Blood pressure measuring devices: recommendations of the European Society of Hypertension

Critical role of arterial constitutive model in predicting blood pressure from pulse wave velocity

BACKGROUND

A promising approach to cuff-less, continuous blood pressure monitoring is to estimate blood pressure (BP) from Pulse Wave Velocity (PWV). However, most existing PWV-based methods rely on empirical BP-PWV relations and have large prediction errors, which may be caused by the implicit assumption of thin-walled, linear elastic arteries undergoing small deformations. Our objective is to understand the BP-PWV relationship in the absence of such limiting assumptions.

METHOD

We performed Fluid-Structure Interaction (FSI) simulations of the radial artery and the common carotid artery under physiological flow conditions. In these dynamic simulations, we employed two constitutive models for the arterial wall: the linear elastic model, implying a thin-walled linear elastic artery undergoing small deformations, and the Holzapfel-Gasser-Ogden (HGO) model, accounting for the nonlinear effects of collagen fibers and their orientations on the large arterial deformation.

RESULTS

Despite the changing BP, the linear elastic model predicts a constant PWV throughout a cardiac cycle, which is not physiological. The HGO model correctly predicts a positive BP-PWV correlation by capturing the nonlinear deformation of the artery, showing up to 50 % variations of PWV in a cardiac cycle.

CONCLUSION

Dynamic FSI simulations reveal that the BP-PWV relationship strongly depends on the arterial constitutive model, especially in the radial artery. To infer BP from PWV, one must account for the varying PWV, a consequence of the nonlinear arterial response due to collagen fibers. Future efforts should be directed towards robust measurement of time-varying PWV if it is to be used to predict BP.

DNN-BP: a novel framework for cuffless blood pressure measurement from optimal PPG features using deep learning model

Continuous blood pressure (BP) provides essential information for monitoring one's health condition. However, BP is currently monitored using uncomfortable cuff-based devices, which does not support continuous BP monitoring. This paper aims to introduce a blood pressure monitoring algorithm based on only photoplethysmography (PPG) signals using the deep neural network (DNN). The PPG signals are obtained from 125 unique subjects with 218 records and filtered using signal processing algorithms to reduce the effects of noise, such as baseline wandering, and motion artifacts. The proposed algorithm is based on pulse wave analysis of PPG signals, extracted various domain features from PPG signals, and mapped them to BP values. Four feature selection methods are applied and yielded four feature subsets. Therefore, an ensemble feature selection technique is proposed to obtain the optimal feature set based on major voting scores from four feature subsets. DNN models, along with the ensemble feature selection technique, outperformed in estimating the systolic blood pressure (SBP) and diastolic blood pressure (DBP) compared to previously reported approaches that rely only on the PPG signal. The coefficient of determination ( R 2 ) and mean absolute error (MAE) of the proposed algorithm are 0.962 and 2.480 mmHg, respectively, for SBP and 0.955 and 1.499 mmHg, respectively, for DBP. The proposed approach meets the Advancement of Medical Instrumentation standard for SBP and DBP estimations. Additionally, according to the British Hypertension Society standard, the results attained Grade A for both SBP and DBP estimations. It concludes that BP can be estimated more accurately using the optimal feature set and DNN models. The proposed algorithm has the potential ability to facilitate mobile healthcare devices to monitor continuous BP.

Correlation of ankle-wrist pressure index with ankle-brachial pressure index to assess lower limb perfusion in diabetic foot ulcer: a pilot study

OBJECTIVE

The presence of peripheral artery disease (PAD) in patients with diabetic foot ulcers (DFUs) is a significant risk factor for chronicity and amputation. Ankle-brachial pressure index (ABPI) is a screening tool for PAD. Brachial systolic pressure measurement, used as a denominator in the calculation of ABPI, produces inaccurate results in patients with obesity and the presence of heavy clothing. The wrist, however, is easily accessible, and the ankle-wrist pressure index (AWPI), if comparable with ABPI, may be useful in screening selected patients. This study aimed to assess the efficacy of AWPI in diagnosing perfusion in DFUs and compare it to ABPI in patients with DFUs.

METHOD

ABPI and AWPI were calculated by measuring systolic blood pressure in the arteries of the ankle, arm and wrist with a handheld Doppler. Actual perfusion was determined by the presence or absence of PAD by duplex ultrasound.

RESULTS

A total of 46 lower extremities in 41 patients were studied. The prevalence of PAD was 61%. Duplex ultrasound confirmed that the sensitivity of ABPI and AWPI in detecting PAD in patients with DFUs was 67.9% and 71.4% respectively, whereas the specificity of ABPI and AWPI was 94.4% and 88.9% respectively. On receiver operating characteristic analysis, the area under the curve of ABPI and AWPI was 0.804 and 0.795, respectively. A statistically significant positive correlation between ABPI and AWPI was found (r=0.986; p<0.001).

CONCLUSION

There was a good correlation between ABPI and AWPI over a wide range of values. ABPI and AWPI may have a similar role in predicting perfusion in patients with DFUs. AWPI could be used in place of ABPI in selected patients in whom measuring ABPI may be difficult.

DECLARATION OF INTEREST

The authors have no conflicts of interest to declare.

Validity and reliability of a wearable blood flow restriction training device for arterial occlusion pressure assessment

Purpose

The blood flow restriction (BFR) training is an effective approach to promoting muscle strength, muscle hypertrophy, and regulating the peripheral vascular system. It is recommended to use to the percentage of individual arterial occlusion pressure (AOP) to ensure safety and effectiveness. The gold standard method for assessing arterial occlusive disease is typically measured using Doppler ultrasound. However, its high cost and limited accessibility restrict its use in clinical and practical applications. A novel wearable BFR training device (Airbands) with automatic AOP assessment provides an alternative solution. This study aims to examine the reliability and validity of the wearable BFR training device.

Methods

Ninety-two participants (46 female and 46 male) were recruited for this study. Participants were positioned in the supine position with the wearable BFR training device placed on the proximal portion of the right thigh. AOP was measured automatically by the software program and manually by gradually increasing the pressure until the pulse was no longer detected by color Doppler ultrasound, respectively. Validity, inter-rater reliability, and test-retest reliability were assessed by intraclass correlation coefficients (ICC) and Bland-Altman analysis.

Results

The wearable BFR training device demonstrated good validity (ICC = 0.85, mean difference = 4.1 ± 13.8 mmHg [95% CI: -23.0 to 31.2]), excellent inter-rater reliability (ICC = 0.97, mean difference = -1.4 ± 6.7 mmHg [95% CI: -14.4 to 11.7]), and excellent test-retest reliability (ICC = 0.94, mean difference = 0.6 ± 8.6 mmHg [95% CI: -16.3 to 17.5]) for the assessment of AOP. These results were robust in both male and female subgroups.

Conclusion

The wearable BFR training device can be used as a valid and reliable tool to assess the AOP of the lower limb in the supine position during BFR training.

Food intake, plasma copeptin and cardiovascular risk in patients with type 2 diabetes: A cross-sectional analysis

BACKGROUND AND AIM

Increased consumption of ultra-processed foods has been linked to both mortality and cardiovascular risk. Copeptin levels may serve as potential risk markers for cardiovascular death and events. This cross-sectional analysis seeks to assess the potential correlation between the intake of ultra-processed foods and copeptin levels in outpatients diagnosed with type 2 diabetes, based on estimates of cardiovascular risk.

METHODS AND RESULTS

Outpatients underwent clinical and nutritional assessments. Dietary information was gathered using a validated quantitative food frequency questionnaire, and the consumption of all foods, beverages, and food products was assessed according to the NOVA food classification system. Fasting plasma-EDTA samples were collected and preserved at -80 °C. Plasma copeptin measurements were analyzed using an enzyme-linked immunosorbent assay based on the competition principle. Participants were categorized into two groups: high risk and very high risk, based on cardiovascular risk calculated by the HEARTS calculator. A total of 190 participants were included in the evaluation, with an average age of 60 ± 9 years, glycated hemoglobin of 8.4 ± 1.4%, and a diabetes duration of 11 (5-19) years. Patients at a very high cardiovascular risk exhibited higher plasma copeptin levels compared to those at high cardiovascular risk. Notably, 92.1% of patients reported consuming more than 10% of total energy intake from ultra-processed foods, although this proportion did not differ between the two groups.

CONCLUSION

This patient sample reported elevated consumption of ultra-processed foods; nevertheless, the correlation between ultra-processed foods and plasma copeptin has not been substantiated.

Association between antihypertensive treatment, blood pressure variability, and stroke severity and outcomes in acute ischemic stroke

OBJECTIVES

The management of blood pressure (BP) and the role of antihypertensive medications (AHT) in acute ischemic stroke (AIS) remain uncertain. This study aimed to investigate the impact of pre- and intra-stroke AHT use on systolic (SBP), diastolic (DBP), and blood pressure variability (BPV).

MATERIALS AND METHODS

A post-hoc analysis was conducted on 228 AIS patients from the PREVISE study. All patients underwent 24-hour ambulatory blood pressure monitoring within 48 h of symptom onset. Clinical and laboratory data, as well as AHT details, were recorded. Mean BP parameters and BPV for SBP and DBP were computed. The study endpoint was 3-month mortality.

RESULTS

The majority of stroke patients (84.2%) were already taking AHTs. Beta blockers and ACE inhibitors use before and after stroke were linked to higher DBP variability. Prior angiotensin receptor blockers (ARBs) and vasodilators use correlated with increased SBP variability and lower daytime SBP/DBP levels, respectively. The continuation, discontinuation, or change of AHTs after stroke onset did not significantly affect outcomes. Patients under AHTs during AIS exhibited reduced mortality, with those previously using calcium channel blockers experiencing less severe strokes, and those previously using ARBs showing better outcomes at three months.

CONCLUSIONS

These findings advocate for personalized BP management in AIS, based on a patient's antihypertensive history. These insights could enhance treatment efficacy, guide research, and improve care for acute ischemic stroke patients.

Could the Wheel of Cardiovascular Health Diet Be a Tool for Diet Quality in Nutritional Counseling? Comparison with Healthy Eating Index-2020

OBJECTIVE

The Wheel of Cardiovascular Health Diet was recently elaborated based on current nutritional recommendations for healthy eating and was made for person-centered nutritional counseling, thus it may be an interesting tool to improve food awareness. However, its validity is yet to be tested. We hypothesized that the self-perception of patients diagnosed with chronic diseases, evaluated by the Wheel of Cardiovascular Health Diet, is satisfactory in assessing the quality of their diets, compared with the Healthy Eating Index-2020.

METHODS

This is a cross-sectional analysis of adults with uncontrolled hypertension or type 2 diabetes. The patients answered the Wheel of Cardiovascular Health Diet, and then, based on the food frequency questionnaire, we analyzed the Healthy Eating Index-2020.

RESULTS

A total of 330 patients were included in the study: 91.5% had hypertension, 58.5% had type 2 diabetes, and the median age was 58 (50-65) years. The mean difference observed between the percentage of the graphic area assessed by the patients' self-perception from the Wheel of Cardiovascular Health Diet and Healthy Eating Index-2020 was -10.0% (95%CI -35.3 to 15.3), and a moderate correlation was observed. Linear Regression models showed that a 10-point increase in patients' self-perception in the Wheel of Cardiovascular Health Diet is associated with a 2.9% increase (95%CI 2.08 to 3.70) in the diet quality by the Healthy Eating Index-2020 and is associated with lower BMI values: β = -0.42 kg/m2 (95%CI -0.83 to -0.01).

CONCLUSION

The Wheel of Cardiovascular Health Diet performed satisfactorily regarding validity and reliability by BMI and was associated with higher overall dietary quality, with the Healthy Eating Index-2020 as a relative reference.

Taslim Uddin Raju S, Hossain MI, Dipto SA, M. Tanvir Uddin S, Sijan A, Chowdhury MAS, Ahmad A, Mahamudul Hasan Khan M. A continuous cuffless blood pressure measurement from optimal PPG characteristic features using machine learning algorithms

Background and objective

Hypertension is a potentially dangerous health condition that can be detected by measuring blood pressure (BP). Blood pressure monitoring and measurement are essential for preventing and treating cardiovascular diseases. Cuff-based devices, on the other hand, are uncomfortable and prevent continuous BP measurement.

Methods

In this study, a new non-invasive and cuff-less method for estimating Systolic Blood Pressure (SBP), Mean Arterial Pressure (MAP), and Diastolic Blood Pressure (DBP) has been proposed using characteristic features of photoplethysmogram (PPG) signals and nonlinear regression algorithms. PPG signals were collected from 219 participants, which were then subjected to preprocessing and feature extraction steps. Analyzing PPG and its derivative signals, a total of 46 time, frequency, and time-frequency domain features were extracted. In addition, the age and gender of each subject were also included as features. Further, correlation-based feature selection (CFS) and Relief F feature selection (ReliefF) techniques were used to select the relevant features and reduce the possibility of over-fitting the models. Finally, support vector regression (SVR), K-nearest neighbour regression (KNR), decision tree regression (DTR), and random forest regression (RFR) were established to develop the BP estimation model. Regression models were trained and evaluated on all features as well as selected features. The best regression models for SBP, MAP, and DBP estimations were selected separately.

Results

The SVR model, along with the ReliefF-based feature selection algorithm, outperforms other algorithms in estimating the SBP, MAP, and DBP with the mean absolute error of 2.49, 1.62 and 1.43 mmHg, respectively. The proposed method meets the Advancement of Medical Instrumentation standard for BP estimations. Based on the British Hypertension Society standard, the results also fall within Grade A for SBP, MAP, and DBP.

Conclusion

The findings show that the method can be used to estimate blood pressure non-invasively, without using a cuff or calibration, and only by utilizing the PPG signal characteristic features.

ACNN-BiLSTM: A Deep Learning Approach for Continuous Noninvasive Blood Pressure Measurement Using Multi-Wavelength PPG Fusion

As an essential physiological indicator within the human body, noninvasive continuous blood pressure (BP) measurement is critical in the prevention and treatment of cardiovascular disease. However, traditional methods of blood pressure prediction using a single-wavelength Photoplethysmographic (PPG) have bottlenecks in further improving BP prediction accuracy, which limits their development in clinical application and dissemination. To this end, this study proposed a method to fuse a four-wavelength PPG and a BP prediction model based on the attention mechanism of a convolutional neural network and bidirectional long- and short-term memory (ACNN-BiLSTM). The effectiveness of a multi-wavelength PPG fusion method for blood pressure prediction was evaluated by processing PPG signals from 162 volunteers. The study compared the performance of the PPG signals with different individual wavelengths and using a multi-wavelength PPG fusion method in blood pressure prediction, assessed using mean absolute error (MAE), root mean squared error (RMSE) and AAMI-related criteria. The experimental results showed that the ACNN-BiLSTM model achieved a better MAE ± RMSE for a systolic BP and diastolic BP of 1.67 ± 5.28 and 1.15 ± 2.53 mmHg, respectively, when using the multi-wavelength PPG fusion method. As a result, the ACNN-BiLSTM blood pressure model based on multi-wavelength PPG fusion could be considered a promising method for noninvasive continuous BP measurement.

Accuracy of oscillometric-based blood pressure monitoring devices: impact of pulse volume, arrhythmia, and respiratory artifact

An oscillometric waveform (OMW) envelope-based blood pressure (BP) monitoring device is widely used to monitor blood pressure and prevent hypertension and adverse cardiovascular events. At present, all primary care physicians and clinicians widely recommend oscillometric-based BP devices. The consumer selects the device based on their own decision, without knowing whether the device is validated or not, resulting in over- or under-treatment of hypertension. It is imperative that each device must comply with international protocols. In this study, we have investigated the accuracy of inflation and deflation-based oscillometric BP monitoring devices in the case of sinus rhythm (SR). Since different health conditions of the patient affect the oscillometric waveform, which can affect the device's accuracy, in such cases, many BP monitors are skeptical of succeeding in the norms of international protocols. Therefore, this study also aims to calculate the accuracy of these devices in various health conditions and measure the effect of pulse volume, arrhythmia, and respiratory artifact on it using a non-invasive blood pressure (NIBP) simulator. We found that the oscillometric BP devices failed to measure the correct blood pressure in several clinical conditions.

Exploring the Impact of a Trauma-informed Yoga and Mindfulness Curriculum for Multiple Populations: A Pilot Study

Individuals with trauma experience negative mental health impacts and are at risk of poor cardiovascular outcomes. Unmanaged, these conditions may worsen, compromising healing and wellbeing. Yoga, particularly trauma-informed, may improve outcomes. The current pilot study explores the impact of a novel trauma-informed yoga and mindfulness curriculum on wellbeing in two parts. The first examined mental health (stress, mood) outcomes in four trauma-impacted populations: adults who were incarcerated (INC), individuals in recovery from substance use disorders (SU), veterans (VA), and vulnerable youth (YTH) assessing both the impact of individual class participation and impact of attending at least four curriculum sessions. For the subgroup of incarcerated individuals, impact by theme was examined. After curriculum sessions, stress was reduced, and mood improved. Across multiple sessions both the largest decreases in stress and greatest increase in mood occurred after participant in the first session. Further, a specific exploration of curriculum class impact by theme for participants who were incarcerated indicated no difference in impact by theme. The second part of this study explored cardiovascular outcomes for the population of those in recovery from substance use. Reductions in systolic blood pressure occurred immediately after the first curriculum session, and diastolic blood pressure reduced over three consecutive sessions.

Photoplethysmography-based cuffless blood pressure estimation: an image encoding and fusion approach

Objective.Photoplethysmography (PPG) is a promising wearable technology that detects volumetric changes in microcirculation using a light source and a sensor on the skin's surface. PPG has been shown to be useful for non-invasive blood pressure (BP) measurement. Deep learning-based BP measurements are now gaining popularity. However, almost all methods focus on 1D PPG. We aimed to design an end-to-end approach for estimating BP using image encodings from a 2D perspective.Approach.In this paper, we present a BP estimation approach based on an image encoding and fusion (BP-IEF) technique. We convert the PPG into five image encodings and use them as input. The proposed BP-IEF consists of two parts: an encoder and a decoder. In addition, three kinds of well-known neural networks are taken as the fundamental architecture of the encoder. The decoder is a hybrid architecture that consists of convolutional and fully connected layers, which are used to fuse features from the encoder.Main results.The performance of the proposed BP-IEF is evaluated on the UCI database in both non-mixed and mixed manners. On the non-mixed dataset, the root mean square error and mean absolute error for systolic BP (SBP) are 13.031 mmHg and 9.187 mmHg respectively, while for diastolic BP (DBP) they are 5.049 mmHg and 3.810 mmHg. On the mixed dataset, the corresponding values for SBP are 4.623 mmHg and 3.058 mmHg, while for DBP the values are 2.350 mmHg and 1.608 mmHg. In addition, both SBP and DBP estimation on the mixed dataset achieved grade A compared to the British Hypertension Society standard. The DBP estimation on the non-mixed dataset also achieved grade A.Significance.The results indicate that the proposed approach has the potential to improve on the current mobile healthcare for cuffless BP measurement.

Non-Invasive Blood Pressure Sensing via Machine Learning

In this paper, a machine learning (ML) approach to estimate blood pressure (BP) using photoplethysmography (PPG) is presented. The final aim of this paper was to develop ML methods for estimating blood pressure (BP) in a non-invasive way that is suitable in a telemedicine health-care monitoring context. The training of regression models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) was conducted using new extracted features from PPG signals processed using the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the interest was on the use of the most significant features obtained by the Minimum Redundancy Maximum Relevance (MRMR) selection algorithm to train eXtreme Gradient Boost (XGBoost) and Neural Network (NN) models. This aim was satisfactorily achieved by also comparing it with works in the literature; in fact, it was found that XGBoost models are more accurate than NN models in both systolic and diastolic blood pressure measurements, obtaining a Root Mean Square Error (RMSE) for SBP and DBP, respectively, of 5.67 mmHg and 3.95 mmHg. For SBP measurement, this result is an improvement compared to that reported in the literature. Furthermore, the trained XGBoost regression model fulfills the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) as well as grade A of the British Hypertension Society (BHS) standard.

Automatic Calibration of a Device for Blood Pressure Waveform Measurement

This article presents a prototype of a new, non-invasive, cuffless, self-calibrating blood pressure measuring device equipped with a pneumatic pressure sensor. The developed sensor has a double function: it measures the waveform of blood pressure and calibrates the device. The device was used to conduct proof-of-concept measurements on 10 volunteers. The main novelty of the device is the pneumatic pressure sensor, which works on the principle of a pneumatic nozzle flapper amplifier with negative feedback. The developed device does not require a cuff and can be used on arteries where cuff placement would be impossible (e.g., on the carotid artery). The obtained results showed that the systolic and diastolic pressure measurement errors of the proposed device did not exceed ±6.6% and ±8.1%, respectively.

Validation of the AViTA BPM636 upper arm blood pressure monitor in adults and pregnant women according to the ANSI/AAMI/ISO 81060-2:2013

OBJECTIVE

To evaluate the accuracy of the AViTA oscillometric upper arm home blood pressure (BP) monitor in adult and pregnant populations according to the American National Standards Institute/Association for the Advancement of Medical Instrumentation/ International Organization for Standardization (ANSI/AAMI/ISO) Universal Standard (ISO 81060-2:2013).

METHODS

BP measurements on the upper arm were performed on 85 adult subjects and 46 pregnant subjects. The AViTA BPM636 and a standard mercury reference sphygmomanometer were applied and followed the same arm sequential BP measurement method. The universal cuff of the test device was used for arm circumference of 22-42 cm.

RESULTS

For validation criterion 1, the mean ± SD of the differences between the test device and reference BP readings was 1.1 ± 5.49/2.9 ± 5.17 mmHg (systolic/diastolic) for adults; and -2.2 ± 5.93/1.5 ± 4.92 mmHg (systolic/diastolic) for pregnant women. For criterion 2, the SD of the averaged BP differences between the test device and reference BP per adult subject was 4.45/4.20 mmHg (systolic/diastolic) and per pregnant women was 4.66/3.96.

CONCLUSION

The AViTA BPM636 had passed the criteria of the ANSI/AAMI/ISO 81060-2:2013 protocol and can be recommended for home BP measurements in adults and pregnant populations.

Midday Dipping and Circadian Blood Pressure Patterns in Acute Ischemic Stroke

The purpose of this study was to investigate the alterations in blood pressure (BP) during midday and the changes in circadian BP patterns in the acute phase of ischemic stroke (AIS) with the severity of stroke and their predictive role outcomes within 3 months. A total of 228 AIS patients (a prospective multicenter follow-up study) underwent 24 h ambulatory blood pressure monitoring (ABPM). Mean BP parameters during the day (7:00-22:59), the midday (13:00-16:59), and the night (23:00-6:59), and midday and nocturnal dipping were calculated. Midday SBP dippers had less severe stroke, lower incidence of hypertension and SBP/DBP on admission, lower levels of serum glucose and WBCs, and delayed initiation of ABPM compared to risers. There was a reverse relation between midday SBP dipping and both nocturnal dipping and stroke severity. The "double dippers" (midday and nocturnal dipping) had the least severe stroke, the lowest SBP/DBP on admission, the lowest heart rate from ABPM, and a lower risk of an unfavorable outcome, while the "double risers" had the opposite results, by an approximately five-fold risk of death/disability at 3 months. These findings indicate different circadian BP patterns during the acute phase of AIS, which could be considered a marker of stroke severity and prognosis.

A Multi-modal Teacher-student Framework for Improved Blood Pressure Estimation

Blood pressure (BP) is a critical vital sign that hypertensive patients regularly measure. In this study, we propose a novel BP estimation framework to distill the knowledge from a multi-modal model to a uni-modal BP estimation model through teacher-student training. The multi-modal BP estimation model consists of three components: first, a gated recurrent unit network that generates features from photoplethysmogram, electrocardiogram, age, height, and weight; second, an attention mechanism that integrates each feature into joint embeddings; and third, a regression layer that estimates BP from the joint embeddings. The uni-modal BP estimation model has similar components to the multi-modal model but uses only PPG signal. BP is predicted by the embeddings extracted from the uni-modal model, and these embeddings are trained to be as similar as possible to the joint embeddings extracted from the multi-modal model. The proposed method demonstrates absolute prediction errors of 5.24±6.41 and 3.49±3.85 mmHg for systolic blood pressure and diastolic blood pressure in the MIMIC-III dataset, satisfying the AAMI standard.

Non-invasive estimation of arterial blood pressure fluctuations using a peripheral photoplethysmograph inside the MRI scanner

The blood-oxygen-level-dependent (BOLD) signal measured by functional magnetic resonance imaging (fMRI) is modulated by neural activity through the neurovascular coupling effect, as well as non-neural factors of physiological origin such as heart rate, respiration, and arterial blood pressure (ABP). While the former two effects have been previously characterized, the modulation of the BOLD signal by ABP fluctuations is still poorly understood. This is largely due to the difficulty of obtaining reliable ABP measurements in the MRI environment. Here, we propose a combined experimental and mathematical modeling framework to estimate ABP fluctuations inside the MRI scanner using photoplethysmography (PPG). Specifically, we used concurrent PPG and ABP measurements obtained outside the scanner to train the mathematical model and applied it to PPG measurements obtained inside the MRI scanner. Our results suggest good agreement between the model-predicted and experimentally measured ABP fluctuations and region specific correlations with the BOLD fluctuations.

Prognostic significance of 24-h blood pressure and variability indices in the outcome of acute ischaemic stroke

BACKGROUND

The association between blood pressure (BP) levels and BP variability (BPV) following acute ischaemic stroke (AIS) and outcome remains controversial.

AIMS

To investigate the predictive value of systolic BP (SBP) and diastolic BP (DBP) and BPV measured using 24-h ambulatory blood pressure monitoring (ABPM) methods during AIS regarding outcome.

METHODS

A total of 228 AIS patients (175 without prior disability) underwent ABPM every 20 min within 48 h from onset using an automated oscillometric device (TM 2430, A&D Company Ltd) during day time (7:00-22:59) and night time (23:00-6:59). Risk factors, stroke subtypes, clinical and laboratory findings were recorded. Mean BP parameters and several BPV indices were calculated. End-points were death and unfavourable functional outcome (disability/death) at 3 months.

RESULTS

A total of 61 (26.7%) patients eventually died. Multivariate logistic regression analysis revealed that only mean night-time DBP (hazard ratio (HR): 1.04; 95% confidence interval (CI): 1.00-1.07) was an independent prognostic factor of death. Of the 175 patients without prior disability, 79 (45.1%) finally met the end-point of unfavourable functional outcome. Mean 24-h SBP (HR: 1.03; 95% CI: 1.00-1.05), day-time SBP (HR: 1.02; 95% CI: 1.00-1.05) and night-time SBP (HR: 1.03; 95% CI: 1.01-1.05), SBP nocturnal decline (HR: 0.93; 95% CI: 0.88-0.99), mean 24-h DBP (HR: 1.08; 95% CI: 1.03-1.13), day-time DBP (HR: 1.07; 95% CI: 1.03-1.12) and night-time DBP (HR: 1.06; 95% CI: 1.02-1.10) were independent prognostic factors of an unfavourable functional outcome.

CONCLUSIONS

In contrast with BPV indices, ABPM-derived BP levels and lower or absence of BP nocturnal decline in the acute phase are prognostic factors of outcome in AIS patients.

Deep-learning-based blood pressure estimation using multi channel photoplethysmogram and finger pressure with attention mechanism

Recently, several studies have proposed methods for measuring cuffless blood pressure (BP) using finger photoplethysmogram (PPG) signals. This study presents a new BP estimation system that measures PPG signals under progressive finger pressure, making the system relatively robust to errors caused by finger position when using the cuffless oscillometric method. To reduce errors caused by finger position, we developed a sensor that can simultaneously measure multi-channel PPG and force signals in a wide field of view (FOV). We propose a deep-learning-based algorithm that can learn to focus on the optimal PPG channel from multi channel PPG using an attention mechanism. The errors (ME ± STD) of the proposed multi channel system were 0.43±9.35 mmHg and 0.21 ± 7.72 mmHg for SBP and DBP, respectively. Through extensive experiments, we found a significant performance difference depending on the location of the PPG measurement in the BP estimation system using finger pressure.

The Preventiometer - reliability of a cardiovascular multi-device measurement platform and its measurement agreement with a cohort study

BACKGROUND

Multimedia multi-device measurement platforms may make the assessment of prevention-related medical variables with a focus on cardiovascular outcomes more attractive and time-efficient. The aim of the studies was to evaluate the reliability (Study 1) and the measurement agreement with a cohort study (Study 2) of selected measures of such a device, the Preventiometer.

METHODS

In Study 1 (N = 75), we conducted repeated measurements in two Preventiometers for four examinations (blood pressure measurement, pulse oximetry, body fat measurement, and spirometry) to analyze their agreement and derive (retest-)reliability estimates. In Study 2 (N = 150), we compared somatometry, blood pressure, pulse oximetry, body fat, and spirometry measurements in the Preventiometer with corresponding measurements used in the population-based Study of Health in Pomerania (SHIP) to evaluate measurement agreement.

RESULTS

Intraclass correlations coefficients (ICCs) ranged from .84 to .99 for all examinations in Study 1. Whereas bias was not an issue for most examinations in Study 2, limits of agreement for most examinations were very large compared to results of similar method comparison studies.

CONCLUSION

We observed a high retest-reliability of the assessed clinical examinations in the Preventiometer. Some disagreements between Preventiometer and SHIP examinations can be attributed to procedural differences in the examinations. Methodological and technical improvements are recommended before using the Preventiometer in population-based research.

Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar-A Feasibility Study

Blood pressure monitoring is of paramount importance in the assessment of a human's cardiovascular health. The state-of-the-art method remains the usage of an upper-arm cuff sphygmomanometer. However, this device suffers from severe limitations-it only provides a static blood pressure value pair, is incapable of capturing blood pressure variations over time, is inaccurate, and causes discomfort upon use. This work presents a radar-based approach that utilizes the movement of the skin due to artery pulsation to extract pressure waves. From those waves, a set of 21 features was collected and used-together with the calibration parameters of age, gender, height, and weight-as input for a neural network-based regression model. After collecting data from 55 subjects from radar and a blood pressure reference device, we trained 126 networks to analyze the developed approach's predictive power. As a result, a very shallow network with just two hidden layers produced a systolic error of 9.2±8.3 mmHg (mean error ± standard deviation) and a diastolic error of 7.7±5.7 mmHg. While the trained model did not reach the requirements of the AAMI and BHS blood pressure measuring standards, optimizing network performance was not the goal of the proposed work. Still, the approach has displayed great potential in capturing blood pressure variation with the proposed features. The presented approach therefore shows great potential to be incorporated into wearable devices for continuous blood pressure monitoring for home use or screening applications, after improving this approach even further.

Continuous blood pressure monitoring by photoplethysmography - signal preprocessing requirements based on blood flow modelling

Objective.The aim of the study is to investigate the effect of the signal sampling frequency and low-pass filtering on the accuracy of the localisation of the fiducial points of the photoplethysmographic signal (PPG), and thus on the estimation of the blood pressure (i.e. the accuracy of the estimation).Approach.Statistical analysis was performed on 3,799 data samples taken from a publicly available database. Four PPG fiducial points of each sample signal were examined in the study.Main results.Simulation suggests that for noise-free data, cubic spline interpolation causes the sampling frequency (in the considered range of 62.5-500 Hz) to have only limited influence on localisation of the fiducial point. Better results were obtained for the pulse transit time (PTT) than pulse arrival time (PAT) approach. The acceptable filter band depends on the selected fiducial point and PAT or PTT approach. The best results were obtained for the tangent fiducial point.Significance.The presented results make it possible to estimate the minimum requirements for the sampling frequency and filtering of the PPG signal in order to obtain a reliable estimation of blood pressure.

Is only-child status associated with a higher blood pressure in adolescence? An observational study

Growing up with siblings may affect quality of life as well as hemodynamic parameters including blood pressure. Using weighted data from the nationwide and representative German KiGGs study, we assessed the relationship between only-child status, birth order, and arterial blood pressure in a cohort of 7311 adolescents aged between 11 and 17 years. Our data showed that only-children had the highest mean arterial blood pressure (87.3 ± 8.4 mmHg) as compared to first-born (86.3 ± 8.0), middle-born (86.4 ± 8.7), and youngest-born siblings (86.6 ± 8.2; p = 0.012). The two groups of only-children and first-borns differed significantly with respect to their age- and sex-specific, z-scored data for systolic (p = 0.047), diastolic (p = 0.012), and mean arterial blood pressure (p = 0.005). Linear regression models with blood pressure recordings as dependent variable adjusted to age, migration background, and age- and sex-specific z-scores of body-mass index confirmed that only-child status was an independent predictor of a higher diastolic blood pressure (p = 0.037). A similar result was observed for mean arterial blood pressure (p = 0.033), whereas systolic blood pressure was not associated with only-children status (p = 0.258).   Conclusion: In summary, we found a significant and positive relationship between only-child status and blood pressure, with the highest recordings in only-children and the lowest in first-borns. Models adjusted for relevant clinical confounders demonstrated slightly higher blood pressure in only-children compared to first-borns, who are both in an alpha birth order. Although these blood pressure differences were statistically significant, they have limited, if any, clinical meaning in this age group. What is Known: • In children and adults, blood pressure is significantly higher in only-children compared to children with siblings. However, it is unclear whether there are also similar blood pressure differences in adolescents and between only-children and first-borns. What is New: • Among adolescents in the alpha birth order, growing up as an only-child is associated with significantly higher mean blood pressure than among first-borns living with younger siblings.

Association of Eating Patterns and Diabetic Kidney Disease in Type 2 Diabetes: A Cross-Sectional Study

OBJECTIVE

The aim of this cross-sectional study was to evaluate the relationship between eating patterns and diabetic kidney disease in patients with type 2 diabetes.

METHODS

Outpatients underwent clinical and nutritional evaluation. Dietary information was obtained through a validated quantitative food frequency questionnaire, and eating patterns were identified by cluster analysis. Diabetic kidney disease was defined as an estimated glomerular filtration rate <60 mL/min/1.73 m² and/or persistently elevated urinary albumin concentration (albuminuria ≥ 14 mg/L). Procedures involving patients were approved by the Hospital's Ethics Committee. Patients with type 2 diabetes treated at university hospital and tertiary referral center, southern Brazil.

RESULTS

A total of 329 patients were evaluated: mean age 62 ± 10 years, body mass index 30.9 ± 4.2 kg/m², glycated hemoglobin 8.7% ± 2.0, and 10 (5 to 19) years of diabetes duration. Four eating patterns were identified based on cluster analysis: healthy= dairy products, fruits, and vegetables; snacks= dairy products, whole breads, vegetables, and low-calorie products; processed foods= refined carbohydrates and processed meat, and red meat= red meat. Poisson regression models confirmed that snack eaters (PR = 1.48, 95% CI 1.10, 1.99; P = .010) and red meat eaters (PR = 1.93, 95% CI 1.29, 2.89; P = .001) were associated with diabetic kidney disease.

CONCLUSION

In this sample of outpatients with type 2 diabetes, the patterns of snacks and red meat were associated with diabetic kidney disease as compared to a healthy pattern.

Impact of beta blockers on resting respiratory rate in older adults: A cross-sectional study

[Purpose] Beta blockers, commonly prescribed for older adults, affect heart rates and blood pressure and may reduce respiratory rates, which are used to evaluate patient status and predict outcomes. However, limited clinical evidence is available on the impact of beta blockers on respiratory rates. This study aimed to investigate the impact of beta blockers on respiratory rates in older adults. [Methods] This cross-sectional study included patients aged ≥60 years who underwent an annual checkup. Patients were excluded if they had a diagnosis of severe heart failure, chronic obstructive pulmonary disease, interstitial pneumonitis, severe anemia, or neurodegenerative disease. Doubly robust estimation with inverse probability weighting was applied to estimate the mean differences between beta blocker users and non-users. The dose-response relationship between the administered beta blockers and respiratory rates was examined using multivariable regression models. [Results] Of 637 participants, 108 had received beta blockers regularly. The adjusted mean differences (95% confidence interval, CI) in respiratory rates, pulse rates, systolic blood pressure, and diastolic blood pressure between beta blocker users and non-users were 0.35 (-0.68 to 1.37), -3.56 (-6.34 to -0.78), -5.53 (-8.53 to -2.52), and -4.70 (-8.27 to -1.14), respectively. The adjusted mean differences (95% CI) in respiratory rates per 1 mg of a carvedilol equivalent dose in all beta blocker users, liposoluble beta blocker users, and carvedilol users were -0.10 (-0.18 to -0.02), -0.35 (-0.59 to -0.11), and -0.29 (-0.54 to -0.06), respectively. [Conclusions] Beta blockers may dose-dependently reduce the respiratory rates of older adults. However, in clinical settings, the impact of beta-blocker use or non-use on the respiratory rate may not occur at a clinically important level. Clinicians should note the potentially suppressive impact of beta blockers on respiratory rates according to the situation.

Time of blood pressure in target range in acute ischemic stroke

OBJECTIVE

The purpose of this study was to investigate the association of blood pressure (BP) time-in-target range (TTR) derived from 24-h ambulatory BP monitoring (ABPM) during the acute phase of ischemic stroke (AIS), with the severity of stroke and its predictive value for the 3 months outcome.

METHODS

A total of 228 AIS patients (prospective multicenter follow-up study) underwent ABPM every 20 min within 48 h from stroke onset using an automated oscillometric device. Clinical and laboratory findings were recorded. Mean BP parameters, BP variability and TTR for SBP (90-140 mmHg), DBP (60-90 mmHg), and mean arterial pressure (MAP) were calculated. Endpoints were death and disability/death at 3 months.

RESULTS

A total of 14 942 BP measurements were recorded (∼66 per AIS patient) within 72 h of stroke onset. Patient's 24-h TTR was 34.7 ± 29.9, 64.3 ± 24.2, and 55.3 ± 29.4% for SBP, DBP and MAP, respectively. In patients without prior hypertension, TTR was lower as stroke severity increased for both DBP (P = 0.031) and MAP (P = 0.016). In 175 patients without prior disability, increase in TTR of DBP and MAP associated significantly with a decreased risk of disability/death (hazard ratio 0.96, 95% CI 0.95-0.99, P = 0.007 and hazard ratio 0.97, 95% CI 0.96-0.99, P = 0.007). TTR of SBP in 130-180 mmHg and 110-160 mmHg ranges seems to be related with mortality and disability outcomes, respectively.

CONCLUSION

TTR can be included for a more detailed description of BP course, according to stroke severity, and for the evaluation of BP predictive role, in addition to mean BP values, derived from ABPM during the acute phase of AIS.

CLINICAL TRIAL REGISTRATIONURL

http://www.clinicaltrials.gov. Unique identifier: NCT01915862.

Applied machine learning for blood pressure estimation using a small, real-world electrocardiogram and photoplethysmogram dataset

Applying machine learning techniques to electrocardiography and photoplethysmography signals and their multivariate-derived waveforms is an ongoing effort to estimate non-occlusive blood pressure. Unfortunately, real ambulatory electrocardiography and photoplethysmography waveforms are inevitably affected by motion and noise artifacts, so established machine learning architectures perform poorly when trained on data of the Multiparameter Intelligent Monitoring in Intensive Care II type, a publicly available ICU database. Our study addresses this problem by applying four well-established machine learning methods, i.e., random forest regression, support vector regression, Adaboost regression and artificial neural networks, to a small, self-sampled electrocardiography-photoplethysmography dataset (n = 54) to improve the robustness of machine learning to real-world BP estimates. We evaluated the performance using a selection of optimal feature morphologies of waveforms by using pulse arrival time, morphological and frequency photoplethysmography parameters and heart rate variability as characterization data. On the basis of the root mean square error and mean absolute error, our study showed that support vector regression gave the best performance for blood pressure estimation from noisy data, achieving an mean absolute error of 6.97 mmHg, which meets the level C criteria set by the British Hypertension Society. We demonstrate that ambulatory electrocardiography- photoplethysmography signals acquired by mobile discrete devices can be used to estimate blood pressure.

Effectiveness of an integrative programme in reducing hypertension incidence among the population at risk for hypertension: A community-based randomized intervention study in Shanghai, China

Background

We aimed to evaluate the effectiveness of a community-based integrative programme in reducing hypertension incidence among populations at high risk for hypertension in Shanghai, Eastern China.

Methods

We conducted a cluster-randomized intervention trial with a total of 607 participants (intervention, n = 303; control, n = 304) between October 2019 and October 2020. A total of 605 participants (intervention, n = 302; control, n = 303) completed the follow-up survey. The intervention group received an integrative programme that included health education, physician follow-up, and self-management, while the control group received usual care only. We used questionnaires to investigate risk factors, knowledge, attitudes, and behaviours regarding hypertension prevention for all participants at baseline and follow-up. We measured the incidence of hypertension according to the predefined protocol based on the national definition during the four follow-ups (only applicable to the intervention group) and the physical examination at the end of the intervention/programme/study. The difference-in-difference (DID) effects of the intervention were estimated using Generalized Estimating Equations.

Results

There were no significant differences in age group, gender, and educational level between intervention and control groups at baseline. The integrative programme reduced the incidence of hypertension in the intervention group compared to the control group (odds ratio (OR) = 0.27, 95% confidence interval (CI) = 0.12-0.61). The DID analysis found that the one-year intervention has improved the level of hypertension-related knowledge and attitudes regarding diagnostic criteria, complications of hypertension, and lifestyle modification (P < 0.05). The intervention was also associated with a 3.7% increase in the behaviour change rate of "not smoking" (OR = 2.50, 95% CI = 1.45-4.30) and a 34.8% increase in the rate of "monitoring blood pressure regularly" (OR = 29.61, 95% CI = 13.02-67.35).

Conclusions

The integrative programme could reduce the risk for hypertension and improve the level of hypertension-related knowledge and attitudes, affecting the formation of healthy behaviours in high-risk populations. The community-based management for high-risk groups should be scaled up and incorporated into national hypertension control programmes, which may potentially reduce the substantial burden of hypertension and cardiovascular disease in China.

Registration

ISRCTN registration number: ISRCTN74154693.

The Effects of Filtering PPG Signal on Pulse Arrival Time-Systolic Blood Pressure Correlation

Pulse arrival time (PAT), evaluated from electro-cardiogram (ECG) and photoplethysmogram (PPG) signals, has been widely used for cuff-less blood pressure (BP) estimation due to its high correlation with BP. However, the question of whether filtering the PPG signal impacts the extracted PAT values and consequently, the correlation between PAT and BP, has not been investigated before. In this paper, using data from 18 subjects, changes in the PAT values, and in the subject-specific PAT-systolic BP (SBP) correlation caused by filtering the PPG signal with variable cutoff frequencies in the range of 2 to 15 Hz are studied. For PAT extraction, three PPG characteristic points (foot, maximum slope and systolic peak) are considered. Results show that differences in the cutoff frequency can shift the PAT values and introduce a worst-case error of over 8.2 mmHg for SBP estimation, indicating that PPG signal filter settings can impact PAT-based BP estimations. Our study suggests that extracting the PAT from the maximum slope point of PPG signal filtered at 10 Hz provides the most stable correlation with SBP.

Novel Blood Pressure Waveform Reconstruction from Photoplethysmography using Cycle Generative Adversarial Networks

Continuous monitoring of blood pressure (BP) can help individuals manage their chronic diseases such as hypertension, requiring non-invasive measurement methods in free-living conditions. Recent approaches fuse Photoplethys-mograph (PPG) and electrocardiographic (ECG) signals using different machine and deep learning approaches to non-invasively estimate BP; however, they fail to reconstruct the complete signal, leading to less accurate models. In this paper, we propose a cycle generative adversarial network (CycleGAN) based approach to extract a BP signal known as ambulatory blood pressure (ABP) from a clean PPG signal. Our approach uses a cycle generative adversarial network that extends the GAN architecture for domain translation, and outperforms state-of-the-art approaches by up to 2× in BP estimation.

Blood Pressure Variability and Cognitive Function: a Scoping Review

PURPOSE OF REVIEW

To conduct a scoping review of articles which have evaluated BPV and cognitive function. Articles with keywords, titles or abstracts containing the terms 'cognitive' OR 'cognition' OR 'dementia' AND 'blood pressure variability' were identified from CINAHL, Medline, PMC and Web of Science.

RECENT FINDINGS

Methods of acquisition and analysis of BPV and cognitive measurements and their relationship were extracted from selected articles. Of 656 studies identified, 53 articles were selected. Twenty-five evaluated long-term (LTBPV), nine mid-term (MTBPV), 12 short-term (STBPV) and nine very short-term BPV (VSTBPV) with conflicting findings on the relationship between BPV and cognition. Variations existed in devices, period and procedure for acquisition. The studies also utilized a wide range of methods of BPV calculation. Thirteen cognitive assessment tools were used to measure global cognition or domain functions which were influenced by the population of interest. The interpretation of available studies was hence limited by heterogeneity. There is an urgent need for standardization of BPV assessments to streamline research on BPV and cognition. Future studies should also establish whether BPV could be a potential modifiable risk factor for cognitive decline, as well as a marker for treatment response.

The Impaired Elasticity of Large Arteries in Systemic Sclerosis Patients

(1) Background: Systemic sclerosis (SSc) is an autoimmune disease characterized by endothelial dysfunction and fibrosis of skin and visceral organs. In the last decade, attention has been focused on the macrovascular involvement of the disease. In particular, the observation of increased arterial stiffness represented an interesting aspect of the disease, as predictor of cardiovascular risk. (2) Methods: We recruited 60 SSc patients (52 ± 12 years old, 90% females) and 150 age/sex-matched healthy controls in order to evaluate both intima-media thickness of the right common carotid artery and arterial stiffness using the B-mode echography and the SphygmoCor system® tonometer. (3) Results: The carotid-femoral pulse wave velocity (PWV) was higher in SSc patients than in controls (8.6 ± 1.7 vs. 7.8 ± 1.5 m/s; p < 0.001), as was the carotid-radial PWV (7.8 ± 1.1 vs. 6.7 ± 1.4 m/s; p < 0.001). The intima-media thickness was higher in SSc than in controls (654 ± 108 vs. 602 ± 118 µm; p = 0.004). The other parameters measured at carotid (radial strain, Young’s modulus, compliance and distensibility) all indicated that arterial stiffness in tension was more pronounced in SSc. Of interest, the direct correlation between PWV and age corresponded closely in SSc. Moreover, a significant difference between SSc and controls as regards the carotid parameters was evident in younger subjects. (4) Conclusions: SSc patients showed an increased arterial stiffness compared to healthy controls. In particular, an SSc-related pathologic effect was suggested by the more pronounced increase in PWV with age and lower values of carotid elasticity in younger SSc patients than in age-matched controls.

Effects of an Iranian traditional fermented food consumption on blood glucose, blood pressure, and lipid profile in type 2 diabetes: a randomized controlled clinical trial

BACKGROUND

Ash-Kardeh is one of the few fermented foods without a dairy base in Iran, which is traditionally prepared from cereals and plants in the presence of microorganisms (mainly lactic acid bacteria).

PURPOSE

This study aimed to assess the effects of Ash-Kardeh consumption on blood glucose, lipid profile, and blood pressure in type 2 diabetic patients.

METHODS

Forty-six patients with type 2 diabetes were studied in this randomized controlled clinical trial. Subjects were randomly allocated into intervention (n = 23) and control (n = 23) groups. Individuals of both groups received the usual treatment of diabetic patients, while those in the intervention group, in addition to the usual treatment, received 250 g of Ash-Kardeh daily for 6 weeks. Fasting blood glucose, blood pressure, and lipid profiles were measured before and after the intervention.

RESULTS

Compared to the control group, Ash-Kardeh consumption led to a significant decrease in fasting blood glucose (P = 0.003), total cholesterol (P = 0.025), triglyceride (P = 0.003), systolic (P < 0.001), and diastolic blood pressure (P = 0.014) in the intervention group. Also, a significant increase in the concentrations of high-density lipoprotein cholesterol (P = 0.048) was observed after Ash-Kardeh consumption.

CONCLUSION

It seems that Ash-Kardeh consumption could improve high blood glucose, lipid profile, and hypertension in type 2 diabetic patients but does not affect low-density lipoprotein cholesterol concentrations. This study was registered on 2019-09-15 in the Iranian Registry of Clinical Trials ( www.irct.ir ) with the code number IRCT20170202032367N3.

Prediction of cardiovascular events from systolic or diastolic blood pressure

Over time, a focus on blood pressure has transferred from diastolic pressure to systolic pressure. Formal analyses of differences in predictive value are scarce. Our goal of the study was whether office SBP adds prognostic information to office DBP and whether both 24-h ambulatory SBP and 24-h ambulatory DBP is specifically important. The authors examined 2097 participants from a population cohort recruited in Copenhagen, Denmark. Cause-specific Cox regression was performed to predict 10-year person-specific absolute risks of fatal and non-fatal cardiovascular (CV) events. Also, the time-dependent area under the receiver operator curve (AUC) was utilized to evaluate discriminative ability. The calibration plots of the models (Hosmer-May test) were calculated as well as the Brier score which combines (discrimination and calibration). Adding both 24-h ambulatory SBP and 24-h ambulatory diastolic blood pressure did not significantly increase AUC for CV mortality and CV events. Moreover, adding both office SBP and office DBP did not significantly improve AUC for both CV mortality and CV events. The difference in AUC (95% confidence interval; p-value) was .26% (-.2% to .73%; .27) for 10-year CV mortality and .69% (-.09% to 1.46%; .082) for 10-year risk of CV events. The difference in AUC was .12% (-.2% to .44%; .46) for 10-year CV mortality and .04% (-.35 to .42%; .85) for 10-year risk of CV events. Moreover, for both CV mortality and CV events, office SBP did not improve prognostic information to office DBP. In addition, the Brier scores of office BP in both CV mortality and CV events were .078 and .077, respectively. Furthermore, the Brier scores were .077 and .078 in CV mortality and CV events of 24-h ambulatory. For the average population as those participating in a population survey, the 10-year discriminative ability for long-term predictions of CV death and CV events is not improved by adding systolic to diastolic blood pressure. This finding is found for ambulatory as well as office blood pressure.

Vascular Dysfunction of COVID-19 Is Partially Reverted in the Long-Term

Background:

COVID-19 is characterized by severe inflammation during the acute phase and increased aortic stiffness in the early postacute phase. In other models, aortic stiffness is improved after the reduction of inflammation. We aimed to evaluate the mid- and long-term effects of COVID-19 on vascular and cardiac autonomic function. The primary outcome was aortic pulse wave velocity (aPWV).

Methods:

The cross-sectional Study-1 included 90 individuals with a history of COVID-19 and 180 matched controls. The longitudinal Study-2 included 41 patients with COVID-19 randomly selected from Study-1 who were followed-up for 27 weeks.

Results:

Study-1: Compared with controls, patients with COVID-19 had higher aPWV and brachial PWV 12 to 24 (but not 25–48) weeks after COVID-19 onset, and they had higher carotid Young’s elastic modulus and lower distensibility 12 to 48 weeks after COVID-19 onset. In partial least squares structural equation modeling, the higher the hs-CRP (high-sensitivity C-reactive protein) at hospitalization was, the higher the aPWV 12 to 48 weeks from COVID-19 onset (path coefficient: 0.184; P =0.04). Moreover, aPWV (path coefficient: −0.186; P =0.003) decreased with time. Study-2: mean blood pressure and carotid intima-media thickness were comparable at the end of follow-up, whereas aPWV (−9%; P =0.01), incremental Young’s elastic modulus (−17%; P =0.03), baroreflex sensitivity (+28%; P =0.049), heart rate variability triangular index (+15%; P =0.01), and subendocardial viability ratio (+12%; P =0.01×10 −4 ) were significantly improved. There was a trend toward improvement in brachial PWV (−6%; P =0.14) and carotid distensibility (+18%; P =0.05). Finally, at the end of follow-up (48 weeks after the onset of COVID-19) aPWV (+6%; P =0.04) remained significantly higher in patients with COVID-19 than in control subjects.

Conclusions:

COVID-19-related arterial stiffening involves several arterial tree portions and is partially resolved in the long-term.

Atrial Fibrillation and Hypertension: "Quo Vadis"

Hypertension is one of the most well-established risk factors for atrial fibrillation. Long-standing untreated hypertension leads to structural remodeling and electrophysiologic alterations causing an atrial myopathy that forms a vulnerable substrate for the development and maintenance of atrial fibrillation. Hypertension-induced hemodynamic, inflammatory, hormonal, and autonomic changes all appear to be important contributing factors. Furthermore, hypertension is also associated with several atrial fibrillation-related comorbidities. As such, hypertension may represent an important target for therapy in atrial fibrillation. Clinicians should be aware of pitfalls of the blood pressure measurement in atrial fibrillation. While the auscultatory method is preferred, the use of automated devices appears to be an acceptable method in the ambulatory setting. There are pathophysiologic bases and emerging clinical evidence suggesting the benefit of renin-angiotensin system inhibition in risk reduction of atrial fibrillation development particularly in patients with left ventricular hypertrophy or left ventricular dysfunction. A better understanding of hypertension's pathophysiologic link to atrial fibrillation may lead to the development of novel therapies for the primary prevention of atrial fibrillation. Finally, future studies are needed to address optimal blood pressure goal to minimize the risk of atrial fibrillation-related complications.

Cuffless blood pressure monitoring from a wristband with calibration-free algorithms for sensing location based on bio-impedance sensor array and autoencoder

Continuous monitoring of blood pressure (BP) is essential for the prediction and the prevention of cardiovascular diseases. Cuffless BP methods based on non-invasive sensors integrated into wearable devices can translate blood pulsatile activity into continuous BP data. However, local blood pulsatile sensors from wearable devices suffer from inaccurate pulsatile activity measurement based on superficial capillaries, large form-factor devices and BP variation with sensor location which degrade the accuracy of BP estimation and the device wearability. This study presents a cuffless BP monitoring method based on a novel bio-impedance (Bio-Z) sensor array built in a flexible wristband with small-form factor that provides a robust blood pulsatile sensing and BP estimation without calibration methods for the sensing location. We use a convolutional neural network (CNN) autoencoder that reconstructs an accurate estimate of the arterial pulse signal independent of sensing location from a group of six Bio-Z sensors within the sensor array. We rely on an Adaptive Boosting regression model which maps the features of the estimated arterial pulse signal to systolic and diastolic BP readings. BP was accurately estimated with average error and correlation coefficient of 0.5 ± 5.0 mmHg and 0.80 for diastolic BP, and 0.2 ± 6.5 mmHg and 0.79 for systolic BP, respectively.

Prediction Algorithms for Blood Pressure Based on Pulse Wave Velocity Using Health Checkup Data in Healthy Korean Men: Algorithm Development and Validation

BACKGROUND

Pulse transit time and pulse wave velocity (PWV) are related to blood pressure (BP), and there were continuous attempts to use these to predict BP through wearable devices. However, previous studies were conducted on a small scale and could not confirm the relative importance of each variable in predicting BP.

OBJECTIVE

This study aims to predict systolic blood pressure and diastolic blood pressure based on PWV and to evaluate the relative importance of each clinical variable used in BP prediction models.

METHODS

This study was conducted on 1362 healthy men older than 18 years who visited the Samsung Medical Center. The systolic blood pressure and diastolic blood pressure were estimated using the multiple linear regression method. Models were divided into two groups based on age: younger than 60 years and 60 years or older; 200 seeds were repeated in consideration of partition bias. Mean of error, absolute error, and root mean square error were used as performance metrics.

RESULTS

The model divided into two age groups (younger than 60 years and 60 years and older) performed better than the model without division. The performance difference between the model using only three variables (PWV, BMI, age) and the model using 17 variables was not significant. Our final model using PWV, BMI, and age met the criteria presented by the American Association for the Advancement of Medical Instrumentation. The prediction errors were within the range of about 9 to 12 mmHg that can occur with a gold standard mercury sphygmomanometer.

CONCLUSIONS

Dividing age based on the age of 60 years showed better BP prediction performance, and it could show good performance even if only PWV, BMI, and age variables were included. Our final model with the minimal number of variables (PWB, BMI, age) would be efficient and feasible for predicting BP.

Aggregation and combination of cardiovascular risk factors and their association with 10-year all-cause mortality: the PERU MIGRANT Study

OBJECTIVE

To estimate the association between the aggregation and pair-wise combination of selected cardiovascular risk factors (CVRF) and 10-year all-cause mortality.

METHODS

Secondary data analysis of the PERU MIGRANT study, a prospective population-based cohort. Ten-year all-cause mortality was determined for participants originally enrolled in the PERU MIGRANT Study (baseline in 2007) through the National Registry of Identification and Civil Status. The CVRF included hypertension, type 2 diabetes mellitus, hypercholesterolemia, and overweight/obesity. Exposures were composed of both the aggregation of the selected CVRF (one, two, and three or more CVRF) and pair-wise combinations of CVRF. Cox regression models were used to calculate hazard ratios (HR) and 95% confidence intervals (95% CI).

FINDINGS

Of the 989 participants evaluated at baseline, 976 (98.8%) had information about vital status at 10 years of follow-up (9992.63 person-years), and 63 deaths were recorded. In the multivariable model, adjusting for sociodemographic and lifestyle variables, participants with two CVRF (HR: 2.48, 95% CI: 1.03-5.99), and those with three or more CVRF (HR: 3.93, 95% CI: 1.21-12.74) had higher all-cause mortality risk, compared to those without any CVRF. The pair-wise combinations associated with the highest risk of all-cause mortality, compared to those without such comorbidities, were hypertension with type 2 diabetes (HR: 11.67, 95% CI: 3.67-37.10), and hypertension with overweight/obesity (HR: 2.76, 95% CI: 1.18-6.71).

CONCLUSIONS

The aggregation of two or more CVRF and the combination of hypertension with type 2 diabetes or overweight/obesity were associated with an increased risk of 10-year all-cause mortality. These risk profiles will inform primary and secondary prevention strategies to delay mortality from cardiovascular risk factors.

Brainstem network connectivity with mid-anterior insula predicts lower systolic blood pressure at rest in older adults with hypertension

Central regulation of heart rate and blood pressure provides the bases for a neurogenic mechanism of hypertension (HTN). Post menopause (PM) age coincides with changes in resting state functional brain connectivity (rsFC) as well as increased risk for HTN. Whether the neural networks underpinning cardioautonomic control differ between PM women with and without HTN is unclear. Phenotypic and functional neuroimaging data from the Nathan Kline Institute was first evaluated for group differences in intrinsic network connectivity between 22 HTN post menopausal women and 22 normotensive controls. Intrinsic rsFC of the midbrain-brainstem-cerebellar network with bilateral mid-anterior insula was lower in women with HTN (FWE-corrected, p < 0.05). Z-scores indicating rsFC of these regions were extracted from the 44 PM women and a cohort of 111 adults, not presenting with metabolic or neurodegenerative disease, and compared to in-office systolic and diastolic blood pressure. Lower rsFC of the left (r  = -0.17, p = 0.019) and right (r  = -0.14, p = 0.048) mid-anterior insula with brainstem nuclei was associated with higher systolic blood pressure in the combined sample. The magnitude of this effect in men and women of post menopausal age supports a neurogenic mechanism for blood pressure regulation in older adults with HTN.

Cuff-less Blood Pressure Estimation from Photoplethysmography via Visibility Graph and Transfer Learning

This paper presents a new solution that enables the use of transfer learning for cuff-less blood pressure (BP) monitoring via short duration of photoplethysmogram (PPG). The proposed method estimates BP with low computational budget by 1) creating images from segments of PPG via visibility graph (VG) that preserves the temporal information of the PPG waveform, 2) using pre-trained deep convolutional neural network (CNN) to extract feature vectors from VG images, and 3) solving for the weights and bias between the feature vectors and the reference BPs with ridge regression. Using the University of California Irvine (UCI) database consisting of 348 records, the proposed method achieves a best error performance of 0.008.46 mmHg for systolic blood pressure (SBP), and -0.045.36 mmHg for diastolic blood pressure (DBP), respectively, in terms of the mean error (ME) and the standard deviation (SD) of error, ranking grade B for SBP and grade A for DBP under the British Hypertension Society (BHS) protocol. Our novel data-driven method offers a computationally-efficient end-to-end solution for rapid and user-friendly cuff-less PPG-based BP estimation.

Cuff-Less Blood Pressure Estimation via Small Convolutional Neural Networks

Deep learning-based cuff-less blood pressure (BP) estimation methods have recently gained increased attention as they can provide accurate BP estimation with only one physiological signal as input. In this paper, we present a simple and effective method for cuff-less BP estimation by training a small-scale convolutional neural network (CNN), modified from LeNet-5, with images created from short segments of the photoplethysmogram (PPG) signal via visibility graph (VG). Results show that the trained modified LeNet-5 model achieves an error performance of 0.184±7.457 mmHg for the systolic BP (SBP), and 0.343±4.065 mmHg for the diastolic BP (DBP) in terms of the mean error (ME) and the standard deviation (SD) of error between the estimated and reference BP. Both the SBP and the DBP accuracy rank grade A under the British Hypertension Society (BHS) protocol, demonstrating that our proposed method is an accurate way for cuff-less BP estimation.

The utility of resting pulse rate in defining high blood pressure among adolescents in Mbarara municipality, Uganda

High resting pulse rate (RPR) is associated with adverse cardiovascular events and could be used as a marker of cardiovascular health. We determined the correlation between RPR and blood pressure (BP); and its accuracy in defining high blood pressure among adolescents attending secondary schools in Mbarara municipality, south-western Uganda. We conducted a cross-sectional study among secondary school adolescents aged 12-19 years in Mbarara municipality, Uganda. We captured demographic characteristics using a structured questionnaire; and measured anthropometric indices and BP. We performed a linear regression analysis to determine the relationship between RPR and blood pressure and plotted receiver operating characteristics curves (ROC) to assess the accuracy of RPR in defining high BP. We enrolled 616 adolescents with a mean age of 15.6 ± 2.0 years and 65.6% (404/616) were female. The RPR was significantly correlated with diastolic blood pressure (DBP) in both boys (Beta = 0.22 [95% CI: 0.10; 0.36]), p < 0.001 and girls (Beta = 0.51 [95% CI: 0.43; 0.60]), p < 0.001. RPR was significantly correlated with systolic blood pressure (SBP) only in the girls (Beta = 0.23 [95% CI: 0.15; 0.30]), p < 0.001. The optimal threshold for RPR in defining prehypertension was RPR ≥ 76 bpm with an area under the curve (AUC) of 0.653 [95% CI: 0.583-0.722], the sensitivity of 0.737 and specificity of 0.577. In defining hypertension, the optimal threshold was RPR ≥ 79 bpm at a sensitivity of 0.737 and specificity of 0.719, with an AUC of 0.728 [95% CI: 0.624-0.831]. Resting pulse rate was positively correlated with BP and was more accurate in defining hypertension compared to prehypertension in the study.

Motion Artifact Resilient Cuff-Less Blood Pressure Monitoring Using a Fusion of Multi-Dimensional Seismocardiograms

Blood pressure (BP) monitoring is critical to raise awareness of hypertension and hypotension, yet the commonly used techniques require the person staying still along with a cuff around the arm. Some cuff-less approaches have been researched, but all hinder the person from moving around. To address the challenge, we propose using a fusion of accelerometers to achieve motion artifact resilient blood pressure monitoring. Such technique is accomplished with the motion artifact removal process and feature extraction from multi-dimensional seismocardiograms. The efficacy of our BP monitoring models is validated in 19 young healthy adults. Both the diastolic and systolic BP monitoring models fulfill the AAMI standard and British Hypertension Society protocol. For sitting still BP monitoring, the mean and standard deviation of diastolic and systolic difference errors (DE) are 0.09 ± 4.10 and -0.25 ± 5.45 mmHg; moreover, the mean absolute difference errors (MADE) are 3.62 and 4.73 mmHg. In walking motions, the DE are 1.15 ±4.47 mmHg for diastolic BP and -0.38 ± 6.67 for systolic BP; furthermore, the MADE are 3.36 and 5.07 mmHg, respectively. The motion artifact resilient cuff-less BP monitoring reveals the potential of portable BP monitoring in healthcare environments.

Endurance Exercise Training reduces Blood Pressure according to the Wilder's Principle

The effect of antihypertensive medicine (AHM) is larger the higher the pre-treatment blood pressure level. It is unknown whether this Wilder's principle, also applies for the exercise-training blood pressure (BP) lowering effect. One hundred seventy-eight (n=178) middle-aged individuals (55±8 y) with metabolic syndrome (MetS), underwent high intensity interval training (3 days·week^-1) for 16 weeks. Participants were divided into medicated (Med; n=103) or not medicated (No Med; n=75) with AHM. Office BP was evaluated before and after the exercise-training. Correlations and stepwise regression analysis were used to determine which variable better predicted the reductions in systolic BP (SBP) with training. After training, participants with hypertension lowered SBP by a similar magnitude regardless of if they were in the Med (-15 mmHg, 95% CI-12,-19; P<0.001) or No Med group (-13 mmHg, 95% CI-9,-16; P<0.001). However, SBP did not decrease among normotensive groups (P=0.847 for Med and P=0.937 for No Med). Pre-treatment SBP levels was the best predictor of exercise-training lowering effect (r=-0.650; β=-0.642; P<0.001). For each 10 mmHg higher pre-training SBP there were a 5 mmHg deeper SBP reduction (Wilder principle). Furthermore, AHM does not interfere with exercise-training BP-lowering effect.

Ambulatory monitoring of central arterial pressure, wave reflections, and arterial stiffness in patients at cardiovascular risk

This paper reviews current 24 h ambulatory noninvasive technologies for pulse wave analysis (PWA) providing central arterial pressure, pulse wave velocity, and augmentation index and the scientific evidence supporting their use in the clinical management of patients with arterial hypertension or at risk for cardiovascular complications.The most outstanding value of these techniques lies in the fact that they are user-friendly, mostly operator independent, and enable the evaluation of vascular function during daily-life conditions, allowing to obtain repeated measurements in different out-of-office circumstances, less artificial than those of the laboratory or doctor's office.Studies performed so far suggest that 24 h PWA may represent a potentially promising tool for evaluating vascular function, structure, and damage in daily-life conditions and promoting early screening in subjects at risk. The current evidence in favor of such an approach in the clinical practice is still limited and does not recommend its routine use. In particular, at the moment, there is a shortage of long-term prognostic studies able to support the predictive value of 24 h PWA. Finally, the accuracy of the measures is strongly dependent on the type of technology and device employed with lack of interoperability among the devices that deeply affects comparability of results among studies using different technologies. It is thus mandatory in the near future to promote proper validation studies, for instance using the ARTERY protocol, and to plan well-designed long-term longitudinal studies that may prove the accuracy and high predictive value of PWA in ambulatory conditions.

Home blood pressure monitoring: methodology, clinical relevance and practical application: a 2021 position paper by the Working Group on Blood Pressure Monitoring and Cardiovascular Variability of the European Society of Hypertension

The present paper provides an update of previous recommendations on Home Blood Pressure Monitoring from the European Society of Hypertension (ESH) Working Group on Blood Pressure Monitoring and Cardiovascular Variability sequentially published in years 2000, 2008 and 2010. This update has taken into account new evidence in this field, including a recent statement by the American Heart association, as well as technological developments, which have occurred over the past 20 years. The present document has been developed by the same ESH Working Group with inputs from an international team of experts, and has been endorsed by the ESH.