Arterial Waveform Research Articles

Longitudinal changes in blood flow, vessel pulsatility, and trans‐capillary pulse wave delay in small vessel disease and amyloid biomarker positive

AbstractBackgroundThis work characterized longitudinal changes in cerebrovascular health in humans with and without presence of small vessel disease (SVD) and amyloid‐β using different neuroimaging techniques.MethodParticipants (n = 41) were biomarker characterized into three groups: amyloid and SVD negative (A‐V‐; “control”), SVD positive (A‐V+), and amyloid positive (A+V‐). All were cognitively unimpaired except two participants clinically diagnosed with MCI (A‐V+) and dementia (A+V‐) (Table 1). MRI: Vascular imaging: Whole‐brain 4D‐Flow data were acquired to characterize cerebrovascular health. Two MRI time‐points were used to establish 4D‐Flow‐based longitudinal changes. Measurements were extracted from intracranial arteries and veins and included blood flow, vessel pulsatility index, and trans‐capillary pulse wave delay (TPWD). TPWD was defined as the time shift between arterial and venous cardiac waveforms. Structural imaging: TIV‐normalized WMH volumes were segmented from T2‐FLAIR images and used as surrogate for SVD. V+/‐ thresholds were determined from Gaussian Mixture Model on 930 participants. PET: Amyloid‐β burden was assessed using 11C‐PiB PET imaging. Amyloid positivity was established as mean cortical PiB DVR >1.16. Vascular changes were assessed using Kruskal‐Wallis followed by multiple comparisons (P<0.05 significance).ResultAfter ∼4.5 years, middle cerebral arteries (MCAs) blood flow decreased significantly more in amyloid than SVD groups, while vessel pulsatility increased significantly more in amyloid compared to SVD (Fig.1). TPWD shortened significantly in SVD compared to amyloid and control groups. All groups showed a decreased in blood flow, increased in vessel pulsatility and shortening of TPWD with time. At the time of the last MRI visit, MCAs blood flow was somewhat higher in SVD compared to amyloid and control groups, but not significantly (Fig.2). Vessel pulsatility was similar between groups, yet slightly higher in SVD and amyloid groups. TPWD was significantly shorter in SVD compared to the amyloid group.ConclusionThis work demonstrates the feasibility to measure longitudinal cerebrovascular changes using 4D‐Flow MRI in the context of SVD and AD biomarker positive groups. Preliminary results indicate 4D‐Flow‐based vascular markers can detect differences between SVD and amyloid groups both longitudinally and cross‐sectionally. Cerebrovascular changes in A+ suggest decreased metabolism and increased macro‐vessel stiffness, while findings in SVD positive supports stiffness of the micro‐circulation and capillaries.

Improved U-Net Model to Estimate Cardiac Output Based on Photoplethysmography and Arterial Pressure Waveform.

We aimed to estimate cardiac output (CO) from photoplethysmography (PPG) and the arterial pressure waveform (ART) using a deep learning approach, which is minimally invasive, does not require patient demographic information, and is operator-independent, eliminating the need to artificially extract a feature of the waveform by implementing a traditional formula. We aimed to present an alternative to measuring cardiac output with greater accuracy for a wider range of patients. Using a publicly available dataset, we selected 543 eligible patients and divided them into test and training sets after preprocessing. The data consisted of PPG and ART waveforms containing 2048 points with the corresponding CO. We achieved an improvement based on the U-Net modeling framework and built a two-channel deep learning model to automatically extract the waveform features to estimate the CO in the dataset as the reference, acquired using the EV1000, a commercially available instrument. The model demonstrated strong consistency with the reference values on the test dataset. The mean CO was 5.01 ± 1.60 L/min and 4.98 ± 1.59 L/min for the reference value and the predicted value, respectively. The average bias was -0.04 L/min with a -1.025 and 0.944 L/min 95% limit of agreement (LOA). The bias was 0.79% with a 95% LOA between -20.4% and 18.8% when calculating the percentage of the difference from the reference. The normalized root-mean-squared error (RMSNE) was 10.0%. The Pearson correlation coefficient (r) was 0.951. The percentage error (PE) was 19.5%, being below 30%. These results surpassed the performance of traditional formula-based calculation methods, meeting clinical acceptability standards. We propose a dual-channel, improved U-Net deep learning model for estimating cardiac output, demonstrating excellent and consistent results. This method offers a superior reference method for assessing cardiac output in cases where it is unnecessary to employ specialized cardiac output measurement devices or when patients are not suitable for pulmonary-artery-catheter-based measurements, providing a viable alternative solution.

Low cardiac index and high total peripheral resistance are independent predictors of mortality for acutely admitted medical patients

Abstract Introduction Identification of critically ill and high-risk patients upon hospital admission represents a significant triage task in the emergency department. Rapid identification and treatment of these patients improves survival, reduces complications during admission, decrease hospitalization duration, and lower treatment cost. Hemodynamic variables such as cardiac index (CI) and total peripheral resistance (TPR) hold the potential to improve the diagnosis of critically ill patients by providing better prognostic value. Using arterial waveform and the model flow method, CI and TPR can be measured non-invasively. These methods are validated against thermodilution, which is the golden standard. Purpose The purpose of the study was to examine the association between CI, TPR, and all-cause mortality within 30 and 90-day among patients admitted to acutely the emergency department or cardiac care unit. Methods In this observational prospective cohort study, all patients were included from the emergency department and cardiac care unit. Patients of age 18 years or older admitted to the emergency department or cardiac care unit due to acute medical disease, were included. Patients with a life expectance of 3months or less and patients unable to give informed consent were excluded. Within the first 24 hours of admission, patients underwent a 10-minute non-invasive hemodynamic measurement using arterial waveform analysis and the model-flow method. Patients were followed up to 90 days after discharge. The endpoints 30- and 90-day mortality were obtained through electronic patient journals. Cox proportional-hazard models examined the association between CI, TPR, and outcomes. All hazard models were adjusted for age, sex, BMI, heart failure, early warning score, and Charlson comorbidity score. Results Between May 2019 and January 2023, we included 920 unselected patients from the department of emergency medicine (n=433) and cardiac care unit (n=487) from a metropolitan hospital. We observed a 3% 30-days mortality while 90-days mortality was 4.9%. The median value of CI was 3.3L, the lowest decile was 2L, and the highest decile was 4.7L. Patients with the lowest CI were older, had higher incidents of heart failure, and had more comorbidities according to Charlson’s comorbidity score (table 1). In Cox proportional hazard models, lowest decile of CI at admission was associated with a significantly higher risk of 30- and 90-days mortality. Total peripheral resistance was significantly associated with 90-day mortality (table 2). Conclusion Low CI ≤2.0 liter/min/m2, and high peripheral resistance measured non-invasively were associated with an increased risk of mortality among patients admitted to the emergency department or cardiac care unit. Non-invasive estimation of CI and TPR may assist in identifying high-risk patients.Table 1Table 2

Abstract 18495: Right and Left Ventricular Starling Function in Heart Failure With Preserved Ejection Fraction

Background: Efficient conversion of cardiac filling pressure to stroke volume (SV) is described by the Frank-Starling mechanism. Our group has previously characterized dynamic Starling mechanisms (DSM) in the frequency domain using respiratory mediated changes in beat-by-beat SV as a function of changes in pulmonary artery diastolic pressures (PAD) and shown this to be impaired in heart failure with preserved ejection fraction (HFpEF). Right ventricular (RV) dysfunction is common in HFpEF but efficiency of the RV Starling mechanism is unknown. The goal of this study was to characterize RV and LV DSM in patients with HFpEF. Methods: We studied 16 HFpEF patients (10 females; age: 68 ± 6 years) at seated rest following the placement of a radial arterial and right heart catheter. DSM was quantified by transfer function (input/output frequency) analyses of beat-by-beat changes in PAD (for LV) and A wave right atrial pressures (for RV) as input variables. Output variable was SV (Modelflow from radial arterial line waveform) and normalized for LV DSM using direct-Fick derived SV and for RV DSM using acetylene rebreathing derived SV as a measure of pulmonary blood flow. Results: The power spectral density (PSD) was higher for LV compared to RV filling pressure (521 ± 446 vs 125 ± 111 mmHg 2 , P = 0.001); there was no difference for adjusted SV PSD (819 ± 764 vs 808 ± 765 ml 2 , P = 0.232). DSM gain was higher for LV compared to RV for all but 1 subject (0.58 ± 0.29 vs 0.30 ± 0.16 ml/mmHg, P = 0.002; Figure). Conclusions: RV DSM gain was significantly worse than LV DSM gain suggesting decreased RV contractile and/or compliance reserve in patients with HFpEF. These results highlight the utility of RV DSM gain to characterize RV dysfunction in patients with HFpEF.

Abstract 15854: Pulmonary Arterial Waveform Analysis and Supervised Machine Learning Predicts Outcomes in Heart Failure: An Analysis of the CHAMPION Trial

Introduction: Pulmonary artery (PA) pressure monitors have been shown to prevent hospitalizations in heart failure patients using mean pressure values. PA waveform analysis offers an overall assessment of right ventricular load that combines resistance and compliance. Hypothesis: We hypothesised that clinical outcomes can be predicted from PA waveform analyses from patients with an implanted PA monitor. Methods: The CHAMPION trial database was analysed. Information about the demographics and data collection methods for this cohort have been published previously. All waveforms were analysed automatically using Python. Tau or RC time, respiratory rate, respiratory range, P asymptote, mean pulmonary arterial pressure (mPAP), diastolic pulmonary arterial pressure (dPAP) and systolic pulmonary arterial pressure (sPAP) were derived from the raw waveforms. Tau and P asymptote were calculated using phase plane analysis. Outcomes of hospitalization or mortality were adjudicated as part of the original trial. Statistics were performed in R. A decision tree statistical approach using the XGBoost algorithm was used. Contribution to the model was assessed using Shapley Additive Explanations (SHAP) values. The dataset was split into 70% train and 30% test sets. Results: 296,948 PA pressure traces from 547 patients were analysed. 3 patients had insufficient quality of trace to analyse. There were 1340 outcome events throughout the follow up period, median 664 days. The trained algorithm was able to predict clinical outcomes in the test dataset with 100% accuracy. The most significant contributors according the mean SHAP value were RC time (SHAP = 1.1), respiratory range (SHAP = 1.1) and heart rate (SHAP = 1.1). Conclusions: Detailed analysis of the PA waveform from PA pressure monitors is feasible and yields clinically relevant information. A supervised machine learning algorithm trained on this data can accurately predict hospitalization or mortality events in heart failure patients.

Abstract 16270: Pulse Pressure Amplification Contributes to the Exaggerated Peripheral Blood Pressure Response to Exercise in Patients With HFpEF

Background: Patients with suspected heart failure and preserved ejection fraction (HFpEF) undergo invasive (right heart catheterization) cardiopulmonary exercise testing (iCPET) to confirm diagnosis. During iCPET peripheral arterial blood pressure (BP) is measured using a brachial cuff or radial arterial catheter. Notably, peripheral BP is typically higher than central BP and this difference is assessed by measuring pulse pressure amplification (PPA; peripheral - central PP; PP = systolic BP [SBP] - diastolic BP). The effect of PPA on the assessment of (exaggerated) exercise BP in HFpEF is unknown. Methods: 20 patients with HFpEF (75% Female; age: 69±7 years, BMI: 38±7 kg/m 2 ) were studied at rest and during submaximal and maximal cycling exercise. We measured radial BP (arterial catheterization) and derived ascending aortic BP using a transfer function applied to radial arterial waveforms (SphygmoCor). Pulse pressure amplification was assessed as radial-aortic pulse pressure. Data were compared with one-way and two-way (Site*Exercise Stage) ANOVA. Results: The increase in radial PP with exercise was greater than aortic PP (Site*Stage P<0.001; Figure A); thus, PPA increased with exercise (ANOVA, P =0.001; Figure B). The increase in PPA was due to a greater increase in radial compared to aortic SBP (Site*Stage P=0.002; Figure C). The change in diastolic pressure was not different between sites (Figure D). Increases in PPA accounted for ~30% of the increase in radial SBP. The incidence of exaggerated exercise SBP (Male: >210 mmHg; Female: >190 mmHg) was higher in the radial artery compared to aorta (95 vs 40 %, χ2<0.001). Conclusion: Peak exercise SBP was ~50 mmHg greater in the radial artery compared to aorta. The marked overestimation of central BP during exercise when measuring peripheral BP leads to misguided interpretation of BP responses to exercise, the magnitude of afterload and has implications for the assessment of ventricular-arterial coupling.

A generalized canine transfer function accurately reconstructs central aortic pressure waveforms to enable enhanced pulse wave analysis

Routine preclinical blood pressure evaluation is an important risk assessment tool. Although proximal aortic pressure is most relevant for key target organs, abdominal aortic pressures are more commonly recorded. Pulse pressure amplification and waveform distortion in abdominal waveforms make it inappropriate for central hemodynamic analytical methods without the use of a mathematical transfer function. Clinical transfer functions have been developed to estimate ascending aortic waveforms from brachial or radial artery waveforms in humans, but no preclinical analogues exist. The aim of this study was to develop a canine-specific transfer function to reconstruct thoracic aortic pressure waveforms from abdominal aortic data to enable the application of central hemodynamic analytical methods. Simultaneous abdominal and thoracic blood pressures were recorded from seven conscious, male beagle dogs administered 3 well-characterized pharmacologic standards and animals were appointed to a training (n = 3) or validation (n = 4) group at baseline and during dosing. A generalized transfer function was developed from the training group data and evaluated for its ability to synthesize thoracic pressure waves in the training and validation groups. Select hemodynamic parameters were evaluated in measured and synthesized thoracic data. There was a high degree of correlation between measured and synthesized thoracic parameters (r2 = 0.74–0.99). There was no difference between indices computed from synthesized or actual thoracic waveforms at baseline or after administration of pharmacologic standards. This work demonstrates that a generalized preclinical transfer function can reproduce thoracic pressure waves across a range of hemodynamic responses thus enabling the application of central hemodynamic analytical methods.

Vascular Age Assessed From an Uncalibrated, Noninvasive Pressure Waveform by Using a Deep Learning Approach: The AI-VascularAge Model.

Aortic stiffness, assessed as carotid-femoral pulse wave velocity, provides a measure of vascular age and risk for adverse cardiovascular disease outcomes, but it is difficult to measure. The shape of arterial pressure waveforms conveys information regarding aortic stiffness; however, the best methods to extract and interpret waveform features remain controversial. We trained a convolutional neural network with fixed-scale (time and amplitude) brachial, radial, and carotid tonometry waveforms as input and negative inverse carotid-femoral pulse wave velocity as label. Models were trained with data from 2 community-based Icelandic samples (N=10 452 participants with 31 126 waveforms) and validated in the community-based Framingham Heart Study (N=7208 participants, 21 624 waveforms). Linear regression rescaled predicted negative inverse carotid-femoral pulse wave velocity to equivalent artificial intelligence vascular age (AI-VA). The AI-VascularAge model predicted negative inverse carotid-femoral pulse wave velocity with R2=0.64 in a randomly reserved Icelandic test group (n=5061, 16%) and R2=0.60 in the Framingham Heart Study. In the Framingham Heart Study (up to 18 years of follow-up; 479 cardiovascular disease, 200 coronary heart disease, and 213 heart failure events), brachial AI-VA was associated with incident cardiovascular disease adjusted for age and sex (model 1; hazard ratio, 1.79 [95% CI, 1.50-2.40] per SD; P<0.0001) or adjusted for age, sex, systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, prevalent diabetes, hypertension treatment, and current smoking (model 2; hazard ratio, 1.50 [95% CI, 1.24-1.82] per SD; P<0.0001). Similar hazard ratios were demonstrated for incident coronary heart disease and heart failure events and for AI-VA values estimated from carotid or radial waveforms. Our results demonstrate that convolutional neural network-derived AI-VA is a powerful indicator of vascular health and cardiovascular disease risk in a broad community-based sample.

A machine learning approach for computation of cardiovascular intrinsic frequencies.

Analysis of cardiovascular waveforms provides valuable clinical information about the state of health and disease. The intrinsic frequency (IF) method is a recently introduced framework that uses a single arterial pressure waveform to extract physiologically relevant information about the cardiovascular system. The clinical usefulness and physiological accuracy of the IF method have been well-established via several preclinical and clinical studies. However, the computational complexity of the current L2 optimization solver for IF calculations remains a bottleneck for practical deployment of the IF method in real-time settings. In this paper, we propose a machine learning (ML)-based methodology for determination of IF parameters from a single carotid waveform. We use a sequentially-reduced Feedforward Neural Network (FNN) model for mapping carotid waveforms to the output parameters of the IF method, thereby avoiding the non-convex L2 minimization problem arising from the conventional IF approach. Our methodology also includes procedures for data pre-processing, model training, and model evaluation. In our model development, we used both clinical and synthetic waveforms. Our clinical database is composed of carotid waveforms from two different sources: the Huntington Medical Research Institutes (HMRI) iPhone Heart Study and the Framingham Heart Study (FHS). In the HMRI and FHS clinical studies, various device platforms such as piezoelectric tonometry, optical tonometry (Vivio), and an iPhone camera were used to measure arterial waveforms. Our blind clinical test shows very strong correlations between IF parameters computed from the FNN-based method and those computed from the standard L2 optimization-based method (i.e., R≥0.93 and P-value ≤0.005 for each IF parameter). Our results also demonstrate that the performance of the FNN-based IF model introduced in this work is independent of measurement apparatus and of device sampling rate.

Predicting pulse wave age from cardiovascular characteristics using machine learning algorithms

Arterial pulse waves are an essential and informative source of data for measuring cardiovascular health and are currently available on various devices, such as smartwatches. In addition, most experiments have focused on pulse wave velocity (PWV) and arterial pulse waveform (APW). However, in recent years, due to many factors, such as high work pressure among young people, cardiovascular diseases have become more prevalent. As a result, we need more research on pulse age to help patients focus on the consistency of their biological age with their legal age. Therefore, this paper introduces a machine learning framework to predict the age of patients according to the HR and SV data in the pulse wave of existing patients. At the beginning of the experiment, we attempted to use K-nearest neighbours (KNN), logistic regression (LR), and random forest (RF) models. Then we compared the accuracy of patient age estimates; RF had the best performance and was used as the final model. In addition, HR and SV were selected as the main features to predict patients' age according to the feature's importance. The final experimental results indicate that the RF model can predict the results with up to 1 accuracy only with two features, i.e., HR and SV. In particular, this is the point that we need to continue to pay attention to in the future to determine whether the prediction accuracy of this model can reach 1. Is it because the model is overly optimistic or because the selected HR and SV features strongly correlate with age? In the future, we can introduce the concept of actual body age and attract young people's attention to their body age. For example, more people will be urged to take a quick and efficient pulse diagnosis, complete a simple physical examination, and give rapid feedback by placing devices in places where young people gather, such as shopping malls. In this case, users can immediately know what part of their body may have problems. In general, our experiment demonstrates that patients' pulse wave ages could be well predicted based on their HR and SV data with high accuracy through the ML model of RF.

Central-to-peripheral blood pressure amplification: role of the recording site, technology, analysis approach, and calibration scheme in invasive and non-invasive data agreement.

Systolic blood pressure amplification (SBPA) and pulse pressure amplification (PPA) can independently predict cardiovascular damage and mortality. A wide range of methods are used for the non-invasive estimation of SBPA and PPA. The most accurate non-invasive method for obtaining SBPA and/or PPA remains unknown. This study aims to evaluate the agreement between the SBPA and PPA values that are invasively and non-invasively obtained using different (1) measurement sites (radial, brachial, carotid), (2) measuring techniques (tonometry, oscillometry/plethysmography, ultrasound), (3) pulse waveform analysis approaches, and (4) calibration methods [systo-diastolic vs. approaches using brachial diastolic and mean blood pressure (BP)], with the latter calculated using different equations or measured by oscillometry. Invasive aortic and brachial pressure (catheterism) and non-invasive aortic and peripheral (brachial, radial) BP were simultaneously obtained from 34 subjects using different methodologies, analysis methods, measuring sites, and calibration methods. SBPA and PPA were quantified. Concordance correlation and the Bland-Altman analysis were performed. (1) In general, SBPA and PPA levels obtained with non-invasive approaches were not associated with those recorded invasively. (2) The different non-invasive approaches led to (extremely) dissimilar results. In general, non-invasive measurements underestimated SBPA and PPA; the higher the invasive SBPA (or PPA), the greater the underestimation. (3) None of the calibration schemes, which considered non-invasive brachial BP to estimate SBPA or PPA, were better than the others. (4) SBPA and PPA levels obtained from radial artery waveform analysis (tonometry) (5) and common carotid artery ultrasound recordings and brachial artery waveform analysis, respectively, minimized the mean errors. Overall, the findings showed that (i) SBPA and PPA indices are not "synonymous" and (ii) non-invasive approaches would fail to accurately determine invasive SBPA or PPA levels, regardless of the recording site, analysis, and calibration methods. Non-invasive measurements generally underestimated SBPA and PPA, and the higher the invasive SBPA or PPA, the higher the underestimation. There was not a calibration scheme better than the others. Consequently, our study emphasizes the strong need to be critical of measurement techniques, to have methodological transparency, and to have expert consensus for non-invasive assessment of SBPA and PPA.

Reconstruction of central arterial pressure waveform based on CBi-SAN network from radial pressure waveform

The central arterial pressure (CAP) is an important physiological indicator of the human cardiovascular system which represents one of the greatest threats to human health. Accurate non-invasive detection and reconstruction of CAP waveforms are crucial for the reliable treatment of cardiovascular system diseases. However, the traditional methods are reconstructed with relatively low accuracy, and some deep learning neural network models also have difficulty in extracting features, as a result, these methods have potential for further advancement. In this study, we proposed a novel model (CBi-SAN) to implement an end-to-end relationship from radial artery pressure (RAP) waveform to CAP waveform, which consisted of the convolutional neural network (CNN), the bidirectional long-short-time memory network (BiLSTM), and the self-attention mechanism to improve the performance of CAP reconstruction. The data on invasive measurements of CAP and RAP waveform were used in 62 patients before and after medication to develop and validate the performance of CBi-SAN model for reconstructing CAP waveform. We compared it with traditional methods and deep learning models in mean absolute error (MAE), root mean square error (RMSE), and Spearman correlation coefficient (SCC). Study results indicated the CBi-SAN model performed great performance on CAP waveform reconstruction (MAE: 2.23 ± 0.11 mmHg, RMSE: 2.21 ± 0.07 mmHg), concurrently, the best reconstruction effect was obtained in the central artery systolic pressure (CASP) and the central artery diastolic pressure(CADP) (RMSECASP: 2.94 ± 0.48 mmHg, RMSECADP: 1.96 ± 0.06 mmHg). These results implied the performance of the CAP reconstruction based on CBi-SAN model was superior to the existing methods, hopped to be effectively applied to clinical practice in the future.

THU657 Racing Hearts: A Closer Look At The Connection Between Hyperthyroidism And Coronary Vasospasm

Abstract Disclosure: A. Elahi: None. R.I. Mateo: None. The cardiovascular system is a major target of thyroid hormone action in which subtle changes in thyroid function can lead to cardiac complications such as coronary vasospasm. There are many proposed mechanisms in how this occurs, but further studies are needed to understand the specific pathophysiology. This case illustrates the clinical relevance of encountering a hyperthyroid patient complaining of chest pain and how to address it in real work practice. A 62 year old African American male with history of Graves hyperthyroidism, type 2 diabetes, hypertension, peripheral artery disease, and stroke with residual aphasia presented with acute onset chest pain, shortness of breath, and palpitations. He was noncompliant with his methimazole (MMI) and subsequent labs were significant for free T4 of 2.5 ng/dL, undetectable TSH, and free T3 of 11.53 pg/mL. His high sensitivity troponins were elevated at 29,569 ng/dL. EKG showed sinus tachycardia with T-wave inversions in leads II, III, and AVF with a bedside echo showing inferior lateral wall motion abnormality. He was started on MMI 20mg daily and admitted to the intensive care unit for non-ST elevation myocardial infarction management. Left heart catheterization (LHC) was done after 1 dose of MMI with findings suggestive of vasospasm; specifically, decreased blood flow of the right coronary artery which improved when the catheter was withdrawn and significant dampening of the arterial wave form of the left main coronary artery. Given the unclear differentiation between true coronary artery disease and vasospasm he needed a repeat LHC once his hyperthyroidism was more controlled. His free T3 levels were monitored every 1-2 days with administration of escalating doses of MMI up to 40mg daily. By day 7 of admission, his free T3 level was 4.54 pg/mL, free T4 was 2 ng/dL and repeat LHC was performed showing open vessel lumens with multiple occlusions requiring 2 stents in the right coronary artery and 1 stent in the left circumflex artery. Given that thyroid hormone receptors are present in the myocardium and vascular tissue, alterations in thyroid hormone concentration can affect cardiovascular function. The documentation of coronary spasm, however, is rare and to date, there are only a few limited cases describing the underlying pathophysiology. This case illustrates via cardiac catheterization images and corresponding labs, the positive correlation of coronary vasospasm with thyroid hormone levels in hyperthyroidism. Presentation: Thursday, June 15, 2023

Percentage of mean arterial pressure as a marker of atherosclerosis for detecting patients with coronary artery disease

The percentage of mean arterial pressure (%MAP) is the height of the mean arterial waveform divided by the peak amplitude of the waveform of pulse volume recording. The purpose of this study was to determine whether the cutoff value of 45% for %MAP at the ankle, which is recommended for the diagnosis of lower extremity artery disease, in combination with ankle-brachial index (ABI) is useful for detecting patients with clinical coronary artery disease (CAD) and investigate the optimal cutoff value of %MAP to diagnose patients with CAD. We measured ABI and %MAP in 2213 subjects (mean age: 61.2 ± 15.5 years). Multivariate analysis revealed that %MAP ≥ 45% was significantly associated with a higher risk of CAD after adjusting for traditional cardiovascular risk factors (odds ratio [OR], 2.14; 95% confidence interval [CI], 1.43–3.21; p < 0.001). However, the association was no longer significant after adjusting for ABI (OR, 1.39; 95% CI, 0.83–2.33; p = 0.21), whereas ABI was significantly associated with CAD (OR, 0.98; 95% CI, 0.97–0.99; p = 0.005). The optimal cutoff value of %MAP derived from a receiver operating characteristic curve to diagnose CAD was 40.3%. Multivariate analysis revealed that %MAP ≥ 40.3% was significantly associated with a higher risk of CAD (OR, 1.63; 95% CI, 1.19–2.24; p = 0.002) independent of ABI (OR, 0.98; 95% CI, 0.97–0.99; p = 0.002). The cutoff value of 40.3%, but not 45%, for %MAP may be useful for detecting patients with advanced atherosclerosis and for cardiovascular risk assessment independent of ABI.Registration Informationhttp://www.umin.ac.jp (University Hospital Medical Information Network Clinical Trials Registry) (UMIN000039512)

Pulse wave analysis as a tool for the evaluation of resuscitation therapy in septic shock

Objective. Pulse wave analysis (PWA) can provide insights into cardiovascular biomechanical properties. The use of PWA in critically ill patients, such as septic shock patients, is still limited, but it can provide complementary information on the cardiovascular effects of treatment when compared to standard indices outlined in international guidelines. Previous works have highlighted how sepsis induces severe cardiovascular derangement with altered arterial blood pressure waveform morphology and how resuscitation according to standard haemodynamic targets is not able to restore the physiological functioning of the cardiovascular system. The aim of this work is to test the effectiveness of PWA in characterizing arterial waveforms obtained from a swine experiment involving polymicrobial septic shock and resuscitation with different drugs. Methods. During the experiment, morphological aortic waveform features, such as indices related to the dicrotic notch and inflection point, were extracted by means of PWA techniques. Finally, all the PWA indices were used to compute a clustering classification (mini batch K-means) of the pigs according to the different phases of the experiment. This analysis aimed to test if PWA features alone could be used to distinguish between the different responses to the administered therapies. Results. The PWA indices highlighted different cardiovascular conditions of the pigs in response to different treatments, despite the mean haemodynamic values typically used to guide therapy administration being similar in all animals. The clustering algorithm was able to distinguish between the different phases of the experiment and the different responses of the animals based on the unique information derived from the aortic PWA. Conclusion. Even when used alone, PWA indices were highly informative when assessing therapy responses in cases of septic shock. Significance. A complex pathological condition like septic shock requires extensive monitoring without neglecting important information from commonly measured signals such as arterial blood pressure. Future studies are needed to understand how individual differences in the response to therapy are associated with different cardiovascular conditions that may become specific therapy targets.

Use of a rat model to characterize 35 arterial pulse wave parameters in a comparative study of isoflurane and Zoletil/xylazine anesthesia and the effect of Acanthopanax senticosus extract.

Information obtained from arterial pulse waveforms (APW) can be useful for characterizing the cardiovascular system. To achieve this, it is necessary to know the detailed characteristics of APWs in different states of an organism, which would allow APW parameters (APW-Ps) to be assigned to particular (patho)physiological conditions. Therefore, our work aimed to characterize 35 APW-Ps in rats under the influence of isoflurane (ISO) and Zoletil/xylazine (ZO/XY) anesthesia and to study the effect of root extract from Acanthopanax senticosus (ASRE) in these anesthetic conditions. The right jugular vein of anesthetized rats was cannulated for the administration of ASRE and the left carotid artery for the detection of APWs from which 35 APW-Ps were evaluated. We obtained data on 35 APW-Ps, which significantly depended on the anesthesia, and thus, they characterized the cardiovascular system under these two conditions. ASRE transiently modulated all 35 APW-Ps, including a transient decrease in systolic and diastolic blood pressure (BP) and heart rate or increases in pulse BP, dP/dtmax , and systolic and diastolic areas. Whereas the transient effects of ASRE were similar, the extract had prolonged disturbing effects on the cardiovascular system in rats under ZO/XY but not under ISO anesthesia. This negative effect can result from the disturbance caused by ZO/XY anesthesia on the cardiovascular system. We characterized 35 APW-Ps of rats under ISO and ZO/XY anesthesia and found that ASRE contains compounds that can modulate the properties of the cardiovascular system, which significantly depended on the status of the cardiovascular system. This should be considered when using ASRE as a nutritional supplement by individuals with cardiovascular problems.

A 2-DOF Model of the Artery-Sensor System for Interpreting Variability in Measured Arterial Pulse Waveform

A 2-DOF Model of the Artery-Sensor System for Interpreting Variability in Measured Arterial Pulse Waveform

Interpretable and accurate curve-fitting method for arterial pulse wave modeling and decomposition.

Arterial pulse waveforms contain a wealth of information about the cardiovascular system. There is a lack of physical meaning in the mathematical model of arterial pulse waves, while the physical model fails to offer individuality as too many assumptions are involved. In this article, we focus on promoting the interpretability of the arterial pulse mathematical model. The proposed method is based on newly developed 3-term fitting functions individualized by the physiological parameter assignment, which are the peak times of the reflected and dicrotic waves in a pulse. In this manner, the model allows decomposition of the pulse into sub-signals with clear physiological significance. With nearly 10,000 pulse fitting experiments, it is demonstrated that the proposed method outperforms the standard methods in fitting accuracy while providing parameters linked to hemodynamic characteristics and common clinical indices such as the peripheral augmentation index (pAI). The proposed method innovatively maintains the individuality and accuracy of mathematical models while improving the interpretability of their parameters. The applications of this newly developed method, which explicitly incorporates hemodynamic characteristics, are expected to be particularly valuable in future pulse wave decomposition studies.

The impact of underdamping on the maximum rate of the radial pressure rise during systole (dP/dtMAX)

PurposeIn patients with a radial arterial catheter, underdamping of the pressure signal is common and responsible for an overestimation of systolic arterial pressure (SAP). The maximum rate of the arterial pressure rise during systole (dP/dtMAX) has been proposed to assess left ventricular systolic function. The impact of underdamping on dP/dtMAX is likely but has never been quantified.MethodsWe analyzed data from 70 critically ill patients monitored with a radial catheter in whom underdamping of the arterial pressure waveform was confirmed by the Gardner’s method. Invasive SAP and dP/dtMAX were recorded at baseline and after the correction of underdamping with a resonance filter.ResultsWith resonance filtering, SAP decreased from 159 ± 25 to 139 ± 22 mmHg (p < 0.001) and dP/dtMAX from 2.0 ± 0.6 to 1.1 ± 0.3 mmHg/ms (p < 0.001). The underdamping-induced overestimation of SAP (delta-SAP) ranged from 6 to 41 mmHg (mean 21 ± 9 mmHg or + 15%) and the overestimation of dP/dtMAX (delta-dP/dtMAX) ranged from 0.2 to 2.0 mmHg/ms (mean 0.9 ± 0.4 mmHg/ms or + 84%). A significant linear relationship (p < 0.001, r = 0.6) was observed between delta-SAP and delta-dP/dtMAX such that the higher was delta-SAP, the higher was delta-dP/dtMAX.ConclusionsRadial arterial pressure underdamping has a major impact on dP/dtMAX. In case of underdamping, the overestimation of dP/dtMAX is > fivefold higher than SAP overestimation. Therefore, caution should be exercised before using radial dP/dtMAX as a marker of left ventricular systolic function.Trial registrationRegistered at ClinicalTrials.gov on December 22, 2021 (NCT05166993).Graphical

Effects of anthracycline chemotherapy and Kuan-Sin-Yin on the spectral indices of arterial pulse waveforms in breast cancer patients.

The vascular structure and function are potentially useful biomarkers for tumor detection. Treatment with chemotherapeutic agents may impair vascular function and increase the risk of cardiovascular disease. This study aimed to use noninvasive pulse waveform measurements to identify differences in the frequency-domain indices of the pulse waveform in breast-cancer patients following anthracycline chemotherapy between with (Group KSY) and without (Group NKSY) receiving Kuan-Sin-Yin (KSY) treatment.Radial blood pressure waveform (BPW) signals were measured noninvasively for 3 minutes in 31 patients, and the FACT-G, BFI-T, and EORTC QLQ-C30 questionnaires were administered. The following pulse indices were calculated for 10 harmonics: the amplitude proportion and its coefficient of variation, and the phase angle and its standard deviation.The changes in spectral BPW indices were more prominent in Group NKSY than in Group KSY, especially for the decreases in BPW variability indices. Scores on the FACT-G, BFI-T, and EORTC QLQ-C30 questionnaires suggested that the quality of life following chemotherapy was better in Group KSY.The identified decreases in pulse variability indices could be related to the greater impairment of regulatory activities in Group NKSY. The present findings may be meaningful in developing techniques with advantages such as being noninvasive and time-saving to evaluate the blood supply and physiological conditions following chemotherapy or other treatment strategies in cancer patients.