excess pressure (XSP) are associated with cardiovascular disease (CVD) events and mortality. However, sex differences in the trajectory of central reservoir pressure and XSP with advancing age and their relations with vascular markers of subclinical CVD risk are incompletely understood. Therefore, we tested the hypothesis that central reservoir pressure and XSP would be positively associated with advancing age and vascular markers of subclinical CVD risk in men and women. Healthy adults ( n = 398; aged 18-80 years, 60% female individuals) had central (carotid) artery pressure waveforms acquired by applanation tonometry. Reservoir pressure and XSP peaks and integrals were derived retrospectively from carotid pressure waveforms using custom written software. Carotid artery intimal-medial thickness (IMT) was measured by ultrasonography, and aortic stiffness was determined from carotid-femoral pulse wave velocity (cfPWV). Reservoir pressure peak, reservoir pressure integral and XSP integral were higher with age in both men and women ( P < 0.05), whereas XSP peak was lower with age in men ( P < 0.05). In women, both reservoir pressure peak ( β = 0.231, P < 0.01) and reservoir pressure integral ( β = 0.254, P < 0.01) were associated with carotid artery IMT, and reservoir pressure peak was associated with cfPWV ( β = 0.120, P = 0.02) after adjusting for CVD risk factors. Central artery reservoir pressure and XSP were higher with advancing age in men and women, and reservoir pressure peak was associated with both carotid artery wall thickness and aortic stiffness in women but not men. Central reservoir pressure peak may provide some insight into sex differences in vascular remodeling and subclinical CVD risk with advancing age in healthy adults.

Central Venous Waveform Analysis and Cardiac Output in a Porcine Model of Endotoxemic Hypotension and Resuscitation.

Cardiac output (CO) is a valuable proxy for perfusion, and governs volume responsiveness during resuscitation from distributive shock. The underappreciated venous system has nuanced physiology that confers valuable hemodynamic information. In this investigation, deconvolution of the central venous waveform by the fast Fourier transformation (FFT) algorithm is performed to assess its ability to constitute a CO surrogate in a porcine model of endotoxemia-induced distributive hypotension and resuscitation. Ten pigs were anesthetized, catheterized, and intubated. A lipopolysaccharides infusion protocol was used to precipitate low systemic vascular resistance hypotension. Four crystalloid boluses (10 cc/kg) were then given in succession, after which heart rate, mean arterial pressure, thermodilution-derived CO, central venous pressure (CVP), and the central venous waveform were collected, the last undergoing fast Fourier transformation analysis. The amplitude of the fundamental frequency of the central venous waveform's cardiac wave (f0-CVP) was obtained. Heart rate, mean arterial pressure, CVP, f0-CVP, and CO were plotted over the course of the boluses to determine whether f0-CVP tracked with CO better than the vital signs, or than CVP itself. Distributive hypotension to a 25% mean arterial pressure decrement was achieved, with decreased systemic vascular resistance (mean 918 ± 227 [SD] dyne/s/cm-5 vs 685 ± 180 dyne/s/cm-5; p = 0.038). Full hemodynamic parameters characterizing this model were reported. Slopes of linear regression lines of heart rate, mean arterial pressure, CVP, f0-CVP, and CO were -2.8, 1.7, 1.8, 0.40, and 0.35, respectively, demonstrating that f0-CVP values closely track with CO over the 4-bolus range. Fast Fourier transformation analysis of the central venous waveform may allow real-time assessment of CO during resuscitation from distributive hypotension, possibly offering a venous-based approach to clinical estimation of volume responsiveness.

Central Venous Pressure Waveform Analysis Predicts Perfusion in a Porcine Model of Endotoxemic Hypotension and Resuscitation

Introduction: Cardiac output (CO), a proxy for perfusion, is a critical measure during sepsis and resuscitation. The understudied venous system has nuanced physiology that confers valuable information. In this investigation, deconvolution of the central venous pressure (CVP) waveform by the fast Fourier transformation (FFT) algorithm is performed to derive a CO surrogate in a “sepsis-like” porcine model of endotoxemic hypotension and resuscitation. Methods: Nine pigs were anesthetized, catheterized and intubated. A lipopolysaccharides infusion protocol was employed to precipitate low systemic vascular resistance (SVR) hypotension. Four subsequent crystalloid boluses (10 cc/kg) were given, after which thermodilution-CO, CVP and the CVP waveform were collected, the latter undergoing FFT analysis. The amplitude of the fundamental frequency of CVP waveform’s cardiac wave (“f0-CVP”) was obtained. Mean CVP, f0-CVP and CO were plotted over the course of the boluses to determine whether f0-CVP tracked with CO better than CVP itself. Results: Distributive hypotension to a 25% mean arterial pressure decrement was achieved, with decreased SVR (918±227 [standard deviation] dynes/seconds/cm-5 vs 685±180 dynes/seconds/cm-5; P = 0.038). Over four boluses, slopes of linear regression lines of CVP (r2=0.27; P < 0.001), f0-CVP (r2=0.15; P = 0.01) and CO (r2=0.08; P = 0.06) were 0.447, 0.148 and 0.178, respectively, demonstrating that f0-CVP constitutes a more accurate CO surrogate than CVP. Conclusion: FFT analysis of CVP waveforms offers a novel paradigm to interpret readily obtainable hemodynamic information. It offers potential for real-time utility in assessing volume responsiveness in sepsis and resuscitation, while further offering potential translation to the less-invasive peripheral venous pressure waveform.

Using generalized additive models to decompose time series and waveforms, and dissect heart–lung interaction physiology

Common physiological time series and waveforms are composed of repeating cardiac and respiratory cycles. Often, the cardiac effect is the primary interest, but for, e.g., fluid responsiveness prediction, the respiratory effect on arterial blood pressure also convey important information. In either case, it is relevant to disentangle the two effects. Generalized additive models (GAMs) allow estimating the effect of predictors as nonlinear, smooth functions. These smooth functions can represent the cardiac and respiratory cycles’ effects on a physiological signal. We demonstrate how GAMs allow a decomposition of physiological signals from mechanically ventilated subjects into separate effects of the cardiac and respiratory cycles. Two examples are presented. The first is a model of the respiratory variation in pulse pressure. The second demonstrates how a central venous pressure waveform can be decomposed into a cardiac effect, a respiratory effect and the interaction between the two cycles. Generalized additive models provide an intuitive and flexible approach to modelling the repeating, smooth, patterns common in medical monitoring data.

Increased Backward Wave Pressures Rather than Flow Explain Age-Dependent Heart Rate Effects on Central, But not Peripheral Arterial Pressure.

Through both backward (Pb) and forward (Pf) wave effects, a lower heart rate (HR) associates with increased central (PPc), beyond brachial pulse pressure (PP). However, the relative contribution to Pf of aortic flow (Q) versus re-reflection of Pb, has not been determined. Using central pressure, aortic velocity and diameter measurements in the outflow tract (echocardiography), we constructed central pressure waveforms that account for the relative contribution of Q versus re-reflection to Pf. We thus evaluated the mechanisms of HR-PPc relations in a community sample (n=824) and the impact of age thereon. Inverse HR-PPc (P<0.0001), but not HR-brachial PP (P=0.064) relations were noted. The slope of HR-PPc relation was increased in older adults (P<0.005). HR was inversely associated with ventricular filling time, ejection duration, stroke volume, and peak Pf (P<0.001 to P<0.0001). However, an increased Q and hence pressures generated by the product of aortic characteristic impedance and Q did not account for Pf effects. Age-dependent HR-PPc and Pf relations were both accounted for by enhanced Pb (P<0.0001) with an increased Pf mediated by increments in wave re-reflection (P<0.0001). The lack of impact of ejection duration on PPc was explained by an increased time to peak Pb (P<0.0001). In conclusion, increases in PPc and Pf at a decreased HR are accounted for by an enhanced Pb rather than by a prolonged ejection or filling duration and hence flow (Q). These effects at a young-to-middle age are of little clinical significance, but at an older age, are of clinical importance.

The use of point-of-care assessments and advanced hemodynamic monitoring in a patient with Eisenmenger syndrome for cesarean section: A case report.

IntroductionEisenmenger syndrome should be diagnosed before pregnancy and surgically corrected if possible. Cesarean section in a patient with Eisenmenger syndrome is high risk as morbidity and mortality are very high. Delivery in hospitals with intensive care units should be recommended. Use of point-of-care assessments and advanced monitors allow accurate management.Case presentationA primigravid with congestive heart failure from a patent ductus arteriosus in Eisenmenger syndrome, presented with threatened preterm labor and uncontrolled hypertension prompting cesarean delivery under epidural anesthesia. Pre-induction focused echocardiography revealed normal right ventricular function and severe pulmonary hypertension. Intraoperatively, hemodynamics became unstable. The decision to use fluids, vasopressor and inotrope was guided by analyses of arterial pulse contour, central venous pressure waveform and blood exams. Hemodynamics improved and a live baby was delivered. Postoperative course was unremarkable.DiscussionThe cause of hemodynamic instability must be accurately determined as inappropriate use of fluid or medication may be detrimental to a patient with Eisenmenger syndrome. In this case, advanced hemodynamic monitoring showed changes in central venous pressure, cardiac output and systemic vascular resistance which differentiated the causes of hypotension and desaturation. Point-of-care blood analysis showed acidosis and hypoxia which may have worsened the right-to-left shunt, contributing to the desaturation. Fluid and drug infusions to address identified problems were then guided by advanced monitors.ConclusionThe use of point-of-care assessments and advanced hemodynamic monitoring allowed accurate diagnoses and goal-directed therapies leading to improved patient safety and outcomes. The need for prolonged intensive care in this case was prevented.