Patient Monitoring System Research Articles

A noninvasive hyperkalemia monitoring system for dialysis patients based on a 1D-CNN model and single-lead ECG from wearable devices

This study aimed to develop a real-time, noninvasive hyperkalemia monitoring system for dialysis patients with chronic kidney disease. Hyperkalemia, common in dialysis patients, can lead to life-threatening arrhythmias or sudden death if untreated. Therefore, real-time monitoring of hyperkalemia in this population is crucial. We propose a wearable single-lead ECG monitoring system, offering enhanced comfort and feasibility for extended use. The key innovation of this system is the design of a compact, multi-channel convolutional neural network. This model offers high stability, strong performance, and exceptional computational efficiency, making it ideal for seamless integration into wearable devices for real-time monitoring applications. The model automatically extracts features from ECG signals at different frequencies through multiple convolutional channels, eliminating the need for manual feature extraction before data input. Data is input using a non-overlapping sliding window approach, reducing preprocessing complexity while maintaining model performance. We investigated the optimal window length and the number of convolution channels for ECG signal input. Experimental results indicate that the model achieves optimal performance with a 1200 ms window length and four parallel convolutional branches, yielding an accuracy of 98.25% (4.52%), F1-score of 98.31% (3.26%), sensitivity of 98.63% (2.41%), and specificity of 97.88% (5.13%). This system holds significant potential for improving patient monitoring comfort and real-time responsiveness.

Enhancing Real-Time Patient Monitoring in Intensive Care Units with Deep Learning and the Internet of Things.

The demand for intensive care units (ICUs) is steadily increasing, yet there is a relative shortage of medical staff to meet this need. Intensive care work is inherently heavy and stressful, highlighting the importance of optimizing these units' working conditions and processes. Such optimization is crucial for enhancing work efficiency and elevating the level of diagnosis and treatment provided in ICUs. The intelligent ICU concept represents a novel ward management model that has emerged through advancements in modern science and technology. This includes communication technology, the Internet of Things (IoT), artificial intelligence (AI), robotics, and big data analytics. By leveraging these technologies, the intelligent ICU aims to significantly reduce potential risks associated with human error and improve patient monitoring and treatment outcomes. Deep learning (DL) and IoT technologies have huge potential to revolutionize the surveillance of patients in the ICUs due to the critical and complex nature of their conditions. This article provides an overview of the most recent research and applications of linical data for critically ill patients, with a focus on the execution of AI. In the ICU, seamless and continuous monitoring is critical, as even little delays in patient care decision-making can result in irreparable repercussions or death. This article looks at how modern technologies like DL and the IoT can improve patient monitoring, clinical results, and ICU processes. Furthermore, it investigates the function of wearable and advanced health sensors coupled with IoT networking systems, which enable the secure connection and analysis of various forms of patient data for predictive and remote analysis by medical professionals. By assessing existing patient monitoring systems, outlining the roles of DL and IoT, and analyzing the benefits and limitations of their integration, this study hopes to shed light on the future of ICU patient care and identify opportunities for further research.

Effectiveness of Remote Patient Monitoring Equipped With an Early Warning System in Tertiary Care Hospital Wards: Retrospective Cohort Study.

Monitoring vital signs in hospitalized patients is crucial for evaluating their clinical condition. While early warning scores like the modified early warning score (MEWS) are typically calculated 3 to 4 times daily through spot checks, they might not promptly identify early deterioration. Leveraging technologies that provide continuous monitoring of vital signs, combined with an early warning system, has the potential to identify clinical deterioration sooner. This approach empowers health care providers to intervene promptly and effectively. This study aimed to assess the impact of a Remote Patient Monitoring System (RPMS) with an automated early warning system (R-EWS) on patient safety in noncritical care at a tertiary hospital. R-EWS performance was compared with a simulated Modified Early Warning System (S-MEWS) and a simulated threshold-based alert system (S-Threshold). Patient outcomes, including intensive care unit (ICU) transfers due to deterioration and discharges for nondeteriorating cases, were analyzed in Ramaiah Memorial Hospital's general wards with RPMS. Sensitivity, specificity, chi-square test for alert frequency distribution equality, and the average time from the first alert to ICU transfer in the last 24 hours was determined. Alert and patient distribution by tiers and vitals in R-EWS groups were examined. Analyzing 905 patients, including 38 with deteriorations, R-EWS, S-Threshold, and S-MEWS generated more alerts for deteriorating cases. R-EWS showed high sensitivity (97.37%) and low specificity (23.41%), S-Threshold had perfect sensitivity (100%) but low specificity (0.46%), and S-MEWS demonstrated moderate sensitivity (47.37%) and high specificity (81.31%). The average time from initial alert to clinical deterioration was at least 18 hours for RPMS and S-Threshold in deteriorating participants. R-EWS had increased alert frequency and a higher proportion of critical alerts for deteriorating cases. This study underscores R-EWS role in early deterioration detection, emphasizing timely interventions for improved patient outcomes. Continuous monitoring enhances patient safety and optimizes care quality.

Accuracy and feasibility of continuous glucose monitoring system in pancreatectomy patients.

Pancreatectomy patients often experience challenging fluctuations in blood glucose levels; therefore, they require a reliable monitoring system. This study aimed to determine the accuracy and acceptability of a continuous glucose monitoring (CGM) system compared with the intermittent capillary glucose test in patients who have undergone pancreatectomy. Thirty non-diabetic pancreatectomy patients participated. We used the FreeStyle Libre Flash Glucose Monitoring System (Abbott Diabetes Care) for continuous interstitial glucose monitoring. Capillary reference glucose levels were checked four times daily. Accuracy was checked using the Clarke Error Grid. The mean age of the participants was 56.8 ± 12.0 years, of whom 61.3% underwent pancreaticoduodenectomy and 38.7% underwent distal pancreatectomy. Three patients developed pancreatogenic diabetes after pancreatectomy. The clinical accuracy of continuous glucose monitoring compared with capillary glucose was 43.9% in Zone A (clinically accurate zone) and 99.8% in Zone A + B (low risk of error) of the Clarke Error Grid. No device-related adverse events were reported. Patients rated favorable user acceptability on the questionnaire. This pilot study demonstrated that the CGM device is accurate and safe for patients who underwent pancreatectomy, with favorable user acceptability. Despite these challenges, the study proposes that the CGM device is beneficial for monitoring glucose levels after discharge in patients with impaired glucose levels following pancreatectomy.

Smart product service systems for remote patient monitoring under uncertainty: A hierarchical framework from a healthcare provider perspective

Smart product service systems for remote patient monitoring under uncertainty: A hierarchical framework from a healthcare provider perspective

Remote monitoring of vital signs: pulse, temperature, and oxygen saturation using cloud computing

Telemedicine has gained significant relevance by allowing patients to be attended to at any time and place, reducing the cost and time of medical care. Therefore, this research aimed at the development of a remote monitoring system for vital signs using cloud computing. Through surveys and expert input, specific system requirements were identified. The chosen sensors, MAX30102 and MLX90614, accurately captured pulse, oxygen saturation, and body temperature. The Arduino Mega-Embedded board facilitated data acquisition, while the Raspberry Pi Zero W enabled remote data transmission. The ThingSpeak platform was used for communication, and the system architecture was established through hardware and software tests. The system’s functionality was verified through expert judgment and statistical tests, showing no significant differences compared to commercial equipment. The remote sampling time for vital sign data was approximately 117 seconds. Overall, this study successfully implemented a robust and efficient remote monitoring system for patients undergoing home treatment.

Remote Patient Monitoring System for Enhanced Care with IOT Devices

Remote Patient Monitoring System for Enhanced Care with IOT Devices

User Experience Study of the Patient Monitoring Systems Based on Usability Testing and Eye Tracking

Background/Objectives: The patient monitoring system is a critical tool commonly used in hospitals, making it essential to assess caregivers’ user requirements and satisfaction with its usability. In intensive care units (ICUs), the usability of these systems is closely linked to the work efficiency of key users, such as nurses, and directly impacts patient safety and treatment outcomes. This study evaluates the usability of patient monitoring systems in intensive care units (ICUs), focusing on user requirements and satisfaction among nurses, the primary users. Usability is directly linked to work efficiency and patient safety, with post-marketing surveillance (PMS) data from overseas, highlighting issues such as unrecognized alarms, leading to worsened patient conditions. Methods: This study involved 22 ICU nurses who had used the system for over a year, assessing usability through testing, satisfaction surveys, the Health-ITUES, and eye-tracking analysis. Results: The results showed a high success rate (94%) and positive satisfaction scores (4.15, SD = 0.88), with a Health-ITUES score of 4.13 (SD = 0.78). Eye tracking revealed that some functions, including alarms, were overlooked or not recognized. Conclusions: Recommendations include improving the interface for alarm messages and recording deletion functions to enhance user satisfaction and patient safety.

Efficacy of an Internet of Things-based system for cardiac rehabilitation monitoring: insights from the IntellIoT pivotal trial in heart failure patients

Abstract Aims Digital health solutions targeted to remote clinical monitoring are constantly gaining ground in cardiovascular care. However, evidence regarding their impact on cardiac rehabilitation efficiency in heart failure (HF) patients is relatively limited. In this study, conducted in the context of the IntellIoT project, we evaluated the effect of a purpose-designed Internet of Things (IoT)-based patient monitoring system on cardiac rehabilitation outcomes in a cohort of HF patients. Methods and results Nineteen clinically stable HF patients were enrolled in the study, which consisted of a 12-month standard-of-care run-in phase and a remote follow-up phase of equal duration, whereby an IoT-based e-health system was provided to study subjects. Device-derived data transmission was facilitated by a mobile phone application, coupled with a web-based platform accessible to study physicians. Study endpoints were (i) patient adherence rates to e-health system use and their associations to key clinical parameters, (ii) the degree of change in physical activity, and (iii) total time dedicated by physicians to enrolled patients’ care with and without the aid of the e-health system. Baseline-to-peak increase in daily step count was calculated at 23.34%. System use was associated with a decrease in time dedicated by physicians to enrolled patients’ care. A significant negative correlation was observed between age and progressive drop-in adherence rate to system use (r = −0.5722, P = 0.02). Conclusion Internet of Things-based healthcare constitutes a promising approach in HF patients’ rehabilitation, whereas elderly patients might constitute the population most likely to benefit. However, larger, randomized studies are required to confirm our findings.

A Smart Motor Rehabilitation System Based on the Internet of Things and Humanoid Robotics

The Internet of Things (IoT) is gaining increasing attention in healthcare due to its potential to enable continuous monitoring of patients, both at home and in controlled medical environments. In this paper, we explore the integration of IoT with human-robotics in the context of motor rehabilitation for groups of patients performing moderate physical routines, focused on balance, stretching, and posture. Specifically, we propose the I-TROPHYTS framework, which introduces a step-change in motor rehabilitation by advancing towards more sustainable medical services and personalized diagnostics. Our framework leverages wearable sensors to monitor patients’ vital signs and edge computing to detect and estimate motor routines. In addition, it incorporates a humanoid robot that mimics the actions of a physiotherapist, adapting motor routines in real-time based on the patient’s condition. All data from physiotherapy sessions are modeled using an ontology, enabling automatic reasoning and planning of robot actions. In this paper, we present the architecture of the proposed framework, which spans four layers, and discuss its enabling components. Furthermore, we detail the current deployment of the IoT system for patient monitoring and automatic identification of motor routines via Machine Learning techniques. Our experimental results, collected from a group of volunteers performing balance and stretching exercises, demonstrate that we can achieve nearly 100% accuracy in distinguishing between shoulder abduction and shoulder flexion, using Inertial Measurement Unit data from wearable IoT devices placed on the wrist and elbow of the test subjects.

IoT-based Ubiquitous Healthcare System with Intelligent Approach to an Epidemic

Background:: The recent pandemic has shown its different shades across various solicitations, especially in the healthcare sector. It has a great impact on transforming the traditional healthcare architecture, which is based on the physical approaching model, into the modern or remote healthcare system. The remote healthcare approach is quite achievable now by utilizing multiple modern technological paradigms like AI, Cloud Computing, Feature Learning, the Internet of Things, etc. Accordingly, the pharmaceutical section is the most fascinating province to be inspected by medical experts in restoring the evolutionary healthcare approaches. COVID-19 has created chaos in the society for which many unexpected deaths occur due to delays in medication and the improper prognosis at an irreverent plan. As medical management applications have become ubiquitous in nature and technology-oriented, patient monitoring systems are getting more popular among medical actors. Method: The Internet of Things (IoT) has achieved the solution criteria for providing such a huge service across the globe at any time and in any place. A quite feasible and approachable framework has evolved through this work regarding hardware development and predictive patent analysis. The desired model illustrates various approaches to the development of a wearable sensor medium that will be directly attached to the body of the patients. These sensor mediums are mostly accountable for observing body parameters like blood pressure, heart rate, temperature, etc., and transmit these data to the cloud storage via various intermediate steps. The storage medium in the cloud will be storing the sensor-acquired data in a time-to-time manner for a detailed analysis. Further, the stored data will be normalized and processed across various predictive models. Results and Conclusion: The model with the best accuracy will be treated as the resultant model among the numerous predictive models deployed in the cloud. During the hardware development process, several hardware modules are discussed. After receiving sensor-acquired data, it will be processed by the cloud's multiple machine-learning models. Finally, thorough analytics will be developed based on a meticulous examination of the patients' cardinal, essential, and fundamental data and communicated to the appropriate physicians for action. This model will then be used for the data dissemination procedure, in which an alarm message will be issued to the appropriate authorities.

Implementation of Health Monitoring System for Patients using Machine Learning Algorithms

Implementation of Health Monitoring System for Patients using Machine Learning Algorithms

Deep Learning-Based Real Time Human Detection System Using LiDAR Data for Smart Healthcare Monitoring

Abstract Continuous patient monitoring is a critical component in healthcare systems to ensure patient safety and well-being. Traditionally, this monitoring requires significant oversight by healthcare professionals, making it resourceintensive. Common methods, such as cameras, often compromise patient privacy by capturing detailed visual information. LiDAR technology presents a non-intrusive alternative for continuous monitoring, offering effectiveness across various lighting conditions due to its use of laser light. It provides high-resolution depth information encoded as intensity, making it an ideal solution for maintaining patient privacy while delivering precise monitoring capabilities. LiDAR was selected as a reference method because of its precision and ability to capture detailed depth information, which are crucial for accurate monitoring. In this project, a solution that leverages LiDAR technology to enhance patient monitoring systems was introduced. By training a YOLOv5 deep learning model using transfer learning, a method for accurate human detection and tracking within rooms using data collected from a digital LiDAR sensor was used. The high accuracy of the LiDAR sensor enables precise human tracking, which is critical for timely interventions and ensuring patient safety. This approach accelerates response times, preserves privacy, ensures safety during disease outbreaks, addresses healthcare worker shortages, and facilitates efficient monitoring of multiple patients simultaneously without invasive sensors. The ultimate goal of this project is to integrate this solution into Ambient Assisted Living (AAL) systems for elderly individuals, providing them with a safer and more autonomous living environment. Our trained YOLOv5 model has demonstrated exceptional performance, achieving a Mean Average Precision (mAP) of 99.4% at an Intersection over Union (IoU) threshold of 0.5, with a Precision of 99.6% and a Recall of 99.4%.

Prognostic Factors Associated With Survival Distribution of Admission to Delayed Rapid Response Team Activation Among Deteriorating Patients: A Retrospective Study.

To investigate the prevalence of rapid response team delays, survival distribution of admission to rapid response team delay and its prognostic factors. A retrospective single-centre study. Data on rapid response team activations from 1 January 2018 to 31 December 2022 were retrieved from electronic medical records at a tertiary hospital in Hangzhou, China. All patients who met the eligibility criteria were included. Multivariable Cox regression analysis was conducted to analyse the data. Out of 636 patients included, 18.4% (117) experienced a delay, with a median (interquartile range) of 8.5 (12) days from admission to rapid response team activation. Six significant prognostic factors were found to be associated with the higher hazard ratio of rapid response team delay, including call time (05:01 PM and 7:59 AM), emergency admission, a higher Modified Early Warning Score, an admission diagnosis of infection, a comorbidity of respiratory failure/Acute Respiratory Distress Syndrome and the absence of lung infection. The prevalence of rapid response team delays was lower, and the days from admission to rapid response team delay was longer than in previous studies. Healthcare providers are suggested to prioritise the care of high-risk patient groups and provide proactive monitoring to ensure timely identification and management. Implementing artificial intelligence in continuous monitoring systems for high-risk patients is recommended. The findings help nurses anticipate potential delays in rapid response team activation, enabling better preparedness. The study highlights the prevalence of rapid response team delays, timing from admission to rapid response team activation and six prognostic factors influencing delays. It could shape patient care and inform future research. Hospital administrators should review staffing, especially during night shifts, to minimise delays. Further qualitative research is needed to explore why nurses may delay rapid response team activation. The STROBE checklist was adhered to when reporting this study. 'No patient or public contribution'.

Development of a low-cost IoT-based e-health monitoring system for diabetic patients

The global need for diabetic patient self-care is increasing. The current global epidemiology of diabetes calls for an improved healthcare management system, particularly in poorer countries, to minimize the burden of diabetes complications and mortality. The main objective of this study seeks to increase diabetic patients’ access to healthcare by utilizing internet of things-based smart self-care high-tech devices. The proposed model was integrated with a Blynk software and hosted in NodeMCU, allowing user to access diabetic health metric from the smartphones or interconnected devices. The e-monitoring system has an inbuilt feedback mechanism that provides online access to healthcare provider for immediate insulin therapy. Furthermore, patients suffering from acute diabetic metabolic issues may have difficulty visiting the hospital. Therefore, they can use the proposed model to track and monitor their blood glucose levels, as well as communicate remotely with their doctor, so improving their health while saving money and time. Also, diabetic patients can use this system to record their health condition on their phone, decreasing the need for doctor visits and clinical medical procedure. The proposed system has several advantages, including speedy internet access and remote monitoring from anywhere in the world. This research also helps to achieve the United Nations’ Sustainable Development Goals, which include good health and well-being, equitable healthcare access, and sustainable cities and communities.

A Remote Patient Monitoring System With Feedback Mechanisms Using a Smartwatch: Concept, Implementation, and Evaluation Based on the activeDCM Randomized Controlled Trial.

Technological advances allow for recording and sharing health-related data in a patient-centric way using smartphones and wearables. Secure sharing of such patient-generated data with physicians would enable close management of individual health trajectories, monitoring of risk factors, and asynchronous feedback. However, most remote patient monitoring (RPM) systems currently available are not fully integrated into hospital IT systems or lack a patient-centric design. The objective of this study was to conceptualize and implement a user-friendly, reusable, interoperable, and secure RPM system incorporating asynchronous feedback mechanisms using a broadly available consumer wearable (Apple Watch). In addition, this study sought to evaluate factors influencing patient acceptance of such systems. The RPM system requirements were established through focus group sessions. Subsequently, a system concept was designed and implemented using an iterative approach ensuring technical feasibility from the beginning. To assess clinical feasibility, the system was used as part of the activeDCM prospective randomized interventional study focusing on dilated cardiomyopathy. Each patient used the system for at least 12 months. The System Usability Scale was used to measure usability from a subjective patient perspective. In addition, an evaluation was conducted on the objective wearable interaction frequency as well as the completeness of transmitted data classified into sensor-based health data (SHD) and patient-reported outcome measures (PROMs). Descriptive statistics using box plots and bootstrapped multiple linear regression with 95% CIs were used for evaluation analyzing the influence of age, sex, device experience, and intervention group membership. The RPM system comprised 4 interoperable components: patient devices, a data server, a data viewer, and a notification service. The system was evaluated with 95 consecutive patients with dilated cardiomyopathy (28/95, 29% female; mean age 50, SD 12 y) who completed the activeDCM study protocol. The system's app achieved a mean System Usability Scale score of 78 (SD 17), which was most influenced by device experience. In total, 87% (83/95) of the patients could integrate the use of the app well or very well into their daily routine, and 71% (67/95) saw a benefit of the RPM system for management of their health condition. On average, patients interacted with the wearable on 61% (SD 26%) of days enrolled in the study. SHD were available on average for 78% (SD 23%) of days, and PROM data were available on 64% (SD 27%) of weeks enrolled in the study. Wearable interaction frequency, SHD, and PROM completeness were most influenced by intervention group membership. Our results mark a first step toward integrating RPM systems based on a consumer wearable device for primary patient input into standardized clinical workflows. They can serve as a blueprint for creating a user-friendly, reusable, interoperable, and secure RPM system that can be integrated into patients' daily routines.

Digital continuous glucose monitoring systems for patients with HIV-diabetes comorbidity in Ethiopia: a situational analysis

In patients with HIV-diabetes mellitus (DM) comorbidity, invasive blood glucose testing can increase the risk of HIV-related blood contamination and discourage regular glucose monitoring. Digital continuous glucose monitoring (CGM) systems may allow real-time glucose monitoring without the need for blood specimens. However, in high-burden HIV-DM countries, current glucose monitoring practices and their challenges are insufficiently explored to guide digital CGM research and developments. This study sought to explore the lived experiences of patients with HIV-DM comorbidity and their healthcare providers regarding glucose monitoring practices, and their openness to CGM and other digital technologies, to provide formative insights for a planned implementation trial of digital CGM in Ethiopia. A phenomenological qualitative study was conducted among patients with HIV-DM and their providers at the two largest public hospitals in Ethiopia. Both groups were interviewed face-to-face about DM clinic workflows, blood glucose monitoring and self-testing practices, and potential benefits and limitations of digital CGM systems. Interviews were audio-recorded, transcribed verbatim, and analyzed thematically. A total of 37 participants were interviewed, consisting of 18 patients with HIV-DM comorbidity and 19 healthcare providers. Patients had an average (min-max) duration of living with HIV and DM of 14 (8–31) and 6.6 (1–16) years, respectively, with 61% taking insulin—33% alone and 28% alongside oral hypoglycemic agents—and 79% having comorbid hypertension. The thematic analysis identified five main themes: “Diabetes routine clinical care and follow-up”, “Blood glucose monitoring practices”, “Perceptions about digital CGMs”, “Technology adoption”, and “Financial coverage”. Home self-testing was deemed beneficial, but the need for regular follow-ups, result cross-referencing, and glucometer reliability were emphasized. Patients performed fingerstick themselves or with family members, expressing concerns about waste disposal and the risk of HIV transmission. They rely mainly on health insurance for DM care. Patients and providers are happy with the quality of DM services but note a lack of integrated HIV-DM care. Very few providers and patients possessed background information about digital CGMs, and all have not yet utilized them in practice, but expressed keen interest in trying them, representing an important step for upcoming CGM clinical trials in these settings. Given the crucial role of regular glucose testing in managing HIV-DM comorbidity, it is essential to explore testing options that align with patient preferences and minimize the risk of HIV transmission.

Evaluation of IMPALA 2.0: Addressing Patient Monitoring in Low-Resource Hospitals in Malawi.

BACKGROUND Patient monitoring systems (PMSs) are essential for monitoring and managing the condition of critically ill patients. In low-resource settings, limited access to technology, low-level digital literacy, and power outage challenges are usability concerns. The main aim of this study was to evaluate the usability of the IMPALA (Innovative Monitoring in Paediatrics in Low-resource settings: an Aid to save lives) PMS optimized for use in low-resource settings by assessing the opinions and experiences of 24 healthcare professionals. MATERIAL AND METHODS The study used a mixed-method design, combining quantitative and qualitative approaches. Quantitatively, 24 participants (nurses and clinicians) completed the Usefulness, Satisfaction, and Ease of Use questionnaire to assess the PMS usability. Qualitatively, contextual inquiry and co-design sessions provided insights into users' experiences and identified usability issues. Data were analyzed using descriptive statistics and thematic analysis. RESULTS The PMS was rated 9.13 for usefulness, 8.49 for user satisfaction, 7.83 for ease of use, and 7.60 for ease of learning. Reported challenges included lack of knowledge/skills due to limited previous exposure (70.8%), frequent sensor detachment (58.3%), inaccurate SpO₂ readings (37.5%), and frequent/false alarms (33.3%). Contextual inquiry revealed that patient movement and poorly fitting sensors often caused inaccurate readings, leading to false alarms and potential patient safety risks. CONCLUSIONS Successful implementation of PMSs in a low-resource setting requires specific contextual user-centered design and training. Applying this, the IMPALA system yielded high usability scores. Further improvement should focus on expanded battery life, robust and durable SpO₂ sensors, and tailored training methods.

Evaluation of the Design of Hemodialysis Patient Monitoring System at Lestari Kidney and Hypertension Clinic Semarang

Introduction: Patients diagnosed with kidney disease are very difficult to accept and are not ready to undergo haemodialysis treatment. Objective: Effectiveness is needed to make patients with chronic kidney disease more obedient to treatment and to ask about kidney treatment and important health information for kidney failure patients. Method: The aim of this research is to evaluate the design of a patient's haemodialysis monitoring system in the Gingival Clinic and the Pressure of Current Lester, by conducting research based on methods and qualitative descriptive types of research. Result: Research has shown that Clinical Kidney and Concurrent Lestari Hypertension require a haemodialysis patient monitoring system to facilitate healthcare in patient monitoring and facilitate physicians in decision making.

Safety of electrophysiological study in atrioventricular block risk stratification in new-onset persistent left bundle brunch block following transcathether aortic valve implantation

Abstract Background A significant percentage of patients develops a persistent complete left bundle branch block (LBBB) following TAVI. Electrophysiological (EP) study could be useful to stratify the risk of atrioventricular block (AVB) in these patients, guiding the need of follow-up and the indication of definite pacemaker. Purpose The primary objective of the study was describing the incidence of bradyarrhythmic events in the 30-day follow-up of patients with new-onset LBBB: AV block with ECG long-term monitoring system (Nuubo) in patients with negative EP study, and ≥ 5 % of ventricular stimulation in patients with positive EP study and pacemaker implantation. Methodology Observational prospective study of consecutive patients without previous permanent pacemaker implantation where transfemoral TAVI was implanted between 2021 and 2023. In case new-onset complete LBBB was present after TAVI, an EP study was performed. EP study was considered positive when His-Ventricule interval was more than 70ms, or after procainamide infusion at 10mg/kg, more than 100ms or more than double the basal HV interval. In case EP was positive, a permanent pacemaker was implanted. In case EP was negative, a 30-day follow-up with ECG long-term monitoring system (Nuubo) was performed. Results In this cohort, 266 patients without pacemaker following TAVI were included. Persistent new-onset left bundle branch block was present in 10,15% of patients (n=27). EP study was positive in 29.62% of patients (n=8), following a definite pacemaker implantation. During follow-up, only one patient (12.5%) was dependent of ventricular stimulation. The group with negative EP study (n=19) were monitored with a 30-day ECG long-term monitoring system (Nuubo). No bradyarrhythmic events were registered in this group. Conclusions Approximately one tenth of patients develop persistent new-onset LBBB after TAVI. Events were seldom in both study groups. Positive EP study patients were dependent of ventricular stimulation in 12.5% of cases, and there was no bradyarrhythmic event registered in EP negative group. EP study could be a safe tool to stratify AVB risk in new-onset LBBB following TAVI.