National standard for measurement of resting and ambulatory blood pressures with automated sphygmomanometers

Hemodynamic Bedside Monitoring Instrument with Pressure and Optical Sensors: Validation and Modality Comparison

Results from two independent clinical validation studies for measuring hemodynamics at the patient's bedside using a compact finger probe are reported. Technology comprises a barometric pressure sensor, and in one implementation, additionally, an optical sensor for photoplethysmography (PPG) is developed, which can be used to measure blood pressure and analyze rhythm, including the continuous detection of atrial fibrillation. The capabilities of the technology are shown in several form factors, including a miniaturized version resembling a common pulse oximeter to which the technology could be integrated in. Several main results are presented: i) the miniature finger probe meets the accuracy requirements of non-invasive blood pressure instrument validation standard, ii) atrial fibrillation can be detected during the blood pressure measurement and in a continuous recording, iii) a unique comparison between optical and pressure sensing mechanisms is provided, which shows that the origin of both modalities can be explained using a pressure-volume model and that recordings are close to identical between the sensors. The benefits and limitations of both modalities in hemodynamic monitoring are further discussed.

Optimization and validation of a suprasystolic brachial cuff-based method for noninvasively estimating central aortic blood pressure

Clinical studies have extensively demonstrated that central aortic blood pressure (CABP) has greater clinical significance in comparison with peripheral blood pressure. Despite the existence of various techniques for noninvasively measuring CABP, the clinical applications of most techniques are hampered by the unsatisfactory accuracy or large variability in measurement errors. In this study, we proposed a new method for noninvasively estimating CABP with improved accuracy and reduced uncertain errors. The main idea was to optimize the estimation of the pulse wave transit time from the aorta to the occluded lumen of the brachial artery under a suprasystolic cuff by identifying and utilizing the characteristic information of the cuff oscillation wave, thereby improving the accuracy and stability of the CABP estimation algorithms under various physiological conditions. The method was firstly developed and verified based on large-scale virtual subject data (n = 800) generated by a computational model of the cardiovascular system coupled to a brachial cuff, and then validated with small-scale in vivo data (n = 34). The estimation errors for the aortic systolic pressure were -0.05 ± 0.63 mmHg in the test group of the virtual subjects and -1.09 ± 3.70 mmHg in the test group of the patients, both demonstrating a good performance. In particular, the estimation errors were found to be insensitive to variations in hemodynamic conditions and cardiovascular properties, manifesting the high robustness of the method. The method may have promising clinical applicability, although further validation studies with larger-scale clinical data remain necessary.

Evaluation of stroke volume estimation during orthostatic stress: the utility of Modelflow

Advanced blood pressure monitoring devices contain algorithms that permit estimation of stroke volume (SV). Modelflow (Finapres Medical Systems) is one common method to non-invasively estimate beat-to-beat SV. However, Modelflow accuracy during profound reductions in SV is unclear. We aimed to compare SV estimation by Modelflow and echocardiography, at rest and during orthostatic challenge. We tested 13 individuals (age 24 ± 2 years; 7 female) using combined head-up tilt and graded lower body negative pressure, continued until presyncope. SV was derived by both Modelflow and echocardiography on multiple occasions while supine, during orthostatic stress, and at presyncope. SV index (SVI) was determined by normalising SV for body surface area. Bias and limits of agreement were determined using Bland-Altman analyses. Two one-sided tests (TOST) examined equivalency. Across all timepoints, Modelflow estimates of SV (73.2 ± 1.6 ml) were strongly correlated with echocardiography estimates (66.1 ± 1.3 ml) (r = 0.56, P  < 0.001) with a bias of +7.1 ± 21.1 ml. Bias across all timepoints was further improved when SV was indexed (+3.6 ± 12.0 ml.m -2 ). Likewise, when assessing responses relative to baseline, Modelflow estimates of SV (-23.4 ± 1.4%) were strongly correlated with echocardiography estimates (-19.2 ± 1.3%) (r = 0.76, P  < 0.001), with minimal bias (-4.2 ± 13.1%). TOST testing revealed equivalency to within 15% of the clinical standard for SV and SVI, both expressed as absolute values and relative to baseline. Modelflow can be used to track changes in SV during profound orthostatic stress, with accuracy enhanced with correction relative to baseline values or body size. These data support the use of Modelflow estimates of SV for autonomic function testing.

Development and clinical validation of a miniaturized finger probe for bedside hemodynamic monitoring

Our aim is to develop a blood pressure (BP) measurement technology that could be integrated into a finger-worn pulse oximeter, eliminating the need for a brachial cuff. We present a miniature cuffless tonometric finger probe system that uses the oscillometric method to measure BP. Our approach uses a motorized press that is used to apply pressure to the fingertip to measure BP. We verified the functionality of the device in a clinical trial (n = 43) resulting in systolic and diastolic pressures ((mean ± SD) mmHg) of (-3.5 ± 8.4) mmHg and (-4.0 ± 4.4) mmHg, respectively. Comparison was made with manual auscultation (n = 26) and automated cuff oscillometry (n = 18). In addition to BP, we demonstrated the ability of the device to assess arterial stiffness (n = 18) and detect atrial fibrillation (n = 6). We were able to introduce a sufficiently small device that could be used for convenient ambulatory measurements with minimal discomfort.

Quantitative analysis of pulse arrival time and PPG morphological features based cuffless blood pressure estimation: a comparative study between diabetic and non-diabetic groups

Pulse arrival time (PAT) and PPG morphological features have attracted much interest in cuffless blood pressure (BP) estimation, but their effects are not clearly understood when vascular characteristics are affected by diseases such as diabetes. This work quantitatively analyzes the effect of diabetic disease on the PAT and PPG morphological features-based BP estimation. We selected 112 diabetic patients and 308 non-diabetic subjects from VitalDB, and extracted 16 features including PAT, PPG morphological features, and heart rate. BP estimation performance was statistically compared between groups using linear regression models with several feature sets, and the relative importance of each feature in the optimal feature set was extracted. As a result, the standard deviation of the error and mean absolute error of PAT-based BP estimation were significantly higher in the diabetic group than in the non-diabetic group (p < 0.01). A feature set containing PAT and PPG morphological features achieved the best performance in both groups. However, the relative importance of each feature for BP estimation differed notably between groups. The results indicate that different features are important depending on the vascular characteristics, which could help to construct different models to accommodate specific diseases.

Recent development of piezoelectric biosensors for physiological signal detection and machine learning assisted cardiovascular disease diagnosis

As cardiovascular disease stands as a global primary cause of mortality, there has been an urgent need for continuous and real-time heart monitoring to effectively identify irregular heart rhythms and to offer timely patient alerts. However, conventional cardiac monitoring systems encounter challenges due to inflexible interfaces and discomfort during prolonged monitoring. In this review article, we address these issues by emphasizing the recent development of the flexible, wearable, and comfortable piezoelectric passive sensor assisted by machine learning technology for diagnosis. This innovative device not only harmonizes with the dynamic mechanical properties of human skin but also facilitates continuous and real-time collection of physiological signals. Addressing identified challenges and constraints, this review provides insights into recent advances in piezoelectric cardiac sensors, from devices to circuit systems. Furthermore, this review delves into the integration of machine learning technologies, showcasing their pivotal role in facilitating continuous and real-time assessment of cardiac status. The synergistic combination of flexible piezoelectric sensor design and machine learning holds substantial potential in automating the detection of cardiac irregularities with minimal human intervention. This transformative approach has the power to revolutionize patient care paradigms.

A randomized n-of-1 study comparing blood pressure measured on a clothed arm and on an arm with a rolled-up sleeve

BACKGROUND

Blood pressure [BP] should be measured using a bare upper arm with an appropriately sized cuff. In practice, it is more convenient to measure BP on a bare arm below a rolled-up sleeve or on a sleeved arm.

AIM

A n-of-1 randomized controlled trial was performed to assess the difference between BP measurements over a sleeve or below a rolled-up sleeve.

METHODS

The study subject was male, white, 72 years old, BMI 26 kg/m2, arm circumference 29 cm, and under stable antihypertensive treatment. The mean of three BP measurements over a thin sleeve was compared with measurements below a rolled-up sleeve. Additional measurements on a completely bare arm, with thicker sleeves and up to three layers were performed. The order of measurements was determined by chance and two oscillometric devices were used. Descriptive statistics, Bland-Altman test and 2-side T test were used for comparisons.

RESULTS

504 measurements were performed: 50 % over the sleeve and 50 % below the rolled-up sleeve. The mean systolic blood pressure (SBP) was respectively 116.9 ± 9. 2 [95% CI 115.7-118.0, range 96-135] and 122.8 ± 9.2 [95% CI 121.7-124.0, range 103-139, p = 0.001] mm Hg. The mean diastolic blood pressure [DBP] was respectively 67.6 ± 6.8 [95% CI 66.8-68.4, range 52-84] and 71.8 ± 6.8 [95% CI 71.0-72.7, range 55-85, p = 0.001] mm Hg. There was no significant difference between the measurements over the sleeve and on the completely bare arm [n = 94, p = 0.97]. Sleeve thickness with 2 layers up to 3 mm thick did not affect the results.

CONCLUSIONS

Blood pressure measurements over a thin sleeve were significantly lower than measurements below a rolled-up sleeve and match measurements on a completely bare arm.

PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN-LSTM

Researchers commonly use continuous noninvasive blood-pressure measurement (cNIBP) based on photoplethysmography (PPG) signals to monitor blood pressure conveniently. However, the performance of the system still needs to be improved. Accuracy and precision in blood-pressure measurements are critical factors in diagnosing and managing patients' health conditions. Therefore, we propose a convolutional long short-term memory neural network (CNN-LSTM) with grid search ability, which provides a robust blood-pressure estimation system by extracting meaningful information from PPG signals and reducing the complexity of hyperparameter optimization in the proposed model. The multiparameter intelligent monitoring for intensive care III (MIMIC III) dataset obtained PPG and arterial-blood-pressure (ABP) signals. We obtained 75,226 signal segments, with 60,180 signals allocated for training data, 12,030 signals allocated for the validation set, and 15,045 signals allocated for the test data. During training, we applied five-fold cross-validation with a grid-search method to select the best model and determine the optimal hyperparameter settings. The optimized configuration of the CNN-LSTM layers consisted of five convolutional layers, one long short-term memory (LSTM) layer, and two fully connected layers for blood-pressure estimation. This study successfully achieved good accuracy in assessing both systolic blood pressure (SBP) and diastolic blood pressure (DBP) by calculating the standard deviation (SD) and the mean absolute error (MAE), resulting in values of 7.89 ± 3.79 and 5.34 ± 2.89 mmHg, respectively. The optimal configuration of the CNN-LSTM provided satisfactory performance according to the standards set by the British Hypertension Society (BHS), the Association for the Advancement of Medical Instrumentation (AAMI), and the Institute of Electrical and Electronics Engineers (IEEE) for blood-pressure monitoring devices.

Telemedicine in Adult Congenital Heart Disease: Usefulness of Digital Health Technology in the Assistance of Critical Patients

The number of adults with congenital heart disease (ACHD) has progressively increased in recent years to surpass that of children. This population growth has produced a new demand for health care. Moreover, the 2019 coronavirus pandemic has caused significant changes and has underlined the need for an overhaul of healthcare delivery. As a result, telemedicine has emerged as a new strategy to support a patient-based model of specialist care. In this review, we would like to highlight the background knowledge and offer an integrated care strategy for the longitudinal assistance of ACHD patients. In particular, the emphasis is on recognizing these patients as a special population with special requirements in order to deliver effective digital healthcare.

Cuff-Less Blood Pressure Prediction Based on Photoplethysmography and Modified ResNet

Cardiovascular disease (CVD) has become a common health problem of mankind, and the prevalence and mortality of CVD are rising on a year-to-year basis. Blood pressure (BP) is an important physiological parameter of the human body and also an important physiological indicator for the prevention and treatment of CVD. Existing intermittent measurement methods do not fully indicate the real BP status of the human body and cannot get rid of the restraining feeling of a cuff. Accordingly, this study proposed a deep learning network based on the ResNet34 framework for continuous prediction of BP using only the promising PPG signal. The high-quality PPG signals were first passed through a multi-scale feature extraction module after a series of pre-processing to expand the perceptive field and enhance the perception ability on features. Subsequently, useful feature information was then extracted by stacking multiple residual modules with channel attention to increase the accuracy of the model. Lastly, in the training stage, the Huber loss function was adopted to stabilize the iterative process and obtain the optimal solution of the model. On a subset of the MIMIC dataset, the errors of both SBP and DBP predicted by the model met the AAMI standards, while the accuracy of DBP reached Grade A of the BHS standard, and the accuracy of SBP almost reached Grade A of the BHS standard. The proposed method verifies the potential and feasibility of PPG signals combined with deep neural networks in the field of continuous BP monitoring. Furthermore, the method is easy to deploy in portable devices, and it is more consistent with the future trend of wearable blood-pressure-monitoring devices (e.g., smartphones and smartwatches).

Development of Real-Time Cuffless Blood Pressure Measurement Systems with ECG Electrodes and a Microphone Using Pulse Transit Time (PTT)

Research has shown that pulse transit time (PTT), which is the time delay between the electrocardiogram (ECG) signal and the signal from a photoplethysmogram (PPG) sensor, can be used to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP) without the need for a cuff. However, the LED of the PPG sensor requires the precise adjustment of both light intensity and light absorption rates according to the contact status of the light-receiving element. This results in the need for regular calibration. In this study, we propose a cuffless blood pressure monitor that measures real-time blood pressure using a microphone instead of a PPG sensor. The blood pulse wave is measured in the radial artery of the wrist using a microphone that can directly measure the sound generated by a body rather than sending energy inside the body and receiving a returning signal. Our blood pressure monitor uses the PTT between the R-peak of the ECG signal and two feature points of the blood pulse wave in the radial artery of the wrist. ECG electrodes and circuits were fabricated, and a commercial microelectromechanical system (MEMS) microphone was used as the microphone to measure blood pulses. The peak points of the blood pulse from the microphone were clear, so the estimated SBP and DBP could be obtained from each ECG pulse in real time, and the resulting estimations were similar to those made by a commercial cuff blood pressure monitor. Since neither the ECG electrodes nor the microphone requires calibration over time, the real-time cuffless blood pressure monitor does not require calibration. Using the developed device, blood pressure was measured three times daily for five days, and the mean absolute error (MAE) and standard deviation (SD) of the SBP and DBP were found to be 2.72 ± 3.42 mmHg and 2.29 ± 3.53 mmHg, respectively. As a preliminary study for proof-of-concept, these results were obtained from one subject. The next step will be a pilot study on a large number of subjects.

Temporal fluctuations of cardiovascular parameters after intravitreal injections

BACKGROUND

Despite the effectiveness of intravitreal injection (IVI) of anti-vascular endothelial growth factor in treating retinal diseases, there remains a paucity of evidence on potential systemic risks associated with this procedure. This study aims to investigate cardiovascular parameters and the risk of hypertensive urgency after IVIs.

METHODS

Patients who received IVIs for retinal/macular diseases were enrolled retrospectively. Patients who received cataract surgeries were enrolled as controls. Systolic and diastolic blood pressure (BP) and heart rate were measured 10 minutes before, immediately after, and more than 30 minutes after IVIs and cataract surgery. Multivariate analysis was performed to evaluate risk factors for hypertensive urgency.

RESULTS

Seventy patients who received IVIs and 95 patients who received cataract surgeries were enrolled. A higher preoperative systolic BP was found in the IVI groups than in the control group (147.0 ± 22.9 vs 136.3 ± 21.8 mmHg, respectively). The patients who received IVIs had a higher increase in perioperative systolic BP immediately after the procedure than the controls (17.43 ± 20.53 mmHg vs 9.11 ± 18.92 mmHg, p = 0.009). The IVI procedure (odds ratio [OR] 4.84, p = 0.008), preoperative systolic BP ≥160 mmHg (OR 17.891, p = 0.001, compared to preoperative systolic BP <140 mmHg), and underlying hypertension (OR 3.305, p = 0.041) were risk factors for hypertensive urgency immediately after the IVIs.

CONCLUSION

We found a transient increase in BP after IVIs, which may have been associated with hypertensive urgency and related cardiovascular disorders in older patients and in those with relevant comorbidities. Clinicians should pay more attention to these patients before performing IVIs.

Real-Time Cuffless Continuous Blood Pressure Estimation Using 1D Squeeze U-Net Model: A Progress toward mHealth

Measuring continuous blood pressure (BP) in real time by using a mobile health (mHealth) application would open a new door in the advancement of the healthcare system. This study aimed to propose a real-time method and system for measuring BP without using a cuff from a digital artery. An energy-efficient real-time smartphone-application-friendly one-dimensional (1D) Squeeze U-net model is proposed to estimate systolic and diastolic BP values, using only raw photoplethysmogram (PPG) signal. The proposed real-time cuffless BP prediction method was assessed for accuracy, reliability, and potential usefulness in the hypertensive assessment of 100 individuals in two publicly available datasets: Multiparameter Intelligent Monitoring in Intensive Care (MIMIC-I) and Medical Information Mart for Intensive Care (MIMIC-III) waveform database. The proposed model was used to build an android application to measure BP at home. This proposed deep-learning model performs best in terms of systolic BP, diastolic BP, and mean arterial pressure, with a mean absolute error of 4.42, 2.25, and 2.56 mmHg and standard deviation of 4.78, 2.98, and 3.21 mmHg, respectively. The results meet the grade A performance requirements of the British Hypertension Society and satisfy the AAMI error range. The result suggests that only using a short-time PPG signal is sufficient to obtain accurate BP measurements in real time. It is a novel approach for real-time cuffless BP estimation by implementing an mHealth application and can measure BP at home and assess hypertension.

A Novel CNN-LSTM Model Based Non-Invasive Cuff-Less Blood Pressure Estimation System

PPG (Photoplethysmography) and ECG (Electro-cardiogram) physiological signals have been known to have certain indicators for establishing blood pressure (BP) levels. Continuous monitoring of blood pressure (BP) is highly valuable for cardiovascular patients; however the existing non-invasive cuff-based blood pressure monitoring system is discreet and applies artificial pressure on patients' arms that is uncomfortable. The other invasive method is highly interventional in nature and is highly disturbing when the patient is recuperating in the hospital wards or elsewhere. A non-invasive cuff-less, non-disturbing, and continuous BP measurement system targeted toward surgical, clinical, and domestic usage are proposed in this work. A convolutional neural network (CNN) followed by a long short-term memory layer (LSTM) was designed and applied to ECG and PPG signals to present accurate systolic blood pressure (SBP), and diastolic blood pressure (DBP). For developing the CNN-LSTM layers, a novel and open-source dataset was compiled that consisted of PPG and ECG signals from 30 healthy participants and is made publicly available for further usage to the research community. The novel CNN-LSTM based cuff-less blood pressure evaluation system presented a mean absolute error (MAE) of 2.57 mmHg and 3.44 mmHg for SBP and DBP respectively with similar standard-deviation (SD) metrics. The characterized error metrics of the proposed system are the lowest to date when compared to other prior work. Clinical Relevance- A cuff-less BP estimation system allows patients to have easy access to blood pressure evaluation as well as aid in determining unsafe health ailments like hypertension. Ready access to such system will not only allow practitioners to continuously monitor BP in hospitals but also help patients to regularly monitor BP more frequently at their convenience.

A Review of Noninvasive Methodologies to Estimate the Blood Pressure Waveform

Accurate estimation of blood pressure (BP) waveforms is critical for ensuring the safety and proper care of patients in intensive care units (ICUs) and for intraoperative hemodynamic monitoring. Normal cuff-based BP measurements can only provide systolic blood pressure (SBP) and diastolic blood pressure (DBP). Alternatively, the BP waveform can be used to estimate a variety of other physiological parameters and provides additional information about the patient's health. As a result, various techniques are being proposed for accurately estimating the BP waveforms. The purpose of this review is to summarize the current state of knowledge regarding the BP waveform, three methodologies (pressure-based, ultrasound-based, and deep-learning-based) used in noninvasive BP waveform estimation research and the feasibility of employing these strategies at home as well as in ICUs. Additionally, this article will discuss the physical concepts underlying both invasive and noninvasive BP waveform measurements. We will review historical BP waveform measurements, standard clinical procedures, and more recent innovations in noninvasive BP waveform monitoring. Although the technique has not been validated, it is expected that precise, noninvasive BP waveform estimation will be available in the near future due to its enormous potential.

Multi-Dimensional Feature Combination Method for Continuous Blood Pressure Measurement Based on Wrist PPG Sensor

The cuffless blood pressure monitoring method based on photoplethysmogram (PPG) makes it possible for long-term blood pressure monitoring to prevent and treat cardiovascular and cerebrovascular events. However, the traditional feature extraction is based on two separate sensors, which is inconvenient. In a single sensor measurement, the prediction model based on a single feature group usually does not perform well. This paper presents an artificial neural network (ANN) model for predicting blood pressure based on feature combinations. The robustness of the model is improved from three aspects. Firstly, an adaptive peak extraction algorithm is used to improve the accuracy of peaks and troughs detection. Secondly, multi-dimensional features are extracted and fused, including three groups of PPG-based features and one group of demographics-based features. Finally, a two-layer feedforward artificial neural networks algorithm is used for regression. Thirty-three subjects distributed in three blood pressure groups were recruited. The proposed method passes the European Society of Hypertension International Protocol revision 2010 (ESP-IP2). Experimental results show that the proposed method exhibits good accuracy for a diverse population with an estimation error of 0.03 4.27 mmHg for SBP and 0.01 3.38 for DBP. Moreover, the model can track blood pressure in a long-term range, which demonstrates the robustness of the algorithm. This work will contribute to the long-term wellness management and rehabilitation process, enabling timely detection and improvement of the user's physical health.

Association of blood pressure with brain structure in youth with and without bipolar disorder

BACKGROUND

We previously found that blood pressure (BP) is elevated, and associated with poorer neurocognition, in youth with bipolar disorder (BD). While higher BP is associated with smaller brain structure in adults, studies have not examined this topic in BD or youth.

METHODS

Participants were 154 youth, ages 13-20 (n = 81 BD, n = 73 HC). Structural magnetic resonance imaging and diastolic (DBP), and systolic (SBP) pressure were obtained. Region of interest (ROI; anterior cingulate cortex [ACC], insular cortex, hippocampus) and vertex-wise analyses controlling for age, sex, body-mass-index, and intracranial volume investigated BP-neurostructural associations; a group-by-BP interaction was also assessed.

RESULTS

In ROI analyses, higher DBP in the overall sample was associated with smaller insular cortex area (β=-0.18 p = 0.007) and was associated with smaller ACC area to a significantly greater extent in HC vs. BD (β=-0.14 p = 0.015). In vertex-wise analyses, higher DBP and SBP were associated with smaller area and volume in the insular cortex, frontal, parietal, and temporal regions in the overall sample. Additionally, higher SBP was associated with greater thickness in temporal and parietal regions. Finally, higher SBP was associated with smaller area and volume in frontal, parietal, and temporal regions to a significantly greater extent in BD vs. HC.

LIMITATIONS

Cross-sectional design, single assessment of BP.

CONCLUSION

BP is associated with brain structure in youth, with variability related to structural phenotype (volume vs. thickness) and psychiatric diagnosis (BD vs. HC). Future studies evaluating temporality of these findings, and the association of BP changes on brain structure in youth, are warranted.

Cuffless Blood Pressure Estimation Based on Monte Carlo Simulation Using Photoplethysmography Signals

Blood pressure measurements are one of the most routinely performed medical tests globally. Blood pressure is an important metric since it provides information that can be used to diagnose several vascular diseases. Conventional blood pressure measurement systems use cuff-based devices to measure the blood pressure, which may be uncomfortable and sometimes burdensome to the subjects. Therefore, in this study, we propose a cuffless blood pressure estimation model based on Monte Carlo simulation (MCS). We propose a heterogeneous finger model for the MCS at wavelengths of 905 nm and 940 nm. After recording the photon intensities from the MCS over a certain range of blood pressure values, the actual photoplethysmography (PPG) signals were used to estimate blood pressure. We used both publicly available and self-made datasets to evaluate the performance of the proposed model. In case of the publicly available dataset for transmission-type MCS, the mean absolute errors are 3.32 ± 6.03 mmHg for systolic blood pressure (SBP), 2.02 ± 2.64 mmHg for diastolic blood pressure (DBP), and 1.76 ± 2.8 mmHg for mean arterial pressure (MAP). The self-made dataset is used for both transmission- and reflection-type MCSs; its mean absolute errors are 2.54 ± 4.24 mmHg for SBP, 1.49 ± 2.82 mmHg for DBP, and 1.51 ± 2.41 mmHg for MAP in the transmission-type case as well as 3.35 ± 5.06 mmHg for SBP, 2.07 ± 2.83 mmHg for DBP, and 2.12 ± 2.83 mmHg for MAP in the reflection-type case. The estimated results of the SBP and DBP satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standards and are within Grade A according to the British Hypertension Society (BHS) standards. These results show that the proposed model is efficient for estimating blood pressures using fingertip PPG signals.

Comparing Central Aortic Pressures Obtained Using a SphygmoCor Device to Pressures Obtained Using a Pressure Catheter

BACKGROUND

This study compared aortic pressures estimated using a SphygmoCor XCEL PWA device (ATCOR, Naperville, IL) noninvasively with aortic pressures obtained using pressure catheters during catheterization procedures and analyzed the impact of a linear-fit function on the estimated pressure values.

METHODS

One hundred and thirty-six patients scheduled for cardiac catheterization procedure were enrolled in IRB approved studies. Catheterization procedures were performed according to standard-of-care to acquire aortic pressure measurements. Immediately after the catheterization procedure with the pressure catheters removed, while the patients were still in the catheterization laboratory, central aortic pressures were estimated with the SphygmoCor device (using its inbuilt transfer function). The error between measured and estimated aortic pressures was evaluated using Bland-Altman analysis (n = 93). A linear-fit was performed between the measured and estimated pressures, and using the linear equation the error measurements were repeated. A bootstrap analysis was performed to test the generalizability of the linear-fit function. In a subset of cases (n = 13), central aortic pressure values were also obtained using solid-state high-fidelity catheters (Millar, Houston, TX), and the error measurements were repeated.

RESULTS

The magnitude of errors between the measured and estimated aortic pressures (mean errors >6.4 mm Hg; mean errors >8.0 mm Hg in the subset) were reduced to less than 1 mm Hg after using the linear-fit function derived in this study.

CONCLUSIONS

For the population examined in this study, the SphygmoCor data must be used with the linear-fit function to obtain aortic pressures that are comparable to the measurements obtained using pressure catheters.

CLINICAL TRIALS REGISTRATION

Trial Numbers NCT03243942 and NCT03245255.

Metabolic syndrome in middle-aged and older women: A cross-sectional study

Objective:

This study aims to compare the prevalence of metabolic syndrome between different age groups of middle-aged and older women and to assess whether these differences are independent of potential covariates.

Methods:

Study conducted with 510 women divided into three age groups: 45–54, 55–64 and 65–74 years. Socioeconomic, reproductive and lifestyle variables were self-reported. We defined metabolic syndrome using the National Cholesterol Education Program Adult Treatment Panel III criteria (abdominal obesity, diabetes, reduced high-density lipoprotein, elevated triglycerides, and hypertension). Logistic regression assessed the association between age groups, and metabolic syndrome was adjusted for covariates (socioeconomic variables, age at menarche and at first childbirth, parity, menopausal status, physical activity variables and smoking).

Results:

Women aged 55–64 years presented higher prevalence of all metabolic syndrome criteria than the other groups, except for abdominal obesity, which was higher in the oldest group. In the fully adjusted analysis, the 55–64 years age group continues to exhibit significantly higher odds of presenting metabolic syndrome when compared to the youngest group (45–54 years) (OR = 2.257; 95% CI = 1.20:4.24). There was no statistical difference in the odds of presenting metabolic syndrome when comparing the oldest and the youngest groups (OR = 1.500; 95% CI = 0.85:2.65).

Conclusion:

The higher prevalence of metabolic syndrome among those aged 55–64 years may indicate that middle-aged women become unhealthy earlier in the life course and that many of them may die prematurely. This result highlights the importance of screening metabolic syndrome earlier in the midlife and the need for public health policies aimed at reducing adverse effects in later years.

Physical Activity, Sport Practice and Cardiovascular Risk Factors in Workers From a Public Hospital: A Trajectory Analysis

Background: Studies have demonstrated the positive effects of physical activity on cardiovascular risk factors. Longitudinal studies using modeled trajectories are necessary to understand patterns of physical activity and association with cardiovascular risk factors. Objective: To analyze the association between sports practice in young people and current physical activity with the trajectory of cardiovascular risk factors in workers at a public hospital. Methods: Four hundred and seventeen workers was followed for four years reporting Physical Activity, health status, lifestyle behaviors and socio-demographic characteristics. Group-based trajectory modeling identified the trajectories of PA and associations with time-stable and time-varying covariates. We considered a range of sociodemographic and health and lifestyle factors as potential covariates. Results: The results shows the association between participation in sports activities in youth and current physical activity and trajectories of cardiovascular risk, adjusted for sex and age (p < 0.05). Adults who reported having played sports in their youth and are currently active have a lower risk of having a history of obesity and low HDL-c than workers who did not play sports in their youth and are currently sedentary 0.690 (0.565-0.844) obesity, 0.647 (0.500-0.837) low HDL-c. Conclusion: The practice of sports in youth and current physical activity is a protective factor against the trajectory of obesity and low HDL-c, mainly in female workers. Programs to encourage the practice of physical activity should be carried out in order to reduce cardiovascular risk factors and prevent chronic diseases in workers.

Wireless, Skin-Interfaced Devices for Pediatric Critical Care: Application to Continuous, Noninvasive Blood Pressure Monitoring

Indwelling arterial lines, the clinical gold standard for continuous blood pressure (BP) monitoring in the pediatric intensive care unit (PICU), have significant drawbacks due to their invasive nature, ischemic risk, and impediment to natural body movement. A noninvasive, wireless, and accurate alternative would greatly improve the quality of patient care. Recently introduced classes of wireless, skin-interfaced devices offer capabilities in continuous, precise monitoring of physiologic waveforms and vital signs in pediatric and neonatal patients, but have not yet been employed for continuous tracking of systolic and diastolic BP-critical for guiding clinical decision-making in the PICU. The results presented here focus on materials and mechanics that optimize the system-level properties of these devices to enhance their reliable use in this context, achieving full compatibility with the range of body sizes, skin types, and sterilization schemes typically encountered in the PICU. Systematic analysis of the data from these devices on 23 pediatric patients, yields derived, noninvasive BP values that can be quantitatively validated against direct recordings from arterial lines. The results from this diverse cohort, including those under pharmacological protocols, suggest that wireless, skin-interfaced devices can, in certain circumstances of practical utility, accurately and continuously monitor BP in the PICU patient population.

Patient preferences for ambulatory blood pressure monitoring devices: Wrist-type or arm-type?

Background

Ambulatory blood pressure monitoring (ABPM) is important in evaluating average 24-hour blood pressure (BP) levels, circadian rhythm, sleeping BP and BP variability but many patients are reluctant to use standard ABPM devices.

Methods

We compared two validated ABPM devices, the BPro tonometric wrist monitor and the A&D TM-2430 oscillometric upper arm monitor, for agreement of recordings and acceptability in 37 hypertensive patients (aged 55±9 years).

Results

Successful BP measurements were less frequent with the wrist-type than the arm-type device during the sleeping (66.3% vs. 92.9%, P <0.01) and awake periods (56.2% vs. 86.5%, P <0.01). Comparable paired readings showed no significant difference in systolic BP but diastolic BP (DBP) values were higher with the wrist compared to the arm monitor (24-hour 89±13 vs. 85±14 mmHg, P <0.01) with similar differences awake and sleeping. Bland-Altman analysis showed some large discrepancies between individual arm and wrist monitor measurements. More patients found the wrist monitor more comfortable to use than the arm monitor.

Conclusions

Despite the difference in individual BP measurements and the systematic overestimation of DBP values with the BPro device, wrist monitors with good patient acceptability may be useful to facilitate ABPM in some patients to provide additional information about cardiovascular risk and response to antihypertensive therapies.

Combined deep CNN-LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features

The pulse arrival time (PAT), the difference between the R-peak time of electrocardiogram (ECG) signal and the systolic peak of photoplethysmography (PPG) signal, is an indicator that enables noninvasive and continuous blood pressure estimation. However, it is difficult to accurately measure PAT from ECG and PPG signals because they have inconsistent shapes owing to patient-specific physical characteristics, pathological conditions, and movements. Accordingly, complex preprocessing is required to estimate blood pressure based on PAT. In this paper, as an alternative solution, we propose a noninvasive continuous algorithm using the difference between ECG and PPG as a new feature that can include PAT information. The proposed algorithm is a deep CNN–LSTM-based multitasking machine learning model that outputs simultaneous prediction results of systolic (SBP) and diastolic blood pressures (DBP). We used a total of 48 patients on the PhysioNet website by splitting them into 38 patients for training and 10 patients for testing. The prediction accuracies of SBP and DBP were 0.0 ± 1.6 mmHg and 0.2 ± 1.3 mmHg, respectively. Even though the proposed model was assessed with only 10 patients, this result was satisfied with three guidelines, which are the BHS, AAMI, and IEEE standards for blood pressure measurement devices.

Comparison of a tonometric with an oscillometric blood pressure monitoring device over 24 hours of ambulatory use

PURPOSE

Multiple devices capable of measuring ambulatory blood pressure without cuffs have been recently developed and it is required that they offer high-accuracy measurements. The purpose of this prospective study was to compare the performance of a tonometric blood pressure monitor with that of an oscillometric cuff-based device used as a reference in healthy and hypertensive subjects over 24 hours of ambulatory use.

MATERIALS AND METHODS

Conventional oscillometric cuff-based device (Oscar 2; Sun Tech Medical) was placed in the left arm of 33 subjects, and a watch-type device based on the tonometric method (Bpro; HealthSTATS International, Singapore) was positioned in the right wrist. Both devices were synchronized to measure simultaneously over 24 hours.

RESULTS

The difference between the means over 24 hours of the oscillometric and the tonometric devices was -0.9 mmHg for SBP and -4.5 mmHg for DBP; the standard deviations were 14.7 and 12.2 mmHg, respectively. Greater differences in bias and dispersion were observed overnight than during the daytime. The accuracy of the tonometric device for diagnosing hypertension was 75% and for detecting the non-dipper profile, 48%.

CONCLUSION

The test device presented a high disagreement (especially during the night) compared to the oscillometric cuff-based device against which it was initially calibrated. This disagreement resulted in limited accuracy for diagnosing patients with suspected arterial hypertension and detecting non-dipper profiles.

Effect of Elevated Ambient Temperature on Simulator-Derived Oscillometric Blood Pressure Measurement

BACKGROUND

Oscillometric blood pressure (BP) devices are typically labeled for use up to 40 °C. Many geographic regions have ambient temperatures exceeding 40 °C. We assessed the effect of increased ambient temperature (40–55 °C) on simulator-derived oscillometric BP measurement.

METHODS

Three Omron BP769CAN devices, 3 A&D Medical UA-651BLE devices, and accompanying cuffs were used. A custom heat chamber heated each device to the specified temperature. A noninvasive BP simulator was used to take 3 measurements with each device at differing temperatures (22, 40, 45, 50, and 55 °C) and BP thresholds: 80/50, 100/60, 120/80, 140/90, 160/110, and 180/130 mm Hg. Using each device as its own control (22 °C), we determined the relative differences in mean BP for each device at each temperature and BP setting, assessed graphical trends with increasing temperature, and examined variability.

RESULTS

Graphical trends of mean simulator-subtracted BP differences from room temperature showed no discernable pattern, with differences clustered around zero. Overall mean difference in BP (combined elevated temperatures minus room temperature) was −0.8 ± 2.1 (systolic ± SD)/1.2 ± 3.5 (diastolic ± SD) mm Hg for the A&D device and 0.2 ± 0.4 (systolic ± SD)/−0.1 ± 0.1 (diastolic ± SD) mm Hg for the Omron. All individual elevated temperature differences (elevated temperature minus room temperature) except A&D diastolic BP at 50 °C were within 5 mm Hg.

CONCLUSIONS

In this simulator-based study assessing within-device differences, higher ambient temperatures resulted in oscillometric BP measurements that were comparable to those performed at room temperature.

An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach

Blood pressure (BP) monitoring has significant importance in the treatment of hypertension and different cardiovascular health diseases. As photoplethysmogram (PPG) signals can be recorded non-invasively, research has been highly conducted to measure BP using PPG recently. In this paper, we propose a U-net deep learning architecture that uses fingertip PPG signal as input to estimate arterial BP (ABP) waveform non-invasively. From this waveform, we have also measured systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP). The proposed method was evaluated on a subset of 100 subjects from two publicly available databases: MIMIC and MIMIC-III. The predicted ABP waveforms correlated highly with the reference waveforms and we have obtained an average Pearson's correlation coefficient of 0.993. The mean absolute error is 3.68 ± 4.42 mmHg for SBP, 1.97 ± 2.92 mmHg for DBP, and 2.17 ± 3.06 mmHg for MAP which satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed method is an efficient process to estimate ABP waveform directly using fingertip PPG.

Goal-directed fluid therapy in autologous breast reconstruction results in less fluid and more vasopressor administration without outcome compromise

OBJECTIVE

Aggressive or restricted perioperative fluid management has been shown to increase complications in patients undergoing microsurgery. Goal-directed fluid therapy (GDFT) aims to administer fluid, vasoactive agents, and inotropes according to each patient's hemodynamic indices. This study assesses GDFT impact on perioperative outcomes of autologous breast reconstruction (ABR) patients, as there remains a gap in management understanding. We hypothesize that GDFT will have lower fluid administration and equivocal outcomes compared to patients not on GDFT.

METHODS

A single-center retrospective review was conducted on ABR patients from January 2010-April 2017. An enhanced recovery after surgery (ERAS) using GDFT was implemented in April 2015. With GDFT, patients were administered intraoperative fluids and vasoactive agents according to hemodynamic indices. Patients prior to April 2015 were included in the pre-ERAS cohort. Primary outcomes included the amount and rate of fluid delivery, urine output (UOP), vasopressor administration, major (i.e., flap failure) and minor (i.e., seroma) complications, and length of stay (LOS).

RESULTS

Overall, 777 patients underwent ABR (ERAS: 312 and pre-ERAS: 465). ERAS patients received significantly less total fluid volume (ERAS median: 3750 mL [IQR: 3000-4500 mL]; pre-ERAS median: 5000 mL [IQR 4000-6400 mL]; and p<0.001), had lower UOP, were more likely to receive vasopressor agents (47% vs 35% and p<0.001), and had lower LOS (ERAS: 4 days [4-5]; pre-ERAS: 5 [4-6]; and p<0.001) as compared to pre-ERAS patients. Complications did not differ between cohorts.

CONCLUSIONS

GDFT, as part of ERAS, and the prudent use of vasopressors were found to be safe and did not increase morbidity in ABR patients. GDFT provides individualized perioperative care to the ABR patient.

Artificial Intelligence Based Blood Pressure Estimation From Auscultatory and Oscillometric Waveforms: A Methodological Review

Cardiovascular disease is the number one cause of death globally, with elevated blood pressure (BP) being the single largest risk factor. Hence, BP is an important physiological parameter used as an indicator of cardiovascular health. The use of automated non-invasive blood pressure (NIBP) measurement devices is growing, as measurements can be taken by patients at home. While the oscillometric technique is most common, some automated NIBP measurement methods have been developed based on the auscultatory technique. By utilizing (relatively) large BP data annotated by experts, models can be trained using machine learning and statistical concepts to develop novel NIBP estimation algorithms. Amongst artificial intelligence (AI) techniques, deep learning has received increasing attention in different fields due to its strength in data classification and feature extraction problems. This paper reviews AI-based BP estimation methods with a focus on recent advances in deep learning-based approaches within the field. Various architectures and methodologies proposed todate are discussed to clarify their strengths and weaknesses. Based on the literature reviewed, deep learning brings plausible benefits to the field of BP estimation. We also discuss some limitations which can hinder the widespread adoption of deep learning in the field and suggest frameworks to overcome these challenges.

Non-Invasive Risk Stratification of Hypertension: A Systematic Comparison of Machine Learning Algorithms

One of the most important physiological parameters of the cardiovascular circulatory system is Blood Pressure. Several diseases are related to long-term abnormal blood pressure, i.e., hypertension; therefore, the early detection and assessment of this condition are crucial. The identification of hypertension, and, even more the evaluation of its risk stratification, by using wearable monitoring devices are now more realistic thanks to the advancements in Internet of Things, the improvements of digital sensors that are becoming more and more miniaturized, and the development of new signal processing and machine learning algorithms. In this scenario, a suitable biomedical signal is represented by the PhotoPlethysmoGraphy (PPG) signal. It can be acquired by using a simple, cheap, and wearable device, and can be used to evaluate several aspects of the cardiovascular system, e.g., the detection of abnormal heart rate, respiration rate, blood pressure, oxygen saturation, and so on. In this paper, we take into account the Cuff-Less Blood Pressure Estimation Data Set that contains, among others, PPG signals coming from a set of subjects, as well as the Blood Pressure values of the latter that is the hypertension level. Our aim is to investigate whether or not machine learning methods applied to these PPG signals can provide better results for the non-invasive classification and evaluation of subjects’ hypertension levels. To this aim, we have availed ourselves of a wide set of machine learning algorithms, based on different learning mechanisms, and have compared their results in terms of the effectiveness of the classification obtained.

Development of intelligent healthcare system based on ambulatory blood pressure measuring device

Currently, the market size of blood pressure monitors both in domestic and overseas is gradually increasing due to the increase in hypertension patients resulting from aging population. In addition, the necessity of developing systems and devices for the healthcare of hypertension patients is also increasing. Moreover, the determination of health normality in respect to the management of hypertension patients is possible, but it is essentially important to incorporate preventive healthcare. Thus, further studies on deep learning-based prediction technology using previous data are needed. This paper proposes the development of an intelligent healthcare management system that can help to manage the health of hypertensive patients. The system includes a wrist-worn ambulatory blood pressure monitoring device that can analyze the normality of measured blood pressures. The performance evaluation results of the proposed system verified the reliability of data acquisition as compared with the existing equipment as well as the efficiency of the intelligent healthcare system.

Monitoring of cardiovascular physiology augmented by a patient-specific biomechanical model during general anesthesia. A proof of concept study

During general anesthesia (GA), direct analysis of arterial pressure or aortic flow waveforms may be inconclusive in complex situations. Patient-specific biomechanical models, based on data obtained during GA and capable to perform fast simulations of cardiac cycles, have the potential to augment hemodynamic monitoring. Such models allow to simulate Pressure-Volume (PV) loops and estimate functional indicators of cardiovascular (CV) system, e.g. ventricular-arterial coupling (Vva), cardiac efficiency (CE) or myocardial contractility, evolving throughout GA. In this prospective observational study, we created patient-specific biomechanical models of heart and vasculature of a reduced geometric complexity for n = 45 patients undergoing GA, while using transthoracic echocardiography and aortic pressure and flow signals acquired in the beginning of GA (baseline condition). If intraoperative hypotension (IOH) appeared, diluted norepinephrine (NOR) was administered and the model readjusted according to the measured aortic pressure and flow signals. Such patients were a posteriori assigned into a so-called hypotensive group. The accuracy of simulated mean aortic pressure (MAP) and stroke volume (SV) at baseline were in accordance with the guidelines for the validation of new devices or reference measurement methods in all patients. After NOR administration in the hypotensive group, the percentage of concordance with 10% exclusion zone between measurement and simulation was >95% for both MAP and SV. The modeling results showed a decreased Vva (0.64±0.37 vs 0.88±0.43; p = 0.039) and an increased CE (0.8±0.1 vs 0.73±0.11; p = 0.042) in hypotensive vs normotensive patients. Furthermore, Vva increased by 92±101%, CE decreased by 13±11% (p < 0.001 for both) and contractility increased by 14±11% (p = 0.002) in the hypotensive group post-NOR administration. In this work we demonstrated the application of fast-running patient-specific biophysical models to estimate PV loops and functional indicators of CV system using clinical data available during GA. The work paves the way for model-augmented hemodynamic monitoring at operating theatres or intensive care units to enhance the information on patient-specific physiology.

Clinical applicability of the Feline Grimace Scale: real-time versus image scoring and the influence of sedation and surgery

Background

The Feline Grimace Scale (FGS) is a facial expression-based scoring system for acute pain assessment in cats with reported validity using image assessment. The aims of this study were to investigate the clinical applicability of the FGS in real-time when compared with image assessment, and to evaluate the influence of sedation and surgery on FGS scores in cats.

Methods

Sixty-five female cats (age: 1.37 ± 0.9 years and body weight: 2.85 ± 0.76 kg) were included in a prospective, randomized, clinical trial. Cats were sedated with intramuscular acepromazine and buprenorphine. Following induction with propofol, anesthesia was maintained with isoflurane and cats underwent ovariohysterectomy (OVH). Pain was evaluated at baseline, 15 min after sedation, and at 0.5, 1, 2, 3, 4, 6, 8, 12 and 24 h after extubation using the FGS in real-time (FGS-RT). Cats were video-recorded simultaneously at baseline, 15 min after sedation, and at 2, 6, 12, and 24 h after extubation for subsequent image assessment (FGS-IMG), which was performed six months later by the same observer. The agreement between FGS-RT and FGS-IMG scores was calculated using the Bland & Altman method for repeated measures. The effects of sedation (baseline versus 15 min) and OVH (baseline versus 24 h) were assessed using linear mixed models. Responsiveness to the administration of rescue analgesia (FGS scores before versus one hour after) was assessed using paired t-tests.

Results

Minimal bias (-0.057) and narrow limits of agreement (-0.351 to 0.237) were observed between the FGS-IMG and FGS-RT. Scores at baseline (FGS-RT: 0.16 ± 0.13 and FGS-IMG: 0.14 ± 0.13) were not different after sedation (FGS-RT: 0.2 ± 0.15, p = 0.39 and FGS-IMG: 0.16 ± 0.15, p = 0.99) nor at 24 h after extubation (FGS-RT: 0.16 ± 0.12, p = 0.99 and FGS-IMG: 0.12 ± 0.12, p = 0.96). Thirteen cats required rescue analgesia; their FGS scores were lower one hour after analgesic administration (FGS-RT: 0.21 ± 0.18 and FGS-IMG: 0.18 ± 0.17) than before (FGS-RT: 0.47 ± 0.24, p = 0.0005 and FGS-IMG: 0.45 ± 0.19, p = 0.015).

Conclusions

Real-time assessment slightly overestimates image scoring; however, with minimal clinical impact. Sedation with acepromazine-buprenorphine and ovariohysterectomy using a balanced anesthetic protocol did not influence the FGS scores. Responsiveness to analgesic administration was observed with both the FGS-RT and FGS-IMG.

Cuffless Single-Site Photoplethysmography for Blood Pressure Monitoring

One in three adults worldwide has hypertension, which is associated with significant morbidity and mortality. Consequently, there is a global demand for continuous and non-invasive blood pressure (BP) measurements that are convenient, easy to use, and more accurate than the currently available methods for detecting hypertension. This could easily be achieved through the integration of single-site photoplethysmography (PPG) readings into wearable devices, although improved reliability and an understanding of BP estimation accuracy are essential. This review paper focuses on understanding the features of PPG associated with BP and examines the development of this technology over the 2010-2019 period in terms of validation, sample size, diversity of subjects, and datasets used. Challenges and opportunities to move single-site PPG forward are also discussed.

Wearable Piezoelectric-Based System for Continuous Beat-to-Beat Blood Pressure Measurement

Non-invasive continuous blood pressure measurement is an emerging issue that potentially can be applied to cardiovascular disease monitoring and prediction. Recently, many groups have proposed the pulse transition time (PTT) method to estimate blood pressure for long-term monitoring. However, the PTT-based methods for blood pressure estimation are limited by non-specific estimation models and require multiple calibrations. This study aims to develop a low-cost wearable piezoelectric-based system for continuous beat-to-beat blood pressure measurement. The pressure change in the radial artery was extracted by systolic and diastolic feature points in pressure pulse wave (PPW) and the pressure sensitivity of the sensor. The proposed system showed a reliable accuracy of systolic blood pressure (SBP) (mean absolute error (MAE) ± standard deviation (SD) 1.52 ± 0.30 mmHg) and diastolic blood pressure (DBP, MAE ± SD 1.83 ± 0.50), and its performance agreed with standard criteria of MAE within 5 mmHg and SD within ±8 mmHg. In conclusion, this study successfully developed a low-cost, high-accuracy piezoelectric-based system for continuous beat-to-beat SBP and DBP measurement without multiple calibrations and complex regression analysis. The system is potentially suitable for continuous, long-term blood pressure-monitoring applications.

Central arterial and peripheral arterial blood pressure in patients with chronic kidney disease undergoing versus not undergoing hemodialysis

Objective

We assessed the consistency of noninvasive and invasive measurements of central arterial pressure (CAP) and the difference between peripheral brachial artery pressure and CAP in patients with chronic kidney disease (CKD) undergoing versus not undergoing hemodialysis.

Methods

This single-center cross-sectional study was performed from May to December 2018. The patients were divided into a control group (n = 50), CKD group (stages 3–5, n = 50), and dialysis group (n = 20), and all underwent measurement of peripheral humeral arterial pressure and noninvasive and invasive measurement of CAP. Group differences and correlations between CAP and peripheral arterial pressure were assessed.

Results

The consistency between noninvasive and invasive CAP was better in the control and CKD groups than in the dialysis group. In the dialysis group, the noninvasive equipment underestimated the actual CAP. The CAP was close to the peripheral brachial artery pressure in the dialysis group, while the CAP was significantly lower than the peripheral brachial artery pressure in the control and CKD groups.

Conclusion

Noninvasive equipment underestimates the actual CAP in patients undergoing dialysis and should be used with caution. The difference between the peripheral arterial pressure and CAP was smaller in patients undergoing dialysis than in patients with CKD and controls.

Validation of the modified Microlife blood pressure monitor in patients with paroxysmal atrial fibrillation

AIMS

Undiagnosed atrial fibrillation (AF) accounts for 6% of all strokes, therefore early detection and treatment of the arrhythmia are paramount. Previous research has illustrated that the Microlife WatchBPO3 AFIB, an automated blood pressure (BP) monitor with an inbuilt AF algorithm, accurately detects permanent AF. Currently, limited data exist on whether the modified BP monitor is able to detect paroxysmal AF (PAF). Therefore, this study aims to assess the accuracy of the Microlife WatchBPO3 AFIB monitor to detect PAF against a pacemaker reference standard over a 24-h period.

METHODS AND RESULTS

Forty-eight patients with a pacemaker implanted for sick sinus syndrome and previously documented fast AF participated. Sensitivity of the atrial pacemaker lead was set to allow detection of signals of ≥ 0.5 mV. Patients engaged in their normal daily routine whilst wearing the modified BP monitor. The modified BP monitor demonstrated an overall sensitivity of 76.0% and specificity of 80.8% for detecting PAF. This sensitivity and specificity increased to 100% and 83.1%, respectively, for patients that achieved more than 80% successful BP readings. Compared to day-time readings, night-time readings also demonstrated a lower proportion of movement artefact (14.4% vs. 3.4%), and therefore, a higher sensitivity and specificity of 100% and 84.9%, respectively, for detecting PAF.

CONCLUSION

The Microlife WatchBPO3 AFIB device has an acceptable diagnostic accuracy to detect PAF; however, movement artefact affects the accuracy of the readings. This modified BP monitor may potentially be useful as a screening tool for AF in patients at high risk of developing stroke.

What is the most important determinant of cardiometabolic risk in 60-65-year-old subjects: physical activity-related behaviours, overall energy expenditure or occupational status? A cross-sectional study in three populations with different employment status in Poland

OBJECTIVES

The aim of the study was to determine whether cardiovascular risk factors may differ according to occupational status and whether physical activity related to total energy expenditure (PA-EE) and related to health-related behaviours (PA-HRB) is associated with common cardiovascular risk factors or metabolic syndrome in pre-elderly subjects.

METHODS

Three hundred subjects aged 60-65 were recruited and divided into three equal groups of white-collar, blue-collar workers and unemployed subjects; 50% were women. The subjects were tested for major cardiovascular risk factors such as smoking, anthropometric indices, blood pressure, lipid levels, glucose, uric acid and homocysteine. PA-EE and PA-HRB were assessed with PA questionnaires.

RESULTS

Blue-collar workers displayed higher anthropometric indices, blood pressure and higher PA-EE in comparison with other two groups. PA-HRB had a positive impact on body mass indices, lipids, glucose, uric acid and the prevalence of metabolic syndrome, with no such relationship observed for PA-EE.

CONCLUSIONS

The greatest cardiovascular risk was observed in the blue-collar workers group. Only PA-HRB had a positive association with cardiometabolic risk profile. No relationship was observed for PA-EE. Thus, promoting everyday life and leisure time PA behaviours is crucial for preventing cardiometabolic risk in pre-elderly subjects, even in blue-collar workers with high work-related EE.

Photoplethysmography-Based Continuous Systolic Blood Pressure Estimation Method for Low Processing Power Wearable Devices

Regardless of age, it is always important to detect deviations in long-term blood pressure from normal levels. Continuous monitoring of blood pressure throughout the day is even more important for elderly people with cardiovascular diseases or a high risk of stroke. The traditional cuff-based method for blood pressure measurements is not suitable for continuous real-time applications and is very uncomfortable. To address this problem, continuous blood pressure measurement methods based on photoplethysmogram (PPG) have been developed. However, these methods use specialized high-performance hardware and sensors, which are not available for common users. This paper proposes the continuous systolic blood pressure (SBP) estimation method based on PPG pulse wave steepness for low processing power wearable devices and evaluates its suitability using the commercially available CMS50FW Pulse Oximeter. The SBP estimation is done based on the PPG pulse wave steepness (rising edge angle) because it is highly correlated with systolic blood pressure. The SBP estimation based on this single feature allows us to significantly reduce the amount of data processed and avoid errors, due to PPG pulse wave amplitude changes resulting from physiological or external factors. The experimental evaluation shows that the proposed SBP estimation method allows the use of off-the-shelf wearable PPG measurement devices with a low sampling rate (up to 60 Hz) and low resolution (up to 8-bit) for precise SBP measurements (mean difference MD = −0.043 and standard deviation SD = 6.79). In contrast, the known methods for continuous SBP estimation are based on equipment with a much higher sampling rate and better resolution characteristics.

Pulse wave analysis using the Mobil-O-Graph, Arteriograph and Complior device: a comparative study

PURPOSE

Pulse wave velocity (PWV) is a marker of arterial stiffness with major prognostic value. We compared Arteriograph and Complior devices with the Mobil-O-Graph for assessment of PWV and central systolic blood pressure (cSBP).

MATERIALS AND METHODS

We studied 316 consecutive subjects (age: 55 ± 14 years). For each individual, we measured PWV and cSBP with Arteriograph, Complior and Mobil-O-Graph and compared the readings. Differences in values among three devices were calculated for each measurement. We used Bland-Altman analysis, intraclass correlation coefficient (ICC) and coefficient of variation (CV).

RESULTS

Bland-Altman analysis indicated a mean difference for PWV: i.0.5 m/s (limits of agreement -1.4-2.4) between Complior and Mobil-O-Graph, ii.0.6 m/s (limits of agreement -1.4-2.6) between Arteriograph and Mobil-O-Graph. cSBP mean difference was 3.8 mmHg between Complior and Mobil-O-Graph (limits of agreement -12.5-20.1) and 9.2 mmHg between Arteriograph and Mobil-O-Graph (limits of agreement -7.6-26). ICC for PWV was 0.86 between Arteriograph and Mobil-O-Graph, 0.87 between Complior and Mobil-O-Graph and for cSBP 0.92 and 0.91 respectively. CV for PWV was 9.5% between Arteriograph and Mobil-O-Graph, 8.8% between Complior and Mobil-O-Graph. Respective values for cSBP were 6.8% and 5.1%.

CONCLUSION

Our study shows acceptable agreement among the three devices regarding pulse wave analysis markers though Mobil-O-Graph appears to underestimate the values of these markers. Further studies are needed to explore the agreement between the 3 devices in various clinical settings and patient populations.

In-Home Cardiovascular Monitoring System for Heart Failure: Comparative Study

Background

There is a pressing need to reduce the hospitalization rate of heart failure patients to limit rising health care costs and improve outcomes. Tracking physiologic changes to detect early deterioration in the home has the potential to reduce hospitalization rates through early intervention. However, classical approaches to in-home monitoring have had limited success, with patient adherence cited as a major barrier. This work presents a toilet seat–based cardiovascular monitoring system that has the potential to address low patient adherence as it does not require any change in habit or behavior.

Objective

The objective of this work was to demonstrate that a toilet seat–based cardiovascular monitoring system with an integrated electrocardiogram, ballistocardiogram, and photoplethysmogram is capable of clinical-grade measurements of systolic and diastolic blood pressure, stroke volume, and peripheral blood oxygenation.

Methods

The toilet seat–based estimates of blood pressure and peripheral blood oxygenation were compared to a hospital-grade vital signs monitor for 18 subjects over an 8-week period. The estimated stroke volume was validated on 38 normative subjects and 111 subjects undergoing a standard echocardiogram at a hospital clinic for any underlying condition, including heart failure.

Results

Clinical grade accuracy was achieved for all of the seat measurements when compared to their respective gold standards. The accuracy of diastolic blood pressure and systolic blood pressure is 1.2 (SD 6.0) mm Hg (N=112) and –2.7 (SD 6.6) mm Hg (N=89), respectively. Stroke volume has an accuracy of –2.5 (SD 15.5) mL (N=149) compared to an echocardiogram gold standard. Peripheral blood oxygenation had an RMS error of 2.3% (N=91).

Conclusions

A toilet seat–based cardiovascular monitoring system has been successfully demonstrated with blood pressure, stroke volume, and blood oxygenation accuracy consistent with gold standard measures. This system will be uniquely positioned to capture trend data in the home that has been previously unattainable. Demonstration of the clinical benefit of the technology requires additional algorithm development and future clinical trials, including those targeting a reduction in heart failure hospitalizations.

Association of Klotho gene polymorphism with hypertension and coronary artery disease in an Iranian population

Background

Klotho, possibly an age-regulating protein, is considered an important factor contributing to the lifespan and pathophysiology of hypertension and coronary artery disease (CAD). The present study was carried out aiming to investigate the association of Klotho-rs564481 (C1818T) gene polymorphism with hypertension and CAD.

Methods

A total of 286 CAD-suspicious subjects were entered into this case-control study. The polymorphism was investigated in hypertensive patients with no CAD (H-Tens, n = 60); hypertensive patients with CAD (CAD + H-Tens, n = 95); CAD patients with no hypertension (CAD, n = 61); and non-hypertensive non-CAD subjects, which were regarded as the control group (Ctrl, n = 70). Genotype and allele frequencies were assessed using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method.

Results

A significant difference was found in allele frequency of Klotho C1818T among the four research groups (P = 0.03). It was also found that wild-type homozygote subjects were negatively associated with hypertension as compared to heterozygote ones (OR = 0.07 [95% CI: 0.008–0.69] P = 0.02). Moreover, in the subgroups older than 57 years old, dominant genetic model demonstrated a negative association with CAD combined with hypertension (OR = 0.31 [95% CI: 0.10–0.95] P = 0.04).

Conclusions

In conclusion, Klotho C1818T variant may be associated with a decreased risk of hypertension. Moreover, aging enhanced positive effects of the Klotho polymorphism on CAD combined with hypertension, indicating the possibility that the KLOTHO gene might play a part in the age-related occurrence of CAD combined with hypertension.

Multiscale mathematical modeling vs. the generalized transfer function approach for aortic pressure estimation: a comparison with invasive data

We aimed to evaluate the performance of a mathematical model and currently available non-invasive techniques (generalized transfer function (GTF) method and brachial pressure) in the estimation of aortic pressure. We also aimed to investigate error dependence on brachial pressure errors, aorta-to-brachial pressure changes and demographic/clinical conditions. Sixty-two patients referred for invasive hemodynamic evaluation were consecutively recruited. Simultaneously, the registration of the aortic pressure using a fluid-filled catheter, brachial pressure and radial tonometric waveform was recorded. Accordingly, the GTF device and mathematical model were set. Radial invasive pressure was recorded soon after aortic measurement. The average invasive aortic pressure was 141.3 ± 20.2/76 ± 12.2 mm Hg. The simultaneous brachial pressure was 144 ± 17.8/81.5 ± 11.7 mm Hg. The GTF-based and model-based aortic pressure estimates were 133.1 ± 17.3/82.4 ± 12 and 137 ± 21.6/72.2 ± 16.7 mm Hg, respectively. The Bland-Altman plots showed a marked tendency to pressure overestimation for increasing absolute values, with the exclusion of mathematical model diastolic estimations. The systolic pressure was increased from the aortic to radial locations (7.5 ± 19 mm Hg), while the diastolic pressure was decreased (3.8 ± 9.8 mm Hg). The brachial pressure underestimated the systolic and overestimated diastolic intra-arterial radial pressure. GTF errors were independently correlated with the variability in pulse pressure amplification and with the brachial error. Errors of the mathematical model were related to only demographic and clinical conditions. Neither a multiscale mathematical model nor a generalized transfer function device substantially outperformed the oscillometric brachial pressure in the estimation of aortic pressure. Mathematical modeling should be improved by including further patient-specific conditions, while the variability in pulse pressure amplification may hamper the performance of the GTF method in patients at the risk of coronary artery disease.