Continuous Blood Pressure Measurement Research Articles

The interactive effects of posture and biological sex on the control of muscle sympathetic nerve activity during rhythmic handgrip exercise.

Body posture and biological sex exhibit independent effects on the sympathetic neural responses to dynamic exercise. However, the neural mechanisms (e.g., baroreflex) by which posture impacts sympathetic outflow during rhythmic muscular contractions and whether biological sex affects posture-mediated changes in efferent sympathetic nerve traffic during exercise remains unknown. Thus, we tested the hypotheses that increases in muscle sympathetic nerve activity (MSNA) would be greater during upright compared to supine rhythmic handgrip (RHG) exercise, and that females would demonstrate smaller increases in MSNA during upright RHG exercise than males. Twenty young (30 [6] years; mean [SD]) individuals (9 males, 11 females) underwent 6-minutes of supine and upright (head-up tilt 45°) RHG exercise at 40% maximal voluntary contraction with continuous measurements of MSNA (microneurography), blood pressure (photoplethysmography) and heart rate (electrocardiogram). In the pooled group, absolute MSNA burst frequency (P<0.001), amplitude (P=0.009), and total MSNA (P<0.001) were higher during upright compared to supine RHG exercise. However, body posture did not impact the peak change in MSNA during RHG exercise (range: P=0.063-0.495). Spontaneous sympathetic baroreflex gain decreased from rest to RHG exercise (P=0.006) and was not impacted by posture (P=0.347). During upright RHG exercise, males demonstrated larger increases in MSNA burst amplitude (P=0.002) and total MSNA (P=0.001) compared to females, that coincided with greater reductions in sympathetic baroreflex gain (P=0.004). Collectively, these data indicate that acute attenuation of baroreflex-mediated sympathoinhibition permits increases in MSNA during RHG exercise, and that males exhibit a greater reserve for efferent sympathetic neural recruitment during orthostasis than females.

A Wireless Noninvasive Blood Pressure Measurement System Using MAX30102 and Random Forest Regressor for Photoplethysmography Signals

Hypertension, often termed “the silent killer”, is associated with cardiovascular risk and requires regular blood pressure (BP) monitoring. However, existing methods are cumbersome and require medical expertise, which is worsened by the need for physical contact, particularly during situations such as the coronavirus pandemic that started in 2019 (COVID-19). This study aimed to develop a cuffless, continuous, and accurate BP measurement system using a photoplethysmography (PPG) sensor and a microcontroller via PPG signals. The system utilizes a MAX30102 sensor and ESP-WROOM-32 microcontroller to capture PPG signals that undergo noise reduction during preprocessing. Peak detection and feature extraction algorithms were introduced, and their output data were used to train a machine learning model for BP prediction. Tuning the model resulted in identifying the best-performing model when using a dataset from six subjects with a total of 114 records, thereby achieving a coefficient of determination of 0.37/0.46 and a mean absolute error value of 4.38/4.49 using the random forest algorithm. Integrating this model into a web-based graphical user interface enables its implementation. One probable limitation arises from the small sample size (six participants) of healthy young individuals under seated conditions, thereby potentially hindering the proposed model’s ability to learn and generalize patterns effectively. Increasing the number of participants with diverse ages and medical histories can enhance the accuracy of the proposed model. Nevertheless, this innovative device successfully addresses the need for convenient, remote BP monitoring, particularly during situations like the COVID-19 pandemic, thus making it a promising tool for cardiovascular health management.

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.

AcumenTM hypotension prediction index guidance for prevention and treatment of hypotension in noncardiac surgery: a prospective, single-arm, multicenter trial

BackgroundIntraoperative hypotension is common during noncardiac surgery and is associated with postoperative myocardial infarction, acute kidney injury, stroke, and severe infection. The Hypotension Prediction Index software is an algorithm based on arterial waveform analysis that alerts clinicians of the patient’s likelihood of experiencing a future hypotensive event, defined as mean arterial pressure < 65 mmHg for at least 1 min.MethodsTwo analyses included (1) a prospective, single-arm trial, with continuous blood pressure measurements from study monitors, compared to a historical comparison cohort. (2) A post hoc analysis of a subset of trial participants versus a propensity score-weighted contemporaneous comparison group, using external data from the Multicenter Perioperative Outcomes Group (MPOG). The trial included 485 subjects in 11 sites; 406 were in the final effectiveness analysis. The post hoc analysis included 457 trial participants and 15,796 comparison patients. Patients were eligible if aged 18 years or older, American Society of Anesthesiologists (ASA) physical status 3 or 4, and scheduled for moderate- to high-risk noncardiac surgery expected to last at least 3 h. Measurements: minutes of mean arterial pressure (MAP) below 65 mmHg and area under MAP < 65 mmHg.ResultsAnalysis 1: Trial subjects (n = 406) experienced a mean of 9 ± 13 min of MAP below 65 mmHg, compared with the MPOG historical control mean of 25 ± 41 min, a 65% reduction (p < 0.001). Subjects with at least one episode of hypotension (n = 293) had a mean of 12 ± 14 min of MAP below 65 mmHg compared with the MPOG historical control mean of 28 ± 43 min, a 58% reduction (p< 0.001). Analysis 2: In the post hoc inverse probability treatment weighting model, patients in the trial demonstrated a 35% reduction in minutes of hypotension compared to a contemporaneous comparison group [exponentiated coefficient: − 0.35 (95%CI − 0.43, − 0.27); p < 0.001].ConclusionsThe use of prediction software for blood pressure management was associated with a clinically meaningful reduction in the duration of intraoperative hypotension. Further studies must investigate whether predictive algorithms to prevent hypotension can reduce adverse outcomes.Trial registrationClinical trial number: NCT03805217. Registry URL: https://clinicaltrials.gov/ct2/show/NCT03805217. Principal investigator: Xiaodong Bao, MD, PhD. Date of registration: January 15, 2019.

A continuous cuffless blood pressure measurement from optimal PPG characteristic features using machine learning algorithms

Background and objectiveHypertension 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. MethodsIn 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. ResultsThe 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. ConclusionThe 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.

Can Currently Available Non-invasive Continuous Blood Pressure Monitors Replace Invasive Measurement With an Arterial Catheter?

Deviations from normal blood pressure (BP) during general anesthesia have been clearly linked to several adverse outcomes. Measuring BP accurately is therefore critically important for producing excellent outcomes in health care. Normal BP does not necessarily guarantee adequate organ perfusion however and adverse events have occurred even when BP seemed adequate. Invasive blood pressure monitoring has recently evolved beyond merely measuring BP. Arterial line-derived pulse contour analysis is used now to assess both cardiac output and stroke volume variation as indices of adequate intravascular volume. Confirmation of acceptable cardiac output with data derived from invasive intra-arterial catheters has become very important when managing high-risk patients. Newer devices that measure BP continuously and non-invasively in the digital arteries via a finger cuff have also become available. Many clinicians contemplate now if these new devices are ready to replace invasive monitoring with an arterial catheter. Unlike non-invasive devices, intra-arterial catheters allow frequent blood sampling. This makes it possible to assess vital parameters like pH, hemoglobin concentration, ionized calcium, potassium, glucose, and arterial partial pressure of oxygen and carbon dioxide frequently. Non-invasive continuous BP measurement has been found to be unreliable in critically ill patients, the elderly, and patients with calcified arteries. Pulse contour-derived estimates of cardiac output and stroke volume variation have been validated better with data derived from arterial lines than that from the newer finger cuff monitors. Significant advances have been recently made with non-invasive continuous BP monitors. Invasive monitoring with an arterial line however remains the gold standard for measuring BP and assessing pulse contour analysis-derived hemodynamic variables in critically ill patients. In the future, non-invasive continuous BP monitors will likely replace intermittent oscillometers in the operating room and the postoperative period. They will however not eliminate the need for arterial catheterization in critically ill patients.

Single-Cycle Pulse Signal Recognition Based on One-Dimensional Deep Convolutional Neural Network

Pulse signals carry comprehensive information regarding human cardiovascular physiology and pathology, providing a noninvasive and continuous method to assess cardiovascular health status in blood pressure monitoring. The blood pressure measurement method based on the pulse signal needs to extract the features of the single-cycle pulse signal, while the pulse signal pertains to the weak physiological signal of body surface. The acquisition process is susceptible to various factors leading to abnormal cycles, especially adjacent channel interference, affecting the subsequent feature extraction. To address this problem, this paper conducts an analysis of the formation mechanism of adjacent channel interference and proposes a single-cycle pulse signal recognition algorithm based on a one-dimensional deep convolutional neural network (1D-CNN) model. Radial pulse signals were collected from 150 subjects by pulse bracelet, and a dataset comprising 3446 single-cycle signals was extracted in total after denoising, single-cycle segmentation, and standardized preprocessing. The 1D-CNN model is trained to classify input signals into three categories: effective pulse signals, distortion, and interference signals. This classification is achieved by evaluating the waveform morphology of the signals within a single cycle. The results show that the overall classification accuracy of the algorithm on the test set is 98.26%, in which the classification accuracy of pulse waves is 99.8%, indicating that it can effectively recognize single-cycle pulse waves, which lays the foundation for subsequent continuous blood pressure measurement.

Randomized open-label trial of semaglutide and dapagliflozin in patients with type 2 diabetes of different pathophysiology

The limited understanding of the heterogeneity in the treatment response to antidiabetic drugs contributes to metabolic deterioration and cardiovascular complications1,2, stressing the need for more personalized treatment1. Although recent attempts have been made to classify diabetes into subgroups, the utility of such stratification in predicting treatment response is unknown3. We enrolled participants with type 2 diabetes (n = 239, 74 women and 165 men) and features of severe insulin-deficient diabetes (SIDD) or severe insulin-resistant diabetes (SIRD). Participants were randomly assigned to treatment with the glucagon-like peptide 1 receptor agonist semaglutide or the sodium–glucose cotransporter 2 inhibitor dapagliflozin for 6 months (open label). The primary endpoint was the change in glycated haemoglobin (HbA1c). Semaglutide induced a larger reduction in HbA1c levels than dapagliflozin (mean difference, 8.2 mmol mol−1; 95% confidence interval, −10.0 to −6.3 mmol mol−1), with a pronounced effect in those with SIDD. No difference in adverse events was observed between participants with SIDD and those with SIRD. Analysis of secondary endpoints showed greater reductions in fasting and postprandial glucose concentrations in response to semaglutide in participants with SIDD than in those with SIRD and a more pronounced effect on postprandial glucose by dapagliflozin in participants with SIDD than in those with SIRD. However, no significant interaction was found between drug assignment and the SIDD or SIRD subgroup. In contrast, continuous measures of body mass index, blood pressure, insulin secretion and insulin resistance were useful in identifying those likely to have the largest improvements in glycaemic control and cardiovascular risk factors by adding semaglutide or dapagliflozin. Thus, systematic evaluation of continuous pathophysiological variables can guide the prediction of the treatment response to these drugs and provide more information than stratified subgroups (NCT04451837).

Towards Non-Invasive and Continuous Blood Pressure Monitoring in Neonatal Intensive Care Using Artificial Intelligence: A Narrative Review.

Preterm birth is a live birth that occurs before 37 completed weeks of pregnancy. Approximately 11% of babies are born preterm annually worldwide. Blood pressure (BP) monitoring is essential for managing the haemodynamic stability of preterm infants and impacts outcomes. However, current methods have many limitations associated, including invasive measurement, inaccuracies, and infection risk. In this narrative review, we find that artificial intelligence (AI) is a promising tool for the continuous measurement of BP in a neonatal cohort, based on data obtained from non-invasive sensors. Our findings highlight key sensing technologies, AI techniques, and model assessment metrics for BP sensing in the neonatal cohort. Moreover, our findings show that non-invasive BP monitoring leveraging AI has shown promise in adult cohorts but has not been broadly explored for neonatal cohorts. We conclude that there is a significant research opportunity in developing an innovative approach to provide a non-invasive alternative to existing continuous BP monitoring methods, which has the potential to improve outcomes for premature babies.

Economic Evaluation of Blood Pressure Monitoring Techniques in Patients With Hypertension

Blood pressure monitoring is critical to the timely diagnosis and treatment of hypertension. At-home self-monitoring techniques are highly effective in managing high blood pressure; however, evidence regarding the cost-effectiveness of at-home self-monitoring compared with traditional monitoring in clinical settings remains unclear. To identify and synthesize published research examining the cost-effectiveness of at-home blood pressure self-monitoring relative to monitoring in a clinical setting among patients with hypertension. A systematic literature search of 5 databases (PubMed, MEDLINE, Embase, EconLit, and CINAHL) followed by a backward citation search was conducted in September 2022. Full-text, peer-reviewed articles in English including patients with high blood pressure (systolic blood pressure ≥130 mm Hg and diastolic blood pressure ≥80 mm Hg) at baseline were included. Data from studies comparing at-home self-monitoring with clinical-setting monitoring alternatives were extracted, and the outcomes of interest included incremental cost-effectiveness and cost-utility ratios. Non-peer-reviewed studies or studies with pregnant women and children were excluded. To ensure accuracy and reliability, 2 authors independently evaluated all articles for eligibility and extracted relevant data from the selected articles. Of 1607 articles identified from 5 databases, 16 studies met the inclusion criteria. Most studies were conducted in the US (6 [40%]) and in the UK (6 [40%]), and almost all studies (14 [90%]) used a health care insurance system perspective to determine costs. Nearly half the studies used quality-adjusted life-years gained and cost per 1-mm Hg reduction in blood pressure as outcomes. Overall, at-home blood pressure monitoring (HBPM) was found to be more cost-effective than monitoring in a clinical setting, particularly over a minimum 10-year time horizon. Among studies comparing HBPM alone vs 24-hour ambulatory blood pressure monitoring (ABPM) or HBPM combined with additional support or team-based care, the latter were found to be more cost-effective. In this systematic review, at-home blood pressure self-monitoring, particularly using automatic 24-hour continuous blood pressure measurements or combined with additional support or team-based care, demonstrated the potential to be cost-effective long-term compared with care in the physical clinical setting and could thus be prioritized for patients with hypertension from a cost-effectiveness standpoint.

History of blood pressure measurement in newborns and infants.

The development of methods for measuring blood pressure (BP) in newborns and small children has a rich history. Methods for BP measuring in adults had to be adapted to this age group. For measuring BP in direct invasive way, a suitable approach had to be found to access the arterial circulation through the umbilical and later radialis artery. Currently, results obtained from direct invasive BP measurement are considered the "gold standard". The development of non-invasive methods for BP measuring in newborns and children began with the use of von Basch's sphygmomanometer (1880). In 1899, Gustav Gärtner constructed the device, which was the basis for the flush method. After the discovery of the palpation and auscultation methods, these methods were also used for BP measurement in newborns and children, however, the BP values obtained in these ways were typically underestimated using excessively wide cuffs. From the auscultation method, methods utilizing ultrasound and infrasound to detect arterial wall movement and blood flow were later developed. The oscillometric method for BP measurement was introduced by E. J. Marey so early as in 1876. In 1912, P. Balard used the oscillometric technique to measure blood pressure in a large group of newborns. Through different types of oscillometers using various methods for detecting vascular oscillations (such as xylol method, impedance and volume plethysmography, etc.), the development has continued to assessment of vascular oscillations by modern sensor technology and software. For continuous non-invasive blood pressure measurement, the volume-clamp method, first described by Jan Peňáz in 1968, was developed. After modification for use in newborns, application of the cuff to the wrist instead of the finger, it is primarily used in clinical physiological studies to evaluate beat-to-beat BP and heart rate pressure variability, such as in the determination of the baroreflex sensitivity.

A Novel Convolutional Neural Network Deep Learning Implementation for Cuffless Heart Rate and Blood Pressure Estimation

Cardiovascular diseases (CVDs) affect components of the circulatory system responsible for transporting blood through blood vessels. The measurement of the mechanical force acting on the walls of blood vessels, as well as the blood flow between heartbeats and when the heart is at rest, is known as blood pressure (BP). Regular assessment of BP can aid in the prevention and early detection of CVDs. In the present research, a deep learning algorithm was developed to accurately calculate both blood pressure (BP) and heart rate (HR) by extracting relevant features from photoplethysmogram (PPG), electrocardiogram (ECG), and ABP signals. This algorithm was implemented using the Medical Information Mart for Intensive Care (MIMIC-II) dataset. It captures vital blood pressure-related features extracted from the PPG signal and accounts for the time relationship with the ECG. The algorithm also determines the values of systolic blood pressure (SBP) and diastolic blood pressure (DBP) based on the ABP waveform through a convolutional neural network and stepwise multivariate linear regression. In comparison with other established BP measurement methods, our proposed approach achieved better results, with a mean absolute error (MAE) of approximately 4.7 mmHg for SBP and 2.1 mmHg for DBP, respectively. The standard deviation (STD) for SBP and DBP was approximately 7.6 mmHg and 3.9 mmHg, respectively. This study makes a valuable contribution to the healthcare field by introducing a novel, cost-effective continuous BP measurement method with improved accuracy while also minimizing the data dimension without losing any important information.

Validation of a novel cuffless photoplethysmography-based wristband for measuring blood pressure according to the regulatory standard

Abstract Background Blood pressure is a key paramater in acute and chronic cardiovascular diseases (CVD) [1]. However, obtaining reliable and reproducible cuff blood pressure (BP) measurements is challenging. We demonstrate that a wristband-based PPG measurement in combination with a custom developed BP algorithm represents a possible alternative to cuff BP measurements. Purpose This study aimed to validate a novel cuffless photopletysmography (PPG)-based non-invasive wristband for continuous BP monitoring in accordance with ISO 81060-2:2019. Methods The study compared PPG-guided BP algorithm predictions with subclavian arterial reference measurements taken during cardiac catheterization. Eligible patients were consecutively included, and eligibility was screened following ISO 81060-2:2019 requirements. Reference measurements were performed using a validated invasive BP monitoring device with a sampling rate of 100Hz. PPG signals were collected using six light emission diodes and two photodiodes at a sampling rate of 128Hz. Three sequential initialization measurements were taken using a validated blood pressure cuff before the cardiac catheterization exam. These measurements, along with approximately 100 additional features (PPG-derived and based on patient demographics), were used as input for the machine learning-based BP algorithm. Correlation, mean error, and standard deviation (SD) were determined for systolic and diastolic BP measurements between the BP algorithm predictions and invasive reference measurements Results The study included 97 patients from whom 420 individual 30-second samples were obtained. The mean age, weight, and height of the analysed subjects were 67.1 (SD 11.1), 83.4 (SD 16.1), and 174.1 (SD 10.0), respectively. In 48 samples (11%), systolic BP was ≤100mmHg, while in 106 samples (25%), systolic BP was ≥160mmHg. Diastolic BP was ≤70 mmHg in 222 samples (53%) and ≥85 mmHg in 99 samples (24%). The BP algorithm predictions showed a high correlation with invasive reference measurements for systolic (r = 0.985) and diastolic (r = 0.961) BP measurements. The mean error of the BP algorithm predictions compared to the invasive reference measurements was ±3.7 mmHg (SD 4.4 mmHg) and ±2.5 mmHg (SD 3.7 mmHg) for systolic and diastolic BP, respectively. Results were similar within each gender and skin colour category (Fitzpatrick I-VI). Conclusion This study demonstrates that a wristband-based PPG measurement in combination with the developed BP algorithm can provide accurate continuous BP measurements across a wide range of BP distributions. Therefore, wristband BP monitoring may serve as a valid and less burdensome alternative to cuff BP measurements for both in-hospital and at-home BP monitoring. However, further research is necessary to evaluate the precision of the BP algorithm during movement and the stability of the predictions over time.figure of BP

Effects of dietary nitrate and vitamin C co-ingestion on blood pressure and hand-grip strength in young adults

Abstract: Background: Co-administration of vitamin C and inorganic nitrate ([Formula: see text]) may reduce oxidative stress, boost the conversion of nitrite ([Formula: see text]) into NO and elicit positive vascular effects. Aims: We aimed to test the effects of oral inorganic [Formula: see text] and vitamin C co-supplementation on vascular function, muscular strength, and on concentrations of urinary [Formula: see text], vitamin C, 8-isoprostanes and salivary [Formula: see text] in healthy young adults. Methods: Ten young healthy participants were enrolled in a randomised, double-blind (only for the [Formula: see text] intervention) crossover clinical trial. Participants consumed in random order: 1) nitrate-rich beetroot juice and vitamin C (N+VC), 2) nitrate-rich beetroot juice alone (N) or 3) nitrate-depleted beetroot juice alone (ND). Resting blood pressure (BP) was measured at the research centre and at home. Non-invasive, continuous measurements of BP and cardiac function parameters were performed using a Finometer device. Free-living physical activity and hand-grip strength were assessed. Salivary [Formula: see text] and [Formula: see text] and urinary [Formula: see text], 8-isoprostanes and vitamin C concentrations were measured. Results: There were no significant differences for any of the vascular outcomes between the three interventions groups. However, analyses of within-intervention changes showed a significant lower daily systolic BP in the [Formula: see text]+vitamin C (N+VC) group only (P=0.04). Urinary [Formula: see text] (P=0.002) and salivary [Formula: see text] (P=0.001) were significantly higher in the N+VC group compared to the N and ND groups. Conclusion: These preliminary findings suggest that combining dietary [Formula: see text] with vitamin C could have protective effects on vascular function in young adults and could represent an effective strategy for the maintenance of healthy cardiovascular trajectories.

Accuracy and User Acceptability of 24-hour Ambulatory Blood Pressure Monitoring by a Prototype Cuffless Multi-Sensor Device Compared to a Conventional Oscillometric Device

Objective24-hour ambulatory blood pressure monitoring (24ABPM) is state of the art in out-of-office blood pressure (BP) monitoring. Due to discomfort and technical limitations related to cuff-based 24ABPM devices, methods for non-invasive and continuous estimation of BP without the need for a cuff have gained interest. The main aims of the present study were to compare accuracy of a pulse arrival time (PAT) based BP-model and user acceptability of a prototype cuffless multi-sensor device (cuffless device), developed by Aidee Health AS, with a conventional cuff-based oscillometric device (ReferenceBP) during 24ABPM.MethodsNinety-five normotensive and hypertensive adults underwent simultaneous 24ABPM with the cuffless device on the chest and a conventional cuff-based oscillometric device on the non-dominant arm. PAT was calculated using the electrocardiogram (ECG) and photoplethysmography (PPG) sensors incorporated in the chest-worn device. The cuffless device recorded continuously, while ReferenceBP measurements were taken every 20 minutes during daytime and every 30 minutes during nighttime. Two-minute PAT-based BP predictions corresponding to the ReferenceBP measurements were compared with ReferenceBP measurements using paired t-tests, bias, and limits of agreement.ResultsMean (SD) of ReferenceBP compared to PAT-based daytime and nighttime systolic BP (SBP) were 129.7 (13.8) mmHg versus 133.6 (20.9) mmHg and 113.1 (16.5) mmHg versus 131.9 (23.4) mmHg. Ninety-five % limits of agreements were [-26.7, 34.6 mmHg] and [-20.9, 58.4 mmHg] for daytime and nighttime SBP respectively. The cuffless device was reported to be significantly more comfortable and less disturbing than the ReferenceBP device during 24ABPM.ConclusionsIn the present study, we demonstrated that a general PAT-based BP model had unsatisfactory agreement with ambulatory BP during 24ABPM, especially during nighttime. If sufficient accuracy can be achieved, cuffless BP devices have promising potential for clinical assessment of BP due to the opportunities provided by continuous BP measurements during real-life conditions and high user acceptability.

Salt-sensitive trait of normotensive individuals is associated with altered autonomous cardiac regulation: a randomized controlled intervention study.

Blood pressure (BP) responses to sodium intake show great variation, discriminating salt-sensitive (SS) from salt-resistant (SR) individuals. The pathophysiology behind salt sensitivity is still not fully elucidated. We aimed to investigate salt-induced effects on body fluid, vascular tone, and autonomic cardiac response with regard to BP change in healthy normotensive individuals. We performed a randomized crossover study in 51 normotensive individuals with normal body mass index and estimated glomerular filtration rate. Subjects followed both a low-Na+ diet (LSD, <50 mmol/day) and a high-Na+ diet (HSD, >200 mmol/day). Cardiac output, systemic vascular resistance (SVR), and cardiac autonomous activity, through heart rate variability and cross-correlation baroreflex sensitivity (xBRS), were assessed with noninvasive continuous finger BP measurements. In a subset, extracellular volume (ECV) was assessed by iohexol measurements. Subjects were characterized as SS if mean arterial pressure (MAP) increased ≥3 mmHg after HSD. After HSD, SS subjects (25%) showed a 6.1-mmHg (SD 1.9) increase in MAP. No differences between SS and SR in body weight, cardiac output, or ECV were found. SVR was positively correlated with Delta BP (r = 0.31, P = 0.03). xBRS and heart rate variability were significantly higher in SS participants compared to SR participants after both HSD and LSD. Sodium loading did not alter heart rate variability within groups. Salt sensitivity in normotensive individuals is associated with an inability to decrease SVR upon high salt intake that is accompanied by alterations in autonomous cardiac regulation, as reflected by decreased xBRS and heart rate variability. No discriminatory changes upon high salt were observed among salt-sensitive individuals in body weight and ECV.NEW & NOTEWORTHY Extracellular fluid expansion in normotensive individuals after salt loading is present in both salt-sensitive and salt-resistant individuals and is not discriminatory to the blood pressure response to sodium loading in a steady-state measurement. In normotensive subjects, the ability to sufficiently vasodilate seems to play a pivotal role in salt sensitivity. In a normotensive cohort, differences in sympathovagal balance are also present in low-salt conditions rather than being affected by salt loading. Whereas treatment and prevention of salt-sensitive blood pressure increase are mostly focused on renal sodium handling and extracellular volume regulation, our study suggests that an inability to adequately vasodilate and altered autonomous cardiac functioning are additional key players in the pathophysiology of salt-sensitive blood pressure increase.

Accuracy of intra-arterial line transducer levelling practice in a general intensive care unit

BackgroundThe intra-arterial line is a common device intervention used in the intensive care environment to provide continuous blood pressure measurement. The transducer line is levelled to the patient's phlebostatic axis to provide accurate measurements. AimThe aim of this study was to investigate registered nurses' accuracy at levelling the transducer to the correct anatomical position using visual judgement, compared to one done using a laser level. MethodsPatient transducers were levelled by visual judgement and then by using a laser level. Time and mean arterial pressure (MAP) were recorded with each measurement along with any difference in transducer level between the two methods and subsequent changes in inotrope administration. ResultsA total of 577 MAP measurements were recorded from 178 patients; 70% of observations had a difference in transducer level, 30% of the time the inotrope rate was increased and 18% of the time the inotrope rate was reduced. The prevalence of clinically significant observations with an absolute difference of 50 mm or more in transducer placement was 25%. The mean difference in MAP measurements when a cut-off of 64 mmHg or more for laser was applied to the data was 0.22 (95% confidence interval: −0.14, 0.58, n = 513, p = 0.23), and for a cut-off of less than 64 for laser, a larger mean difference of 4.36 (95% confidence interval: 3.75, 5.28], n = 64, p < 0.001) was observed. ConclusionsTransducers were unable to be accurately levelled for haemodynamic monitoring using visual means alone. Over the range of patient MAP values examined, 25% of all observations had a clinically significant absolute difference of 50 mm or more in the transducer level position between the two methods. The visual method became increasingly inaccurate and unreliable at low MAP levels requiring medical intervention.

FewShotBP

Deep learning-based methods demonstrate promising results in continuous non-invasive blood pressure measurement, whereas those models trained on large public datasets suffer from severe performance degradation in predicting from real-world user data collected in home settings. Transfer learning has been recently introduced to personalize the pre-trained model with unseen users' data to solve the problem. However, the existing methods based on network fine-tuning for model personalization require a large amount of labeled data, lacking practicality due to labeling using a cuff-based blood pressure monitor is extremely tedious and laborious for home users. In this paper, we propose a novel few-shot transfer learning approach named FewShotBP, which addresses the above-mentioned challenges by introducing a personalization adapter at the personalization stage (i.e., the transfer learning stage), and a multi-modal spectro-temporal neural network at the pre-train stage, to bridge the gap between data-hungry models and limited labeled data in realistic scenarios. To evaluate the approach's significance, we conducted experiments using both a publicly available dataset and a real-world user experiment. The results demonstrated that the proposed approach achieves similar accuracy of blood pressure prediction with 10× less data for personalization compared with the state-of-the-art method in the public dataset and achieves a mean absolute error of 6.68 mmHg (systolic blood pressure) and 3.91 mmHg (diastolic blood pressure) with only 10 personal data samples in the real-world user experiment.

Pressure- vs. volume-controlled ventilation and their respective impact on dynamic parameters of fluid responsiveness: a cross-over animal study

Background and goal of studyPulse pressure variation (PPV) and stroke volume variation (SVV), which are based on the forces caused by controlled mechanical ventilation, are commonly used to predict fluid responsiveness. When PPV and SVV were introduced into clinical practice, volume-controlled ventilation (VCV) with tidal volumes (VT) ≥ 10 ml kg− 1 was most commonly used. Nowadays, lower VT and the use of pressure-controlled ventilation (PCV) has widely become the preferred type of ventilation. Due to their specific flow characteristics, VCV and PCV result in different airway pressures at comparable tidal volumes. We hypothesised that higher inspiratory pressures would result in higher PPVs and aimed to determine the impact of VCV and PCV on PPV and SVV.MethodsIn this self-controlled animal study, sixteen anaesthetised, paralysed, and mechanically ventilated (goal: VT 8 ml kg− 1) pigs were instrumented with catheters for continuous arterial blood pressure measurement and transpulmonary thermodilution. At four different intravascular fluid states (IVFS; baseline, hypovolaemia, resuscitation I and II), ventilatory and hemodynamic data including PPV and SVV were assessed during VCV and PCV. Statistical analysis was performed using U-test and RM ANOVA on ranks as well as descriptive LDA and GEE analysis.ResultsComplete data sets were available of eight pigs. VT and respiratory rates were similar in both forms. Heart rate, central venous, systolic, diastolic, and mean arterial pressures were not different between VCV and PCV at any IVFS. Peak inspiratory pressure was significantly higher in VCV, while plateau, airway and transpulmonary driving pressures were significantly higher in PCV. However, these higher pressures did not result in different PPVs nor SVVs at any IVFS.ConclusionVCV and PCV at similar tidal volumes and respiratory rates produced PPVs and SVVs without clinically meaningful differences in this experimental setting. Further research is needed to transfer these results to humans.

Impaired cerebrovascular reactivity correlates with reduced retinal vessel density in patients with carotid artery stenosis: Cross-sectional, single center study.

The cerebral and retinal circulation systems are developmentally, anatomically, and physiologically interconnected. Thus, we hypothesized that hypoperfusion due to atherosclerotic stenosis of the internal carotid artery (ICA) can result in disturbances of both cerebral and retinal microcirculations. We aimed to characterize parameters indicating cerebrovascular reactivity (CVR) and retinal microvascular density in patients with ICA stenosis, and assess if there is correlation between them. In this cross-sectional study the middle cerebral artery (MCA) blood flow velocity was measured by transcranial Doppler (TCD) and, simultaneously, continuous non-invasive arterial blood pressure measurement was performed on the radial artery by applanation tonometry. CVR was assessed based on the response to the common carotid artery compression (CCC) test. The transient hyperemic response ratio (THRR) and cerebral arterial resistance transient hyperemic response ratio (CAR-THRR) were calculated. Optical coherence tomography angiography (OCTA) was used to determine vessel density (VD) on the papilla whole image for all (VDP-WIall) and for small vessels (VDP-WIsmall). The same was done in the peripapillary region: all (VDPPall), and small (VDPPsmall) vessels. The VD of superficial (VDMspf) and deep (VDMdeep) macula was also determined. Significance was accepted when p<0.05. Twenty-four ICA stenotic patients were evaluated. Both CVR and retinal VD were characterized. There was a significant, negative correlation between CAR-THRR (median = -0.40) and VDPPsmall vessels (median = 52%), as well as between VDPPall vessels (median = 58%), and similar correlation between CAR-THRR and VDP-WIsmall (median = 49.5%) and between VDP-WIall (median = 55%). The significant correlation between impaired cerebrovascular reactivity and retinal vessel density in patients with ICA stenosis suggests a common mechanism of action. We propose that the combined use of these diagnostic tools (TCD and OCTA) helps to better identify patients with increased ischemic or other cerebrovascular risks.