Blood Pressure Estimation Research Articles

Broadening the Utilization of Flexible and Wearable Pressure Sensors for the Monitoring of Health and Physiological Activities

Recently, the high need for artificial intelligence, human motion monitoring, and wearable devices that can be utilized to track human health conditions, particularly for patients experiencing any sickness, has drawn serious interest from researchers. The flexible pressure sensors are a promising possibility for these human–machine interface applications because of their quick reaction, huge sensitivity, and ultra-low detection limit. These flexible sensing devices provide real-time statistics about the conditions of the human body, like blood pressure and heartbeat estimation, to recognize cardiovascular-related diseases. We have developed a versatile and wearable capacitive sensing device by changing the polydimethylsiloxane (PDMS) dielectric layer's structure using a scrubber layer which introduced the porosity in the dielectric layer. The developed sensor showed excellent response under static pressure and dynamic pressure applications. Due to large sensitivity, high working stability, and speedy reaction (120 ms), the versatility of the pressure sensor has been demonstrated in various human motion monitoring applications like wrist bending, palm grip, elbow bending, knee twisting, and vocal-cord vibration detection. In addition, the developed sensor was also used in human–machine interface applications to perceive heartbeat and wrist-pulse motion under typical and after-exercise conditions.

Continuous cuffless blood pressure monitoring with a wearable ring bioimpedance device

Smart rings provide unique opportunities for continuous physiological measurement. They are easy to wear, provide little burden in comparison to other smart wearables, are suitable for nocturnal settings, and can be sized to provide ideal contact between the sensors and the skin at all times. Continuous measuring of blood pressure (BP) provides essential diagnostic and prognostic value for cardiovascular health management. However, conventional ambulatory BP measurement devices operate using an inflating cuff that is bulky, intrusive, and impractical for frequent or continuous measurements. We introduce ring-shaped bioimpedance sensors leveraging the deep tissue sensing ability of bioimpedance while introducing no sensitivity to skin tones, unlike optical modalities. We integrate unique human finger finite element model with exhaustive experimental data from participants and derive optimum design parameters for electrode placement and sizes that yields highest sensitivity to arterial volumetric changes, with no discrimination against varying skin tones. BP is constructed using machine learning algorithms. The ring sensors are used to estimate arterial BP showing peak correlations of 0.81, and low error (systolic BP: 0.11 ± 5.27 mmHg, diastolic BP: 0.11 ± 3.87 mmHg) for >2000 data points and wide BP ranges (systolic: 89–213 mmHg and diastolic: 42–122 mmHg), highlighting the significant potential use of bioimpedance ring for accurate and continuous estimation of BP.

Estimation of blood pressure In adult population in Baiji

Data was collected from 60 cases in age groups from 40-60 years ( 30 female and 30 male ).The cases included in this study was collected randomly in Baiji hospital from June to October 2006 .The data was obtained by using a questionnaire . It was found that the most common cases use Beta blockers as antihypertensive drugs , 12 cases use B-blocker plus Angiotensin converting enzymeinhibitors and 5 cases use B-blocker with diuretics.There are 8 cases use Angiotensin converting enzyme inhibitor only and 7 cases use vasodilator drugs .Regarding the measurement of bloodpressure and heart rate , in 36 cases complaining from increase blood pressure above the normal level there is increase in heart rate in 30 cases ( heart rate more than 100 beat / minute ) , andin24cases of normal blood pressure there is increase in heart rate in 5 cases only .The aim of this study is to investigate the prevalence of hypertension in age groups from 40-60 years in Baiji from June toOctober with some investigations

Simultaneous photoplethysmography and blood flow measurements towards the estimation of blood pressure using speckle contrast optical spectroscopy.

Non-invasive continuous blood pressure monitoring remains elusive. There has been extensive research using the photoplethysmographic (PPG) waveform for blood pressure estimation, but improvements in accuracy are still needed before clinical use. Here we explored the use of an emerging technique, speckle contrast optical spectroscopy (SCOS), for blood pressure estimation. SCOS provides measurements of both blood volume changes (PPG) and blood flow index (BFi) changes during the cardiac cycle, and thus provides a richer set of parameters compared to traditional PPG. SCOS measurements were taken on the finger and wrists of 13 subjects. We investigated the correlations between features extracted from both the PPG and BFi waveforms with blood pressure. Features from the BFi waveforms were more significantly correlated with blood pressure than PPG features ( R = - 0.55, p = 1.1 × 10-4 for the top BFi feature versus R = - 0.53, p = 8.4 × 10-4 for the top PPG feature). Importantly, we also found that features combining BFi and PPG data were highly correlated with changes in blood pressure ( R = - 0.59, p = 1.7 × 10-4 ). These results suggest that the incorporation of BFi measurements should be further explored as a means to improve blood pressure estimation using non-invasive optical techniques.

Blood pressure estimation and its recalibration assessment using wrist cuff blood pressure monitor.

The rapid evolution of wearable technology in healthcare sectors has created the opportunity for people to measure their blood pressure (BP) using a smartwatch at any time during their daily activities. Several commercially-available wearable devices have recently been equipped with a BP monitoring feature. However, concerns about recalibration remain. Pulse transit time (PTT)-based estimation is required for initial calibration, followed by periodic recalibration. Recalibration using arm-cuff BP monitors is not practical during everyday activities. In this study, we investigated recalibration using PTT-based BP monitoring aided by a deep neural network (DNN) and validated the performance achieved with more practical wrist-cuff BP monitors. The PTT-based prediction produced a mean absolute error (MAE) of 4.746 ± 1.529 mmHg for systolic blood pressure (SBP) and 3.448 ± 0.608 mmHg for diastolic blood pressure (DBP) when tested with an arm-cuff monitor employing recalibration. Recalibration clearly improved the performance of both DNN and conventional linear regression approaches. We established that the periodic recalibration performed by a wrist-worn BP monitor could be as accurate as that obtained with an arm-worn monitor, confirming the suitability of wrist-worn devices for everyday use. This is the first study to establish the potential of wrist-cuff BP monitors as a means to calibrate BP monitoring devices that can reliably substitute for arm-cuff BP monitors. With the use of wrist-cuff BP monitoring devices, continuous BP estimation, as well as frequent calibrations to ensure accurate BP monitoring, are now feasible.

A benchmark for machine-learning based non-invasive blood pressure estimation using photoplethysmogram

Blood Pressure (BP) is an important cardiovascular health indicator. BP is usually monitored non-invasively with a cuff-based device, which can be bulky and inconvenient. Thus, continuous and portable BP monitoring devices, such as those based on a photoplethysmography (PPG) waveform, are desirable. In particular, Machine Learning (ML) based BP estimation approaches have gained considerable attention as they have the potential to estimate intermittent or continuous BP with only a single PPG measurement. Over the last few years, many ML-based BP estimation approaches have been proposed with no agreement on their modeling methodology. To ease the model comparison, we designed a benchmark with four open datasets with shared preprocessing, the right validation strategy avoiding information shift and leak, and standard evaluation metrics. We also adapted Mean Absolute Scaled Error (MASE) to improve the interpretability of model evaluation, especially across different BP datasets. The proposed benchmark comes with open datasets and codes. We showcase its effectiveness by comparing 11 ML-based approaches of three different categories.

A Machine Learning Approach to the Non-Invasive Estimation of Continuous Blood Pressure Using Photoplethysmography

Blood pressure is an important vital sign that sometimes requires continuous measurement. The current methods include cuff measurements (manual auscultation and oscillometric techniques) for non-continuous measurement and invasive arterial cannulation for continuous measurement. The use of photoplethysmography as a cuffless, non-invasive, and continuous blood pressure measurement system is investigated through the use of four neural networks. These predict the systolic blood pressure, diastolic blood pressure, mean arterial blood pressure, and waveform shape. The models are trained on 890 h of data from 1669 patients in the MIMIC-III database. Feature-trained artificial neural networks predict the systolic blood pressure to 5.26 ± 6.53 mmHg (mean error ± standard deviation), the diastolic blood pressure to 2.96 ± 3.31 mmHg, and the mean arterial pressure to 3.27 ± 3.55 mmHg. These are used to shift and scale the predicted waveform, allowing the waveform prediction neural network to optimise for the wave shape rather than the amplitude. The waveform prediction has 86.4% correlation with the actual arterial blood pressure waveform. All results meet international clinical blood pressure measurement standards and could potentially change how blood pressure is measured in both clinical and research settings. However, more data from healthy individuals and analysis of the models’ biases based on clinical features is required.

Toward IoT-Based Medical Edge Devices: PPG-Based Blood Pressure Estimation Application

The Internet of Things (IoT) now provide a considerable advantage in terms of intelligent devices. For example, sensor data of medical equipment could be used to extract meaningful insights to enable a range of tasks to be performed intelligently at the device location, such as blood pressure (BP) prediction several minutes ahead. In this article, we propose a computing device called Medical Edge, designed to convert conventional medical equipment into IoT-enabled devices. We developed an interworking proxy that allows Medical Edge to be interfaced with medical devices, such as patient monitors (e.g., GE CARESCAPE B650), to smoothly collect physiological data and upload them to an IoT server platform. We used a oneM2M standard-based server platform to provide access to these data in a standardized manner. To demonstrate a promising application of our proposed Medical Edges, we performed a study on BP estimation based on photoplethysmography (PPG) signal only. We propose a hybrid neural network architecture and apply it with a publicly available data set called multiparameter intelligent monitoring in intensive care II (MIMIC II). The model consists of five 1-D convolutional neural networks (CNNs), three Bi-directional long short-term memory networks, and four fully connected layers. The mean absolute error (MAE) and standard deviation (STD) of the proposed model were, respectively, 0.95 and 1.44 millimetres of mercury (mmHg) for diastolic BP (DBP), and 1.38 and 2.13 mmHg for systolic BP (SBP). The experimental results are in full compliance with the international standards of the Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS).

Remote Blood Pressure Estimation via the Spatiotemporal Mapping of Facial Videos

Blood pressure (BP) monitoring is vital in daily healthcare, especially for cardiovascular diseases. However, BP values are mainly acquired through a contact-sensing method, which is inconvenient and unfriendly for BP monitoring. This paper proposes an efficient end-to-end network for estimating BP values from a facial video to achieve remote BP estimation in daily life. The network first derives a spatiotemporal map of a facial video. Then, it regresses the BP ranges with a designed blood pressure classifier and simultaneously calculates the specific value with a blood pressure calculator in each BP range based on the spatiotemporal map. In addition, an innovative oversampling training strategy was developed to handle the problem of unbalanced data distribution. Finally, we trained the proposed blood pressure estimation network on a private dataset, MPM-BP, and tested it on a popular public dataset, MMSE-HR. As a result, the proposed network achieved a mean absolute error (MAE) and root mean square error (RMSE) of 12.35 mmHg and 16.55 mmHg on systolic BP estimations, and those for diastolic BP were 9.54 mmHg and 12.22 mmHg, which were better than the values obtained in recent works. It can be concluded that the proposed method has excellent potential for camera-based BP monitoring in the indoor scenarios in the real world.

Intensive care photoplethysmogram datasets and machine-learning for blood pressure estimation: Generalization not guarantied

The large MIMIC waveform dataset, sourced from intensive care units, has been used extensively for the development of Photoplethysmography (PPG) based blood pressure (BP) estimation algorithms. Yet, because the data comes from patients in severe conditions-often under the effect of drugs-it is regularly noted that the relationship between BP and PPG signal characteristics may be anomalous, a claim that we investigate here. A sample of 12,000 records from the MIMIC waveform dataset was stacked up against the 219 records of the PPG-BP dataset, an alternative public dataset obtained under controlled experimental conditions. The distribution of systolic and diastolic BP data and 31 PPG pulse morphological features was first compared between datasets. Then, the correlation between features and BP, as well as between the features themselves, was analysed. Finally, regression models were trained for each dataset and validated against the other. Statistical analysis showed significant differences between the datasets in diastolic BP and in 20 out of 31 features when adjusting for heart rate differences. The eight features showing the highest rank correlation to systolic BP in PPG-BP all displayed muted correlation levels in MIMIC. Regression tests showed twice higher baseline predictive power with PPG-BP than with MIMIC. Cross-dataset regression displayed a practically complete loss of predictive power for all models. The differences between the MIMIC and PPG-BP dataset exposed in this study suggest that BP estimation models based on the MIMIC dataset have reduced predictive power on the general population.

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

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

Remote Health Monitoring System for the Estimation of Blood Pressure, Heart Rate, and Blood Oxygen Saturation Level

This article presents the design and implementation of an Internet of Things (IoT)-based remote health monitoring system for the estimation of blood pressure (BP), heart rate (HR), and blood oxygen saturation levels (SpO2). Our designed sensor can remotely monitor BP, HR, and SpO2. Our device collects, evaluates, predicts, and reads health data and then stores it on a remote platform named “ThinkSpeak,” which forms an IoT platform, with a 0.91 organic light-emitting diodes (OLEDs) screen display for viewing numerical health readings locally. We used a biomedical sensor device with an embedded signal condition unit, and a single photoplethysmography (PPG) signal was employed to derive and measure the PPG signal. A computer-based algorithm was generated, which factored in selected beneficial parameters measured from a single bio-inspired PPG signal. The measured PPG signal was used to estimate the individual user’s BP (both systolic and diastolic values), HR, and SpO2. An automatic multiscale-based peak (AMBP) detection algorithm was developed to obtain the maximum peak of the PPG signal. Furthermore, the developed sensor was benchmarked against two standard commercially available measurement devices: a Contec ambulatory BP sensor and a Braun pulse oximeter monitor. Our developed sensor is worn as a ring sensor and is interfaced with an Arduino 1010 WIFI MKR for remote health monitoring. Our estimated BP, HR, and SpO2 values were remotely monitored and a graphical representation was constructed.

Noninvasive continuous blood pressure estimation with fewer parameters based on RA-ReliefF feature selection and MPGA-BPN models

Photoplethysmography (PPG) and electrocardiogram (ECG) are closely related to the cardiovascular system and have substantial waveform parameters associated with blood pressure (BP), so extracting their waveform parameters to estimate BP has been widely studied. However, the more parameters, the more complex the model is, and the more factors affect the estimation results, which further affects the application of the BP estimation model to related equipment. Greatly reducing the number of parameters without reducing the accuracy of BP prediction, so as to improve the availability, applicability and robustness of BP estimation model, is the key for the real application of this method. In this study, we used relativity analysis (RA) to fill the gap of ReliefF algorithm, so the same number of parameter dimensions selected by ReliefF contain more signal information. Furthermore, a BP prediction model based on multi-population genetic algorithm (MPGA) and back propagation network (BPN) was constructed. Using waveform parameters of PPG and ECG signals with a duration of 20 s and the measured BP as a set of data, we extracted 522 and 23,608 sets of data from 25 healthy subjects and the UCI_BP dataset respectively. Three, five, seven and all parameters were selected by ReliefF for training in MPGA-BPN model and the results were compared. The comparison shows that five-parameters dataset can performs as all- parameters in both healthy individuals and UCI_BP, which confirms the validity of the feature selection in this study and lays the roots for BP estimation with fewer parameters and high accuracy.

Upper-Arm Photoplethysmographic Sensor with One-Time Calibration for Long-Term Blood Pressure Monitoring.

Wearable cuffless photoplethysmographic blood pressure monitors have garnered widespread attention in recent years; however, the long-term performance values of these devices are questionable. Most cuffless blood pressure monitors require initial baseline calibration and regular recalibrations with a cuffed blood pressure monitor to ensure accurate blood pressure estimation, and their estimation accuracy may vary over time if left uncalibrated. Therefore, this study assessed the accuracy and long-term performance of an upper-arm, cuffless photoplethysmographic blood pressure monitor according to the ISO 81060-2 standard. This device was based on a nonlinear machine-learning model architecture with a fine-tuning optimized method. The blood pressure measurement protocol followed a validation procedure according to the standard, with an additional four weekly blood pressure measurements over a 1-month period, to assess the long-term performance values of the upper-arm, cuffless photoplethysmographic blood pressure monitor. The results showed that the photoplethysmographic signals obtained from the upper arm had better qualities when compared with those measured from the wrist. When compared with the cuffed blood pressure monitor, the means ± standard deviations of the difference in BP at week 1 (baseline) were -1.36 ± 7.24 and -2.11 ± 5.71 mmHg for systolic and diastolic blood pressure, respectively, which met the first criterion of ≤5 ± ≤8.0 mmHg and met the second criterion of a systolic blood pressure ≤ 6.89 mmHg and a diastolic blood pressure ≤ 6.84 mmHg. The differences in the uncalibrated blood pressure values between the test and reference blood pressure monitors measured from week 2 to week 5 remained stable and met both criteria 1 and 2 of the ISO 81060-2 standard. The upper-arm, cuffless photoplethysmographic blood pressure monitor in this study generated high-quality photoplethysmographic signals with satisfactory accuracy at both initial calibration and 1-month follow-ups. This device could be a convenient and practical tool to continuously measure blood pressure over long periods of time.

Blood pressure stratification using photoplethysmography and light gradient boosting machine.

Introduction: Globally, hypertension (HT) is a substantial risk factor for cardiovascular disease and mortality; hence, rapid identification and treatment of HT is crucial. In this study, we tested the light gradient boosting machine (LightGBM) machine learning method for blood pressure stratification based on photoplethysmography (PPG), which is used in most wearable devices. Methods: We used 121 records of PPG and arterial blood pressure (ABP) signals from the Medical Information Mart for Intensive Care III public database. PPG, velocity plethysmography, and acceleration plethysmography were used to estimate blood pressure; the ABP signals were used to determine the blood pressure stratification categories. Seven feature sets were established and used to train the Optuna-tuned LightGBM model. Three trials compared normotension (NT) vs. prehypertension (PHT), NT vs. HT, and NT + PHT vs. HT. Results: The F1 scores for these three classification trials were 90.18%, 97.51%, and 92.77%, respectively. The results showed that combining multiple features from PPG and its derivative led to a more accurate classification of HT classes than using features from only the PPG signal. Discussion: The proposed method showed high accuracy in stratifying HT risks, providing a noninvasive, rapid, and robust method for the early detection of HT, with promising applications in the field of wearable cuffless blood pressure measurement.

Photoplethysmography Signal Wavelet Enhancement and Novel Features Selection for Non-Invasive Cuff-Less Blood Pressure Monitoring.

In this paper, new features relevant to blood pressure (BP) estimation using photoplethysmography (PPG) are presented. A total of 195 features, including the proposed ones and those already known in the literature, have been calculated on a set composed of 50,000 pulses from 1080 different patients. Three feature selection methods, namely Correlation-based Feature Selection (CFS), RReliefF and Minimum Redundancy Maximum Relevance (MRMR), have then been applied to identify the most significant features for BP estimation. Some of these features have been extracted through a novel PPG signal enhancement method based on the use of the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the enhanced signal leads to a reliable identification of the characteristic points of the PPG signal (e.g., systolic, diastolic and dicrotic notch points) by simple means, obtaining results comparable with those from purposely defined algorithms. For systolic points, mean and std of errors computed as the difference between the locations obtained using a purposely defined already known algorithm and those using the MODWT enhancement are, respectively, 0.0097 s and 0.0202 s; for diastolic points they are, respectively, 0.0441 s and 0.0486 s; for dicrotic notch points they are 0.0458 s and 0.0896 s. Hence, this study leads to the selection of several new features from the MODWT enhanced signal on every single pulse extracted from PPG signals, in addition to features already known in the literature. These features can be employed to train machine learning (ML) models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) in a non-invasive way, which is suitable for telemedicine health-care monitoring.

Combining Gaussian Process with Hybrid Optimal Feature Decision in Cuffless Blood Pressure Estimation.

Noninvasive blood pressure estimation is crucial for cardiovascular and hypertension patients. Cuffless-based blood pressure estimation has received much attention recently for continuous blood pressure monitoring. This paper proposes a new methodology that combines the Gaussian process with hybrid optimal feature decision (HOFD) in cuffless blood pressure estimation. First, we can choose one of the feature selection methods: robust neighbor component analysis (RNCA), minimum redundancy, maximum relevance (MRMR), and F-test, based on the proposed hybrid optimal feature decision. After that, a filter-based RNCA algorithm uses the training dataset to obtain weighted functions by minimizing the loss function. Next, we combine the Gaussian process (GP) algorithm as the evaluation criteria, which is used to determine the best feature subset. Hence, combining GP with HOFD leads to an effective feature selection process. The proposed combining Gaussian process with the RNCA algorithm shows that the root mean square errors (RMSEs) for the SBP (10.75 mmHg) and DBP (8.02 mmHg) are lower than those of the conventional algorithms. The experimental results represent that the proposed algorithm is very effective.

The pre-ejection period is a highly stress dependent parameter of paramount importance for pulse-wave-velocity based applications.

The pulse-wave-velocity, is used for indirect, cuff-less, continuous blood pressure estimation. It is commonly detected by measuring the time delay between a defined point in an ECG and the arrival of the peripheral pulse wave (e.g., oxygen saturation sensor). The period between electrical stimulation of the heart (ECG) and actual blood ejection from the heart is called the pre-ejection period (PEP). This study aims at characterizing the PEP under mental and physical stress with focus on its relations to other cardiovascular parameters such as heart rate and importance for blood pressure (BP) estimation. We measured the PEP in 71 young adults at rest, under mental (TSST) and physical stress (ergometer) via impedance-cardiography. The PEP is highly dependent on mental and physical load. It is strongly correlated with indicators of sympathetic strain (p < 0.001). At rest (mean 104.5 ms), the PEP shows a high interindividual variability but small intraindividual variability. Mental stress decreases the PEP by 16% (mean 90.0 ms) while physical stress halves PEP (mean 53.9 ms). The PEP does correlate differently with heart rate under differing circumstances (rest: R 2 0.06, mental stress: R 2 0.29, physical stress: R 2 0.65). Subsequently, using PEP and heart rate enables the discrimination of rest, mental and physical strain with a positive predictive value of 93%. The PEP is a cardiovascular parameter with large interindividual variability at rest and subject-depended dynamic under load which is of great importance for ECG-based pulse-wave-velocity (PWV) determination. Considering its variability and large impact on the pulse arrival time, PEP is a crucial factor in PWV based BP estimation.

Factors of QTc interval prolongation in patients with arterial hypertension

The relevance of determining the factors of the prolonged QTc interval development in patients with arterial hypertension is high, as it solves the issue of preliminary diagnosis among patients from risk groups and allows adjusting the diagnostic and therapeutic tactics of managing such patients. Aim: to determine factors for the development of prolonged average daily QTc interval in patients with arterial hypertension. Materials and methods. The study included 195 patients with arterial hypertension, who were divided into 2 subgroups: 175 individuals with a normal average daily duration of the QTc interval and 20 patients with a prolonged average daily duration of the QTc interval. All the patients underwent 24-hour heart rate monitoring and determination of blood pressure variability. At the stage of involving patients in the study, the following were performed: anamnesis collection and determination of physical data (estimation of systolic and diastolic blood pressure). Results. The study has revealed risk factors of daily QTc interval prolongation in hypertensive patients with further developing a prognostic model with high sensitivity (88.9 %) and specificity (70.6 %). Prolongation of QTc in patients with hypertension was associated with such parameters: male sex (OR = 4.292 [95.0 % CI 1.337–13.779], Р = 0.014), body mass index (OR = 1.107 [95.0 % CI 0.987–1.243], Р = 0.083), mean 24-hour diastolic (OR = 1.076 [95.0 % CI 1.017–1.139], Р = 0.011) and pulse blood pressure (OR = 1.075 [95.0 % CІ 1.012–1.141], Р = 0.018), time since hypertension was diagnosed (OR = 1.093 [95.0 % CI 0.997–1.197], Р = 0.057) and office pulse blood pressure (OR = 0.948 [95.0 % CІ 0.900–0.999], Р = 0.046). Conclusions. The study has determined risk factors of developing a prolonged average daily QTc interval in patients with hypertension, which will allow correcting diagnostic and treatment tactics of management for patients from risk groups.

Examining the Individual Response to a Low-Sodium Diet in Patients with Hypertension: Protocol for a Pilot Randomized Controlled Trial.

Excessive dietary sodium intake is an independent risk factor for hypertension and cardiovascular disease (CVD). Despite the large body of evidence concerning the effects of dietary interventions on blood pressure (BP) and CVD outcomes, trials have often reported low adherence to decreased sodium intake, likely due in part to heterogeneous BP responses. To address the challenges, recent clinical findings suggested a precise and personalized dietary approach that seeks to deliver more preventive and practical dietary advice than the "one-size-fits-all" guidelines and weighs the personal risk of developing specific diseases. The purpose of this pilot randomized controlled trial was to test the feasibility and preliminary efficacy of integrating the use of mobile technology and metabolomics with a low-sodium diet intervention in patients with hypertension to develop personalized low-sodium diet programs. Additionally, the study will examine the associations of urine metabolites with urinary sodium levels and BP control based on the hypothesis that targeted urine metabolites. In this report, we describe the design and protocol of the pilot trial. A total of 40 patients with hypertension will be randomly assigned to either a 8-week low-sodium diet group (n=20) or a standard care group (n=20). Each week, intervention participants went through individual sessions with an interventionist via videoconferencing to discuss low-sodium diet regimens, patients' food choices, and BP tracks on mobile apps. The control group followed their usual care for hypertension management. All participants in both groups monitored diet and BP using mobile apps for 8 weeks. A 24-hour urinary sodium excretion for the estimation of dietary sodium intake, systolic, and diastolic BPs were measured at the baseline and at 8 weeks. The primary outcomes of this study include the feasibility of conducting a randomized controlled trial (RCT) by reporting recruitment, retention, and completion statistics. The preliminary effects of intervention will be tested by a generalized estimating equation model. This pilot RCT study was approved by the institutional review board at the University of Texas Health San Antonio in January 2021. The first participant was enrolled in April 2021, and currently, 26 participants were enrolled. All data collection is expected to conclude by March 2023, with data analysis and study results ready for reporting by December 2023. Findings from this pilot RCT will further guide the team in planning a future large-scale study. The findings of this proposed study will establish a comprehensive knowledge base for future research and development of personalized dietary interventions to promote adherence to dietary strategies and self-management of chronic disease using the Precision Health approach for millions of Americans who are struggling with uncontrolled hypertension. ClinicalTrials.gov NCT04764253; https://clinicaltrials.gov/ct2/show/NCT04764253. DERR1-10.2196/39058.