Association between beat-to-beat blood pressure variability and vascular elasticity in normal young adults during the cold pressor test

Effect of zamicastat on blood pressure and heart rate response to cold pressor test: A double-blind, randomized, placebo-controlled study in healthy subjects

AIMS

Dopamine beta-hydroxylase (DβH) inhibitors, like zamicastat, hold promise for treating pulmonary arterial hypertension. This study aimed to validate the mechanism of action of zamicastat by studying its effect on the overdrive of the sympathetic nervous system (SNS).

METHODS

A single-centre, prospective, double-blind, randomized, placebo-controlled, crossover study evaluated the effect of 400 mg zamicastat in 22 healthy male subjects. Cold pressor test (CPT) was performed at screening and each treatment period on Days -1 and 10. Plasma and 24 h-urine levels of dopamine (DA), epinephrine (EPI) and norepinephrine (NE), and plasma DβH activity, were measured.

RESULTS

Compared to placebo, zamicastat showed a - 4.62 mmHg decrease in systolic blood pressure during the cold stimulus vs. rest phases on Day 10 of CPT (P = .020). Zamicastat decreased mean arterial pressure response to cold stimulus during CPT (-2.62 mmHg; P = .025). At Day 10, zamicastat significantly increased plasma DA, before CPT (12.63 ng/L; P = .040) and after CPT (19.22 ng/L; P = .001) as well as the estimated plasma EPI change from baseline after CPT (P = .040). Inhibition of plasma DβH activity ranged from 19.8% to 25.0%. At Day 10, significant reductions in 24-h urinary excretion of EPI (P = .002) and NE (P = .001) were observed. Zamicastat Cτ geometric mean ± GSD ranged from 45.86 ± 1.46 ng/mL on Day 3 to 58.64 ± 1.52 ng/mL on Day 10, with moderate inter-individual variability (CV: 32.6%-36.6%). Steady state was already achieved on Day 6.

CONCLUSIONS

Our results demonstrated the effect of zamicastat on the overdrive sympathetic response to cold stimulus, confirming its potential as SNS modulator.

Novel Metric for Non-Invasive Beat-to-Beat Blood Pressure Measurements Demonstrates Physiological Blood Pressure Fluctuations during Pregnancy

Beat-to-beat (B2B) variability in biomedical signals has been shown to have high diagnostic power in the treatment of various cardiovascular and autonomic disorders. In recent years, new techniques and devices have been developed to enable non-invasive blood pressure (BP) measurements. In this work, we aim to establish the concept of two-dimensional signal warping, an approved method from ECG signal processing, for non-invasive continuous BP signals. To this end, we introduce a novel BP-specific beat annotation algorithm and a B2B-BP fluctuation (B2B-BPF) metric novel for BP measurements that considers the entire BP waveform. In addition to careful validation with synthetic data, we applied the generated analysis pipeline to non-invasive continuous BP signals of 44 healthy pregnant women (30.9 ± 5.7 years) between the 21st and 30th week of gestation (WOG). In line with established variability metrics, a significant increase (p < 0.05) in B2B-BPF can be observed with advancing WOGs. Our processing pipeline enables robust extraction of B2B-BPF, demonstrates the influence of various factors such as increasing WOG or exercise on blood pressure during pregnancy, and indicates the potential of novel non-invasive biosignal sensing techniques in diagnostics. The results represent B2B-BP changes in healthy pregnant women and allow for future comparison with those signals acquired from women with hypertensive disorders.

Changes in beat-to-beat blood pressure and pulse rate variability following stroke

Associations between cerebrovascular disease and impaired autonomic function and cerebrovascular reactivity have led to increased interest in variability of heart rate (HRV) and blood pressure (BPV) following stroke. In this study, beat-to-beat pulse rate variability (PRV) and BPV were measured in clinically stable stroke patients (6 ischemic, 2 hemorrhagic) at least one year after their last cerebrovascular event. Beat-to-beat blood pressure (BP) measurements were collected from subjects while resting in the sitting position for one hour. Compared with healthy controls, stroke patients exhibited significantly greater time-domain (standard deviation, coefficient of variation, average real variability) and normalized high-frequency BPV (all p < 0.05). Stroke patients also exhibited lower LF:HF ratios than control subjects (p = 0.003). No significant differences were observed in PRV between the two groups, suggesting that BPV may be a more sensitive biomarker of cerebrovascular function in long-term post-stroke patients. Given a paucity of existing literature investigating beat-to-beat BPV in clinically stable post-stroke patients long (> 1 year) after their cerebrovascular events, this pilot study can help inform future studies investigating the mechanisms and effects of BPV in stroke. Elucidating this physiology may facilitate long-term patient monitoring and pharmacological management to mitigate the risk for recurrent stroke.

Intra-beat biomarker for accurate continuous non-invasive blood pressure monitoring

Accurate continuous non-invasive blood pressure (CNIBP) monitoring is the holy grail of digital medicine but remains elusive largely due to significant drifts in signal and motion artifacts that necessitate frequent device recalibration. To address these challenges, we developed a unique approach by creating a novel intra-beat biomarker (Diastolic Transit Time, DTT) to achieve highly accurate blood pressure (BP) estimations. We demonstrated our approach’s superior performance, compared to other common signal processing techniques, in eliminating stochastic baseline wander, while maintaining signal integrity and measurement accuracy, even during significant hemodynamic changes. We applied this new algorithm to BP data collected using non-invasive sensors from a diverse cohort of high acuity patients and demonstrated that we could achieve close agreement with the gold standard invasive arterial line BP measurements, for up to 20 min without recalibration. We established our approach's generalizability by successfully applying it to pulse waveforms obtained from various sensors, including photoplethysmography and capacitive-based pressure sensors. Our algorithm also maintained signal integrity, enabling reliable assessments of BP variability. Moreover, our algorithm demonstrated tolerance to both low- and high-frequency motion artifacts during abrupt hand movements and prolonged periods of walking. Thus, our approach shows promise in constituting a necessary advance and can be applied to a wide range of wearable sensors for CNIBP monitoring in the ambulatory and inpatient settings.

Investigating Methods for Cognitive Workload Estimation for Assistive Robots

Robots interacting with humans in assistive contexts have to be sensitive to human cognitive states to be able to provide help when it is needed and not overburden the human when the human is busy. Yet, it is currently still unclear which sensing modality might allow robots to derive the best evidence of human workload. In this work, we analyzed and modeled data from a multi-modal simulated driving study specifically designed to evaluate different levels of cognitive workload induced by various secondary tasks such as dialogue interactions and braking events in addition to the primary driving task. Specifically, we performed statistical analyses of various physiological signals including eye gaze, electroencephalography, and arterial blood pressure from the healthy volunteers and utilized several machine learning methodologies including k-nearest neighbor, naive Bayes, random forest, support-vector machines, and neural network-based models to infer human cognitive workload levels. Our analyses provide evidence for eye gaze being the best physiological indicator of human cognitive workload, even when multiple signals are combined. Specifically, the highest accuracy (in %) of binary workload classification based on eye gaze signals is 80.45 ∓ 3.15 achieved by using support-vector machines, while the highest accuracy combining eye gaze and electroencephalography is only 77.08 ∓ 3.22 achieved by a neural network-based model. Our findings are important for future efforts of real-time workload estimation in the multimodal human-robot interactive systems given that eye gaze is easy to collect and process and less susceptible to noise artifacts compared to other physiological signal modalities.

Time course of beat-to-beat blood pressure variability and outcome in patients with spontaneous intracerebral haemorrhage

OBJECTIVES

Increased blood pressure variability (BPV) over 24 h or longer was associated with poor clinical outcomes in patients with intracerebral haemorrhage (ICH). However, the characteristics of beat-to-beat BPV, a rapid assessment of BPV and its association with outcome in ICH patients remain unknown.

METHODS

We consecutively and prospectively recruited patients with ICH between June 2014 and December 2020. Five-minute noninvasive beat-to-beat recordings were measured serially at three time points, 1-2, 4-6 and 10-12 days after ICH onset. BPV was calculated using standard deviation (SD) and variation independent of mean (VIM). Favourable outcome was defined as modified Rankin Scale score of less than 2 at 90 days.

RESULTS

The analysis included 66 participants (54.12 ± 10.79 years; 71.2% men) and 66 age and sex-matched healthy controls. Compared with that in healthy adults, beat-to-beat BPV was significantly increased 1-2 days after ICH and was completely recovered 10-12 days later. BPV recorded 1-2 days after ICH onset was higher among patients with unfavourable outcomes than among those with favourable outcomes (all P < 0.05) and higher BPV on days 1-2 was independently associated with a 3-month unfavourable outcome after adjustment for major covariates.

CONCLUSION

Beat-to-beat BPV was significantly increased among patients with ICH and could be completely recovered 10-12 days later. In addition, beat-to-beat BPV 1-2 days after ICH was independently associated with prognosis and could be regarded as a potential prognostic predictor and effective therapeutic target in the future.

Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks

Despite the proliferation of numerous deep learning methods proposed for generic ECG classification and arrhythmia detection, compact systems with the real-time ability and high accuracy for classifying patient-specific ECG are still few. Particularly, the scarcity of patient-specific data poses an ultimate challenge to any classifier. Recently, compact 1D Convolutional Neural Networks (CNNs) have achieved the state-of-the-art performance level for the accurate classification of ventricular and supraventricular ectopic beats. However, several studies have demonstrated the fact that the learning performance of the conventional CNNs is limited because they are homogenous networks with a basic (linear) neuron model. In order to address this deficiency and further boost the patient-specific ECG classification performance, in this study, we propose 1D Self-organized Operational Neural Networks (1D Self-ONNs). Due to its self-organization capability, Self-ONNs have the utmost advantage and superiority over conventional ONNs where the prior operator search within the operator set library to find the best possible set of operators is entirely avoided. As the first study where 1D Self-ONNs are ever proposed for a classification task, our results over the MIT-BIH arrhythmia benchmark database demonstrate that 1D Self-ONNs can surpass 1D CNNs with a significant margin while having a similar computational complexity. Under AAMI recommendations and with minimal common training data used, over the entire MIT-BIH dataset 1D Self-ONNs have achieved 98% and 99.04% average accuracies, 76.6% and 93.7% average F1 scores on supra-ventricular and ventricular ectopic beat (VEB) classifications, respectively, which is the highest performance level ever reported.

Longitudinal study on the effect of surgical weight loss on beat-to-beat blood pressure variability in patients undergoing bariatric surgery: a study protocol

INTRODUCTION

Alterations in linear and non-linear parameters of beat-to-beat blood pressure variability (BPV) have been shown to predict disease prognosis and distinguish between risk categories in various pathological conditions, independently of average blood pressure levels. Obesity places subjects at elevated risk of vascular diseases, including hypertension, resulting in serious cardiac, respiratory and cerebral events. However, little is known about the status of vascular dynamics in obese and morbidly obese adults.

METHODS AND ANALYSIS

In this present quasi-experimental longitudinal study, changes in beat-to-beat BPV, using continuous, non-invasive blood pressure monitoring, in obese subjects undergoing bariatric surgery are characterised. The capacity of linear and non-linear measures of BPV to detect differences between hypertensive, prehypertensive and normotensive obese subjects prebariatric and postbariatric surgery are tested. Additionally, potential correlations between beat-to-beat BPV and age, body mass index, gender and comorbidities will be investigated. In parallel, the impact of the unsteady fluctuations of beat-to-beat blood pressure on the dynamic stresses imparted by blood flow on blood vessel walls will be explored. We expect to find altered BPV profiles in hypertensive and prehypertensive subjects as compared with normotensive subjects. We also expect to see differential normalisation in BPV profiles between hypertensive, prehypertensive and normotensive subjects over time.

ETHICS AND DISSEMINATION

The study has been approved by the Institutional Review Board at the American University of Beirut (IRB ID: BIO-2018-0040). Study results will be made available to the public through publications in peer-reviewed journals and conference papers and/or presentations.

Low-frequency component of photoplethysmogram reflects the autonomic control of blood pressure

The question of how much information the photoplethysmogram (PPG) signal contains on the autonomic regulation of blood pressure (BP) remains unsolved. This study aims to compare the low-frequency (LF) and high-frequency components of PPG and BP and assess their correlation with oscillations in interbeat (RR) intervals at similar frequencies. The PPG signal from the distal phalanx of the right index finger recorded using a reflective PPG sensor at green light, the BP signal from the left hand recorded using a Finometer, and RR intervals were analyzed. These signals were simultaneously recorded within 15 min in a supine resting condition in 17 healthy subjects (12 males and 5 females) aged 33 ± 9 years (mean ± SD). The study revealed the high coherence of LF components of PPG and BP with the LF component of RR intervals. The high-frequency components of these signals had low coherence. The analysis of the signal instantaneous phases revealed the presence of high-phase coherence between the LF components of PPG and BP. It is shown that the LF component of PPG is determined not only by local myogenic activity but also reflects the processes of autonomic control of BP.

Beat-to-beat blood pressure variability in patients with obstructive sleep apnea

STUDY OBJECTIVES

Cardiovascular comorbidities in obstructive sleep apnea (OSA) are difficult to treat, perhaps due to autonomic dysfunction. We assessed beat-to-beat blood pressure (BP) variability (BPV) in OSA while considering other markers derived from electrocardiogram and continuous BP signals.

METHODS

We studied 66 participants (33 participants with OSA: respiratory event index [mean ± SEM]: 21.1 ± 2.7 events/h; 12 females, aged 51.5 ± 2.4 years; body mass index: 32.8 ± 1.4 kg/m²; 33 healthy controls: 20 females; aged 45.3 ± 2.4 years; body mass index: 26.3 ± 0.7 kg/m²). We collected 5-minute resting noninvasive beat-to-beat BP and electrocardiogram values. From BP, we derived systolic, diastolic, and mean BP values, and calculated variability as standard deviations (systolic BPV, diastolic BPV, BPV). We also calculated diastole-to-systole time (time to peak). From the electrocardiogram, we derived QRS markers and calculated heart rate and heart rate variability. We performed a multivariate analysis of variance based on sex and group (OSA vs control), with Bonferroni-corrected post hoc comparisons (P ≤ .05) between groups. We calculated correlations of BPV with biological variables.

RESULTS

Multivariate analysis of variance showed effects of diastolic BPV and BPV in OSA; post hoc comparisons revealed high diastolic BPV and BPV only in female participants with OSA vs controls. QRS duration was higher in OSA, with post hoc comparisons showing the effect only in males. BPV correlated positively with heart rate variability in controls but not in participants with OSA. BPV correlated positively with time to peak in females with OSA and OSA combined, whereas there was no BPV-time-to-peak correlation in healthy participants.

CONCLUSIONS

The findings show sex-specific autonomic dysfunction reflected in beat-to-beat BP in OSA. The higher BPV may reflect poor baroreflex control or vascular damage in OSA, which are potential precursors to cardiovascular complications.

Beat-to-beat blood pressure variability: an early predictor of disease and cardiovascular risk

Blood pressure (BP) varies on the long, short and very-short term. Owing to the hidden physiological and pathological information present in BP time-series, increasing interest has been given to the study of continuous, beat-to-beat BP variability (BPV) using invasive and noninvasive methods. Different linear and nonlinear parameters of variability are employed in the characterization of BP signals in health and disease. Although linear parameters of beat-to-beat BPV are mainly measures of dispersion, such as standard deviation (SD), nonlinear parameters of BPV quantify the degree of complexity/irregularity- using measures of entropy or self-similarity/correlation. In this review, we summarize the value of linear and nonlinear parameters in reflecting different information about the pathophysiology of changes in beat-to-beat BPV independent of or superior to mean BP. We then provide a comparison of the relative power of linear and nonlinear parameters of beat-to-beat BPV in detecting early and subtle differences in various states. The practical advantage and utility of beat-to-beat BPV monitoring support its incorporation into routine clinical practices.

Which Parameters of Beat-to-Beat Blood Pressure Best Predict Poor In-Hospital Outcome in Spontaneous Intracerebral Hemorrhage?

Objective: There is increasing evidence that high blood pressure (BP) levels and BP variability (BPV) over 24 h or longer are associated with poor clinical outcomes in patients with intracerebral hemorrhage (ICH). The objective of this study was to examine the association between different beat-to-beat BP parameters and in-hospital outcomes.

Methods: Patients with a diagnosis of acute spontaneous ICH were recruited consecutively and prospectively between September 2018 and January 2019. Beat-to-beat recordings were measured non-invasively for 5 min within the first 72 h after the onset of symptoms. BPV was analyzed by standard deviation (SD), coefficient of variation (CV), average real variability (ARV), and variation independent of mean (VIM). Outcome was assessed at discharge using the modified Rankin Scale (mRS) score. Multivariate logistic regression analysis was used to assess the association between BP levels, BPV, and clinical outcomes.

Results: A total of 66 patients were included, of whom 34 had poor outcomes (mRS score, 3–6). Patients with poor outcomes had significantly higher National Institute of Health Stroke Scale scores (4.5 vs. 9, p < 0.001), a larger ICH volume (8 vs. 14.5 mL, p = 0.004), and an increased systolic BP (SBP) -CV (3.2 vs. 4.8, p < 0.001) and diastolic BP (DBP) -CV (3.7 vs. 4.9, p = 0.015). After adjustment for major covariates, multivariate logistic regression analysis revealed that SBP-CV was independently associated with an increased risk of poor in-hospital outcomes [odds ratio (OR) 2.535; 95% confidence interval (CI), 1.211–5.305; p = 0.014]. The receiver operating characteristic area for SBP-CV in predicting poor in-hospital outcome was 0.827 (95% CI, 0.730–0.925; p < 0.001), and the best cutoff point was 3.551 (sensitivity, 82.35%; specificity, 68.75%).

Conclusion: A higher beat-to-beat BPV in the first 72 h of admission was associated with unfavorable in-hospital outcomes in patients with ICH. The stabilization of BPV during the acute phase may be a therapeutic target; this could be tested in future clinical trials.

Reducing Dietary Sodium to 1000 mg per Day Reduces Neurovascular Transduction Without Stimulating Sympathetic Outflow

The American Heart Association recommends no more than 1500 mg of sodium/day as ideal. Some cohort studies suggest low-sodium intake is associated with increased cardiovascular mortality. Extremely low-sodium diets (≤500 mg/d) elicit activation of the renin-angiotensin-aldosterone system and stimulate sympathetic outflow. The effects of an American Heart Association-recommended diet on sympathetic regulation of the vasculature are unclear. Therefore, we assessed whether a 1000 mg/d diet alters sympathetic outflow and sympathetic vascular transduction compared with the more commonly recommended 2300 mg/d. We hypothesized that sodium reduction from 2300 to 1000 mg/d would not affect resting sympathetic outflow but would reduce sympathetic transduction in healthy young adults. Seventeen participants (age: 26±2 years, 9F/8M) completed 10-day 2300 and 1000 mg/d sodium diets in this randomized controlled feeding study (crossover). We measured resting renin activity, angiotensin II, aldosterone, blood pressure, muscle sympathetic nerve activity, and norepinephrine. We quantified beat-by-beat changes in mean arterial pressure and leg vascular conductance (femoral artery ultrasound) following spontaneous sympathetic bursts to assess sympathetic vascular transduction. Reducing sodium to 1000 mg/d increased renin activity, angiotensin II, and aldosterone ( P<0.01 for all) but did not alter mean arterial pressure (78±2 versus 77±2 mm Hg, P=0.56), muscle sympathetic nerve activity (13.9±1.3 versus 13.9±0.8 bursts/min, P=0.98), or plasma/urine norepinephrine. Sympathetic vascular transduction decreased ( P<0.01). These data suggest that reducing sodium from 2300 to 1000 mg/d stimulates the renin-angiotensin-aldosterone system, does not increase resting basal sympathetic outflow, and reduces sympathetic vascular transduction in normotensive adults.

A new approach for analysis of heart rate variability and QT variability in long-term ECG recording

Background and purpose

With the emergence of long-term electrocardiogram (ECG) recordings that extend several days beyond the typical 24–48 h, the development of new tools to measure heart rate variability (HRV) and QT variability is needed to utilize the full potential of such extra-long-term ECG recordings.

Methods

In this report, we propose a new nonlinear time–frequency analysis approach, the concentration of frequency and time (ConceFT), to study the HRV QT variability from extra-long-term ECG recordings. This approach is a generalization of Short Time Fourier Transform and Continuous Wavelet Transform approaches.

Results

As proof of concept, we used 14-day ECG recordings to show that the ConceFT provides a sharpened and stabilized spectrogram by taking the phase information of the time series and the multitaper technique into account.

Conclusion

The ConceFT has the potential to provide a sharpened and stabilized spectrogram for the heart rate variability and QT variability in 14-day ECG recordings.

Electronic supplementary material

The online version of this article (10.1186/s12938-018-0490-8) contains supplementary material, which is available to authorized users.

Sleep Extension in Short Sleepers: An Evaluation of Feasibility and Effectiveness for Weight Management and Cardiometabolic Disease Prevention

Sleep duration has become increasingly recognized as an important influencer of health. Epidemiologic and observational studies have shown associations between short sleep duration and increased risk for chronic cardiometabolic disorders, including obesity, type 2 diabetes, and cardiovascular disease. These associations have led to investigations into the potential causal pathways through which short sleep may increase risk for these disorders. Clinical intervention studies have demonstrated that restricting sleep in normal sleepers has adverse health effects, including insulin resistance, and increased blood pressure. The totality of evidence points to negative health effects of short sleep and the recognition of sleep as a lifestyle behavior that may be targeted for disease prevention. It is well established that consistent, adequate sleep is associated with the lowest risk of obesity and cardiometabolic disorders. Yet, it is unclear whether increasing sleep in short sleepers can improve health. In today's society, it is common for individuals to deprive themselves of sleep during the work week, with the intent to sleep longer during the weekend, or have "catch-up sleep." Studies that have examined the health effects of extended sleep, post-sleep restriction, revealed some improvements in health outcomes. However, it is uncertain whether the improvements observed with catch-up sleep are sufficient to reverse the negative health effects of constant sleep restriction. Few intervention studies have been undertaken to determine whether extending sleep, long-term, in short sleepers is feasible and whether it can reduce the disease risk burden associated with short sleep duration. The purpose of this review is to highlight these studies and evaluate information related to the impact of sleep extension on risk factors for chronic cardiometabolic disorders. We discuss limitations of current research, including variability in participant characteristics and the extent to which sleep behaviors are modified and monitored. Although the evidence-base for benefits of sleep extension is still in the early stages, studies to date indicate that prolonging sleep, in short sleepers, may improve cardiometabolic risk. Finally, our review calls attention to areas that require further study and for larger scale studies of behavior modification to establish the health effects of sleep extension in short sleepers.