Remote Monitoring Data Research Articles

IoT based low cost and intelligent module for smart irrigation system

Agriculture contributes to a major share in the Indian economy and most of its people are dependent on it for their livelihood. This makes water an important resource that needs to be preserved using the latest available technologies. Apart from being fundamental in industry 4.0, IoT also extends its capability to smart farming. Work proposed here targets to develop a low cost intelligent system for smart irrigation. It uses IoT to make devices used in the system to talk and connect on their own, with capabilities like: admin mode for user interaction, one-time setup for irrigation schedule estimation, neural based decision making for intelligent support and remote data monitoring. A sample crop test-bed has been chosen to present results of the proposed system, that include irrigation schedule, neural net decision making and remote data viewing. Neural network provides required intelligence to the device that considers current sensor input and masks the irrigation schedule for efficient irrigation. The system uses MQTT and HTTP to keep the user informed about the current crop situation even from a distant location. The proposed system proves beneficial with its intelligence, low cost and portability, making it suitable for greenhouse, farms, etc.

Prediction of grey-catastrophe destabilization time of a granite residual soil slope under rainfall

The stability of a residual soil slope is a system of grey and white, and gradual change and sudden change. It is difficult to accurately forecast the instability time of a granite residual soil slope, because the factors affecting the instability of granite residual soil slopes are random and uncontrollable. Using the grey prediction method, the accumulation generation of data weakens the influence of random disturbance factors in the original sequence, and enhances the regularity of data. In this paper, we proposed a grey cusp catastrophe prediction model to calculate the instability time of granite residual soil slopes under rainfall. We took one-time cumulative transformation of the remote monitoring data of slope displacement, and then conducted polynomial regression fitting on the processed displacement data and monitoring time. The grey cusp catastrophe instability prediction model of granite residual soil slopes was established using the nonlinear dynamic catastrophic prediction theory. The critical instability time of the slope was predicted using the grey catastrophe instability prediction method presented in this paper. For comparison, we conducted exponential curve fitting on the displacement data and monitoring time, and then established the Saito instability forecasting method of a soil slope to predict the slope instability. Through the engineering application, and using the presented grey catastrophe slope instability prediction method, the slope instability process may be accurately predicted. The calculation results using the grey catastrophe slope instability prediction method presented in this paper are in good agreement with the actual situation. Whether it can be used for other slopes remains to be further studied.

Abstract 119: Using the VA National Cardiac Device Surveillance Program to Monitor the Safety of Cardiovascular Implantable Electronic Devices

Background: Hundreds of thousands of cardiovascular implantable electronic devices (CIEDs: pacemakers and implantable-cardioverter-defibrillators [ICDs]) have been the subject of Class 1 FDA recalls, which indicate “a reasonable chance that a product will cause serious health problems or death.” Unfortunately, these recalls occurred only after many patients had experienced adverse events. The Department of Veterans Affairs (VA) has established the VA National Cardiac Device Surveillance Program (VANCDSP), a prospective registry that provides remote monitoring surveillance of enrolled veterans with CIEDs. Objective: To describe the characteristics of veterans enrolled in the VANCDSP between 2002 - 2018 and the prevalence of CIED recalls among currently monitored patients. Methods: We queried the VANCDSP and used descriptive statistics to summarize the characteristics of patients, use of remote monitoring, and prevalence of recalls or alerts for CIED generators and leads. Patient and device characteristics are entered by local VAs when they request remote monitoring. Follow-up data were obtained from scheduled and patient- or alert-initiated remote monitoring transmissions, which are adjudicated and continuously uploaded. Results: VANCDSP has enrolled 197,642 veterans with CIEDs. Of all veterans, 72,571 have had ICDs and 128,808 pacemakers, while 3737 have had both (almost always upgrades from pacemakers to ICDs); 15.2% were implanted outside of VA. A total 351,836 leads and 260,636 generators have been monitored. On average, 1.32 generators and 1.78 leads have been implanted per patient. Of the 72,571 patients implanted with ICDs, 52,549 (72.4%) have available remote monitoring data, with a total of 833,345 transmissions (mean 15.9 [SD 16.2] per patient). Of 128,808 patients implanted with pacemakers, 33,510 (26.0%) have available remote monitoring data, with a total of 356,528 transmissions (mean 10.6 [SD 12.1] per patient), along with 1.3 million trans-telephonic monitoring transmissions. Among the 197,642 enrolled veterans, 51,713 (26%) are currently monitored (nearly all others have died). The mean age of these patients is 72 years and 98.3% are male. The median duration of follow-up from initial implant to most recent RM transmission is 3.44 years. A total of 158 FDA recalls or alerts for lead or generator safety concerns currently affect 13,835 devices implanted in 11,359 (22%) currently monitored patients. This database can also be linked to the VA electronic health record for longitudinal outcome ascertainment. Conclusion: The VANCDSP contains data since 2002 on 197,642 veterans implanted with CIEDs, including 51,713 currently monitored patients. Safety recalls or alerts affect 22% of currently monitored patients. VANCDSP could serve as an early warning system for lead or generator failures and outcomes assessment of alerted or recalled leads or generators.

Data Integration and Interoperability for Patient-Centered Remote Monitoring of Cardiovascular Implantable Electronic Devices.

The prevalence of cardiovascular implantable electronic devices with remote monitoring capabilities continues to grow, resulting in increased volume and complexity of biomedical data. These data can provide diagnostic information for timely intervention and maintenance of implanted devices, improving quality of care. Current remote monitoring procedures do not utilize device diagnostics to their potential, due to the lack of interoperability and data integration among proprietary systems and electronic medical record platforms. However, the development of a technical framework that standardizes the data and improves interoperability shows promise for improving remote monitoring. Along with encouraging the implementation of this framework, we challenge the current paradigm and propose leveraging the framework to provide patients with their remote monitoring data. Patient-centered remote monitoring may empower patients and improve collaboration and care with health care providers. In this paper, we describe the implementation of technology to deliver remote monitoring data to patients in two recent studies. Our body of work explains the potential for developing a patent-facing information display that affords the meaningful use of implantable device data and enhances interactions with providers. This paradigm shift in remote monitoring—empowering the patient with data—is critical to using the vast amount of complex and clinically relevant biomedical data captured and transmitted by implantable devices to full potential.

Practice Variation in Anticoagulation Prescription and Outcomes After Device-Detected Atrial Fibrillation.

Device-detected atrial fibrillation (AF) is associated with increased risk of stroke; however, there are no clearly defined thresholds of AF burden at which to initiate oral anticoagulation (OAC). We sought to describe OAC prescription practice variation in response to new device-detected AF and the association with outcomes. We performed a retrospective cohort study using data from the Veterans Health Administration linked to remote monitoring data that included day-level AF burden. We included patients with cardiac implantable electronic devices and remote monitoring from 2011 to 2014, CHA2DS2-VASc score ≥2, and no prior stroke or OAC receipt in the preceding 2 years. We determined the proportion of patients prescribed OAC within 90 days after new device-detected AF across a range of AF thresholds (≥6 minutes to >24 hours) and examined site variation in OAC prescription. We used multivariable Cox proportional hazards regressions to determine the association of OAC prescription with stroke by device-detected AF burden. Among 10 212 patients with cardiac implantable electronic devices, 4570 (45%), 3969 (39%), 3263 (32%), and 2469 (24%) had device-detected AF >6 minutes, >1 hour, >6 hours, and >24 hours, respectively. For device-detected AF >1 hour, 1712 patients met inclusion criteria (72±10 years; 1.5% female; CHA2DS2-VASc score 4.0±1.4; HAS-BLED score 2.6±1.1). The proportion receiving OAC varied based on device-detected AF burden (≥6 minutes: 272/2101 [13%]; >1 hour: 273/1712 [16%]; >6 hours: 263/1279 [21%]; >24 hours: 224/818 [27%]). Across 52 sites (N=1329 patients), there was substantial site-level variation in OAC prescription after device-detected AF >1 hour (median, 16%; range, 3%-67%; median odds ratio, 1.56 [95% credible interval, 1.49-1.71]). In adjusted models, OAC prescription after device-detected AF >24 hours was associated with reduced stroke risk (hazard ratio, 0.28; 95% CI, 0.10-0.81; P=0.02), although the propensity-adjusted model was significant when AF lasted at least 6 minutes. Among veterans with cardiac implantable electronic devices, device-detected AF is common. There is large practice variation in 90-day OAC initiation after new device-detected AF, with low rates of treatment overall, even for episodes that last >24 hours. The strongest association of OAC with reduction in stroke was observed after device-detected AF >24 hours. Randomized trials are needed to confirm these observational findings.

Internet of Things Based Smart Greenhouse: Remote Monitoring and Automatic Control

Conventional greenhouses need regulated climate conditions to grow plants in there. Without an automatic control system, it is quite impossible to maintain the accurate climate conditions inside the green houses. This paper proposes such an automatic system for greenhouses to control its’ internal environment, to store each climate properties in a database for future analysis and to ensure remote monitoring to those data. Internet of Things (IoT) is used for remote monitoring and analysis of data. Also, an android application is developed to display those data in a short range through Bluetooth technology. The system controls the temperature, humidity, light and soil moisture level by sensing the values from sensors and controlling heaters/coolers, sprayers, bulbs and water pumps accordingly.

The Connected Intensive Care Unit Patient: Exploratory Analyses and Cohort Discovery From a Critical Care Telemedicine Database.

BackgroundMany intensive care units (ICUs) utilize telemedicine in response to an expanding critical care patient population, off-hours coverage, and intensivist shortages, particularly in rural facilities. Advances in digital health technologies, among other reasons, have led to the integration of active, well-networked critical care telemedicine (tele-ICU) systems across the United States, which in turn, provide the ability to generate large-scale remote monitoring data from critically ill patients.ObjectiveThe objective of this study was to explore opportunities and challenges of utilizing multisite, multimodal data acquired through critical care telemedicine. Using a publicly available tele-ICU, or electronic ICU (eICU), database, we illustrated the quality and potential uses of remote monitoring data, including cohort discovery for secondary research.MethodsExploratory analyses were performed on the eICU Collaborative Research Database that includes deidentified clinical data collected from adult patients admitted to ICUs between 2014 and 2015. Patient and ICU characteristics, top admission diagnoses, and predictions from clinical scoring systems were extracted and analyzed. Additionally, a case study on respiratory failure patients was conducted to demonstrate research prospects using tele-ICU data.ResultsThe eICU database spans more than 200 hospitals and over 139,000 ICU patients across the United States with wide-ranging clinical data and diagnoses. Although mixed medical-surgical ICU was the most common critical care setting, patients with cardiovascular conditions accounted for more than 20% of ICU stays, and those with neurological or respiratory illness accounted for nearly 15% of ICU unit stays. The case study on respiratory failure patients showed that cohort discovery using the eICU database can be highly specific, albeit potentially limiting in terms of data provenance and sparsity for certain types of clinical questions.ConclusionsLarge-scale remote monitoring data sources, such as the eICU database, have a strong potential to advance the role of critical care telemedicine by serving as a testbed for secondary research as well as for developing and testing tools, including predictive and prescriptive analytical solutions and decision support systems. The resulting tools will also inform coordination of care for critically ill patients, intensivist coverage, and the overall process of critical care telemedicine.

Remote Patient Monitoring and Telemedicine in Neonatal and Pediatric Settings: Scoping Literature Review.

BackgroundTelemedicine and telehealth solutions are emerging rapidly in health care and have the potential to decrease costs for insurers, providers, and patients in various settings. Pediatric populations that require specialty care are disadvantaged socially or economically or have chronic health conditions that will greatly benefit from results of studies utilizing telemedicine technologies. This paper examines the emerging trends in pediatric populations as part of a systematic literature review and provides a scoping review of the type, extent, and quantity of research available.ObjectiveThis paper aims to examine the role of remote patient monitoring (RPM) and telemedicine in neonatal and pediatric settings. Findings can be used to identify strengths, weaknesses, and gaps in the field. The identification of gaps will allow for interventions or research to improve health care quality and costs.MethodsA systematic literature review is being conducted to gather an adequate amount of relevant research for telehealth in pediatric populations. The fields of RPM and telemedicine are not yet very well established by the health care services sector, and definitions vary across health care systems; thus, the terms are not always defined similarly throughout the literature. Three databases were scoped for information for this specific review, and 56 papers were included for review.ResultsThree major telemedicine trends emerged from the review of 45 relevant papers—RPM, teleconsultation, and monitoring patients within the hospital, but without contact—thus, decreasing the likelihood of infection or other adverse health effects.ConclusionsWhile the current telemedicine approaches show promise, limited studied conditions and small sample sizes affect generalizability, therefore, warranting further research. The information presented can inform health care providers of the most widely implemented, studied, and effective forms of telemedicine for patients and their families and the telemedicine initiatives that are most cost efficient for health systems. While the focus of this review is to summarize some telehealth applications in pediatrics, we have also presented research studies that can inform providers about the importance of data sharing of remote monitoring data between hospitals. Further reports will be developed to inform health systems as the systematic literature review continues.

Remote monitoring data on opencast mining and disturbed land ecology in the Bakal iron ore field

Remote monitoring data on opencast mining and disturbed land ecology in the Bakal iron ore field

Environmental Monitoring of Ancient Buildings Based on a Wireless Sensor Network.

Environmental monitoring plays an important role in the preventive protection of ancient buildings, although it is still in the prototype stage. In order to provide data support for the protection of ancient buildings, an environmental monitoring system with multi-sensor and multi-node for the interior and exterior of ancient buildings is designed and realized, based on ZigBee, TCP/IP, and intranet penetration technology. The new type of indoor node package box and outdoor package device are designed to meet the needs of different types of sensors. The monitoring platform is developed on the strength of the LabView so as to obtain real-time display, storage, and over-limit warning functions for local and remote monitoring data. It also proves that the monitoring system is stable and reliable by monitoring the actual ancient building with a brick (stone) structure.

Atrial fibrillation burden, progression, and the risk of death: a case-crossover analysis in patients with cardiac implantable electronic devices.

Atrial fibrillation (AF) is associated with increased mortality, but the temporal relationship between AF burden (AFB) and death among patients with cardiac implanted electronic devices is unknown. We sought to characterize the timing and progression of AFB before death. Using Merlin.netTM remote monitoring (RM) data, we analysed weekly AFB in patients age ≥55 years implanted with dual-chamber pacemaker, implantable cardioverter-defibrillator, or cardiac resynchronization therapy devices whose death was verified in the Social Security Death Index and who had continuous RM from 1 year to 4 weeks preceding death. Atrial fibrillation burden was defined as amount of time per week atrial rate exceeded a set threshold of 180 b.p.m. Case-crossover analysis was used to compare the AFB at every week to 6 control weeks at the start of the year before death. There were 3131 patients meeting analysis criteria (age at death 76 ± 8 years, 70% men). Weekly increase in AFB >6 h was associated with increased odds of death, which was greatest at 4 weeks before death [odds ratio (OR) 2.30, 95% confidence interval (CI) 2.09-2.53; P < 0.001]. Atrial fibrillation progression week-to-week >24 h was associated with the greatest odds of death (OR 12.95, 95% 8.72-19.22; P < 0.001). A combination of AFB >6 h per week and activity <0.5 h per day was associated with an increased odds of death. In this large patient cohort, AFB progression accelerated in the weeks leading to death. Continuous monitoring of AFB may help identify device patients who may be at risk for adverse outcomes, including death.

Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge

Abstract Assessing the presence and abundance of birds is important for monitoring specific species as well as overall ecosystem health. Many birds are most readily detected by their sounds, and thus, passive acoustic monitoring is highly appropriate. Yet acoustic monitoring is often held back by practical limitations such as the need for manual configuration, reliance on example sound libraries, low accuracy, low robustness, and limited ability to generalise to novel acoustic conditions. Here, we report outcomes from a collaborative data challenge. We present new acoustic monitoring datasets, summarise the machine learning techniques proposed by challenge teams, conduct detailed performance evaluation, and discuss how such approaches to detection can be integrated into remote monitoring projects. Multiple methods were able to attain performance of around 88% area under the receiver operating characteristic (ROC) curve (AUC), much higher performance than previous general‐purpose methods. With modern machine learning, including deep learning, general‐purpose acoustic bird detection can achieve very high retrieval rates in remote monitoring data, with no manual recalibration, and no pretraining of the detector for the target species or the acoustic conditions in the target environment.

A multi-parameter grain detection system based on industry 4.0: Poster

A multi-parameter grain detection system based on industry 4.0 was used to map all kinds of sensors and devices into multiple network addresses through the integration of equipment, to realize the local visual perception and the network transmission of various grain data, using software plug-in architecture technology to build online extension of the software to achieve the corresponding grain multi-parameter monitoring plug-ins; setting sensor and device communication protocol standards to achieve remote monitoring of various grain situation data on the scene equipment Remote debugging and maintenance work to form a remote data center and equipment maintenance center. The system is compatible with a wide range of heterogeneous sensors and devices online and with a high degree of online scalability.

Assessment of usability and viability in clinical practice of the Nabelia mHealthCare Platform to support integrated care management of patients with pulmonary hypertension

Introduction : Pulmonary hypertension (PH) is a rare, life-threating disease that requires complex long term management, often involving coordination of actions across different agencies and jurisdictions. This is a challenging scenario to the effective delivery of care. Adoption of an integrated care (IC) approach with a sensible use of enabling technology can be a way of successfully dealing with this complexity. Aims : a) To assess the usability and potential feasibility in clinical practice of the Nabelia mHealthCare Platform, in the IC management of patients with Pulmonary Hypertension and; b) to identify areas in need of improvement in collaboration with patients and professionals Material and method : This qualitative research study involved two distinct groups chosen for convenience: 1) Patients’ group, patients with a pulmonary arterial hypertension (HAP) diagnose and/or chronic thromboembolic hypertension (HPTEC); and, 2) Health professionals’ group. In both groups, we carried out : 1) user tests followed by administration of a questionnaire (adaptation of the SUMI questionnaire items); 2) Focus groups sessions (1 patients, 1 professionals). A total of 23 people participated in this study (12 patients, 7 practitioners and 4 nurses). Informed consent was obtained from all participants. The study was completed by a heuristic assessment of the platform that was undertaken by a specially trained researcher. Results : Professionals highlighted that the Platform facilitated: 1) the sharing of information (notably in the case of treatment prescribed), 2) coordination of actions (remote monitoring data) and 3) communication with patients. Patients’ appraisals of the platform differed. Globally, it was viewed as an interesting tool and patients especially valued the accessibility to the information about their condition and the easiness to share this information with the different health professionals. Perceptions on the benefits of using the mobile application to manage everyday life depended on patients’ age and treatments’ complexity. Thus, younger patients were more favorable towards its use while elders were more reluctant. Heuristic evaluation. The aspects that were best ranked where those related with the clarity of the interface (adequate management of spaces and text, visual structure of the information, easily recognizable links), consistency in its design (elements, actions, language, navigation), and architecture (organizational system, elements and terms used in the navigation menu). Usability elements in need of attention were those related with system status, information flow, aid in its actions, clarity of information, control by user, readability and configuration. The application is easy to learn and uses standard tags and a language close to professionals with helpful graphics though in some parts readability could be improved. Control of the application by the user was acceptable at the current status of development. Conclusions : At its current stage of development the Nabelia mHealthCare Platform has a good usability and can be potentially applied in the management of patients with pulmonary hypertension. Professionals and patients think that the application facilitates the management of the disease, allows closer follow-up and monitoring and improves coordination across levels of health care. Attention to acceptance and usability issues in the group of older patients is, however, required.

Delivering remote monitoring data to patients with implantable cardioverter-defibrillators: Does medium matter?

This study presents findings on the impact of providing patients with their implantable device data on patients' satisfaction, engagement, healthcare utilization, and provider's perceptions of this practice. Remote monitoring of implantable cardioverter defibrillators (ICDs) improves patient care through timely delivery of ICD data to the clinic. However, patients usually do not receive their ICD data. Providers are concerned that messaging patients with their device data could increase clinical workload. To evaluate the impact of sharing ICD data summary through the patient portal, 128patients were randomized into two groups. Group A and B received their data through the patient portal and postal mail, respectively. Data were collected through surveys and medical record reviews. Forty-eight providers also completed a survey. At the end of the study, at least two-thirds of patients were satisfied with the amount of information received through the electronic or paper ICD data summary. Additionally, providing patients with their device data did not increase ICD-specific clinical workload (e.g., calls or messages to provider). While providers did not foresee the provision of ICD data to patients as decreasing or improving clinical workload, they did see potential value in it for patients. Providing patients with their ICD data (through paper or electronic means) might have the potential to improve patient satisfaction and enhance shared-decision making without adversely impacting clinical workload.

METHOD FOR PLANNING NON-DETERMINED OPERATION PROCESSES OF RAILWAY TECHNICAL SYSTEM PARK

Purpose. The article is aimed to improve the automated systems operation of the railway technical system parks and switch D.C. electric motors (EMs), taking into account all uncertainties. Methodology. Solution of the problem was obtained through the development of the model and the method for optimal planning for the EMs set operation. The method is based on the information technology with the possibility to assess the parameters of the current and the predicted state of EMs based on their individual models. The models are built both for individual EMs and for the specified groups. The factors of non-determinism in the model are calculated based on the Hurst index. The task of planning is solved as calculating the optimal sequence of the EM facilities services, which provides a minimum of the total expected operating costs. Findings. The analysis of the main known models, the automated technologies and the systems of EM (ASEM) park operation on the basis of the remote monitoring was done in the research. Based on the practice of the EM park maintenance the new category of the analysis objects was proposed – the service group (SG). The new procedure for the processes classification was developed based on using the Hurst index to improve the reliability of EM and SG individual models forecasting. The technological and the economic model for planning the EM parks operation was created. The article presents the results of the developed automated data management system based on the improved model for the operation planning of the D.C. EM parks. The optimal planning model ensures the minimization of the expected operating costs for the EMs operation, due to the selection of the EM groups service queue. The specialized procedure is used to classify non-deterministic EM remote monitoring data during planning, which allows increasing the accuracy of forecasting the object state parameters. Origilnality . The article describes development of the mathematical model and the information technology for the remote monitoring of the railway technical systems park operation, the railway switch EMs based on the formation of EM and SG individual models, as well as on the evaluation of their current and predicted states, taking into account random factors. The proposed model of the optimal planning as the possibility to choose the SG service queue differs by the group maintenance of the EM facilities, as well as application of the specialized procedure for classifying EM monitoring data. Practical value . The practical value of the results is determined by the provision of the new opportunities for the group optimal planning of the EM service based on the criterion of the minimum expected costs. The procedure for the monitoring data classification of the operational processes makes it possible to increase the reliability of the forecasting antipersistent time sequences results. It also provides an interpretation of the observational data classification results based on the need for practical usage.

A Patient-Centered Approach Towards Designing a Novel CIED Remote Monitoring Report

Background Individuals with cardiac implantable electronic devices (CIEDs) currently are not given access to their remote monitoring data. The information is managed by their cardiology clinic and each center receives a high-level notification if serious abnormalities are detected in CIED function or cardiac rhythm. The ability to access one's own health information can provide an opportunity for self-reflection, engagement and shared decision making with clinicians. Objective This study sought to identify key CIED data points that patients feel would be helpful for self-management and cardiac disease awareness. Methods We conducted four focus groups with CIED patients and caregivers: two for patients with implantable cardioverter defibrillators (ICDs) (N= 13, 9 patients and 4 caregivers) and two for patients with pacemakers (N = 14, 10 patients and 4 caregivers). First, participants were educated about the types of data collected through remote monitoring. Second, participants engaged in a card-sorting activity where they were presented with a deck of cards containing all possible reported remote monitoring data points (55 data points for ICDs and 37 for pacemakers). Participants selected and prioritized the cards based on what information they would like to receive from their device. Patients sorted the cards into one of three categories: most preferred (no more than 5 cards), moderately preferred (no limit), and not preferred/discarded (no limit). Participants also indicated how frequently they would like to be updated with a data report and if they would like to receive any additional supporting information to facilitate the interpretation of this data. Results Device activity (e.g., total shocks and ventricular pacing) and cardiac episodes (e.g., monitored and treated episodes) were the top selected data categories chosen by 80% and 72% of all participants, respectively. Device settings (programmed parameters) and device information (e.g., lead impedance) were the least selected data categories discarded by 58% and 48% of participants, respectively. Through discussion, we identified the emerging reasons for discarding specific content cards, which included: insufficient understanding, unclear relevance to health condition, information overload, and difficulty interpreting numbers. Conclusion In this presentation, we will share the user-centered design methodology and resulting patient preferences for CIED data, all used to shape a novel patient-centered remote monitoring report.

Active periodic electrograms in remote monitoring of pacemaker recipients: the PREMS study.

AimsRemote monitoring (RM) is considered as a standard of care for pacemaker recipients. Remote monitoring systems provide calendar-based intracardiac electrogram recordings (IEGM) only with the current pacemaker settings (passive IEGM). PREMS (Pacemaker Remote Electrogram Monitoring Study), an observational, multicentre trial, prospectively evaluated the clinical value of an active IEGM (aIEGM), including three 10-s sections (passive IEGM, encouraged sensing, and encouraged pacing), compared to other RM data and to its passive IEGM section. Secondary objectives included the added value of the aIEGM to fully assess the sensing and pacing functions of each lead.Methods and resultsPatients were enrolled within 3 months after pacemaker implantation and followed until the first transmitted aIEGM, which was analysed together with all other RM data. In total, 567 patients were enrolled (79 ± 9 years, 62% men, 19% single-chamber, and 81% dual-chamber pacemakers). Of 547 aIEGMs transmitted in 547 patients, 161 [29.4%; 95% confidence interval (95% CI) 25.6–33.3%] indicated at least one anomaly non-detectable with certainty—or at all—on other RM data, including atrial arrhythmia, extrasystoles, undersensing, oversensing, and loss of capture. In 21.7% of cases the detected events deserved a corrective action. The sensing and pacing function of each lead could be fully assessed in 77.3% of aIEGM (95% CI 72.6–82.0%) vs. 15.5% (95% CI 11.4–19.6%) when considering only the passive IEGM section (P < 0.001).ConclusionAn active IEGM improves the clinical value of remote pacemaker follow-up. Furthermore, compared to a passive IEGM, the aIEGM increases the capability to fully assess remotely the sensing and pacing functions.

Cost-Effective Wireless Microcontroller for Internet Connectivity of Open-Source Chemical Devices

This paper presents a system for remote monitoring of turbidity data that can detect light intensity and temperature using a microcontroller and the Internet of Things. The system comprises a light dependent resistor and infrared temperature sensor interfaced with an ESP-12E microcontroller, a programmable data acquisition platform; together these enable remote real-time monitoring of turbidity data applicable to chemical education. This system will allow educators and students to monitor chemical experiments in real-time through a web browser. Changes in turbidity and signal processing associated with a light dependent resistor are stored locally on the microcontroller and transmitted wirelessly via a WiFi-based connection to a cloud-based server. The light intensity signals are displayed on the system through an organic light-emitting diode and subsequently uploaded to a cloud-based server. A web-based signal processing MATLAB program plots the uploaded signals available as real-time streams to authoriz...

Does the CHA2DS2-VASc score reliably predict atrial arrhythmias? Analysis of a nationwide database of remote monitoring data transmitted daily from cardiac implantable electronic devices

CHA2DS2-VASc is a validated score for predicting stroke in patients with atrial fibrillation (AF). The purpose of this study was to assess whether the CHA2DS2-VASc score can predict new-onset AF in a cohort of patients with a cardiac implantable electronic device (CIED) followed with remote monitoring. Using the database of the Home Monitoring Expert Alliance project, we selected 2410 patients with no documented AF who had received a CIED with diagnostics on atrial high rate episodes (AHREs). The primary endpoint was time to first day with cumulative AHRE burden ≥15 minutes, 5 hours, 24 hours, and ≥7 consecutive days. During a median duration of 24.1(11.5-42.9) months, the incidence of AHRE increased with increasing CHA2DS2-VASc. At 6 years, occurrence of ≥15-minute AHRE was 80.2% (CHA2DS2-VASc ≤1) vs 93.7% (CHA2DS2-VASc ≥5), whereas ≥5-hour AHRE incidence was 68.4% (CHA2DS2-VASc ≤1) vs 92.5% (CHA2DS2-VASc ≥5). Occurrence of ≥24-hour and ≥7-day AHREs also increased with increasing CHA2DS2-VASc: 9.1% and 3.9% (CHA2DS2-VASc ≤1) vs 40.4% and 28.7% (CHA2DS2-VASc ≥5), respectively. Adjusted hazard ratio for unitary CHA2DS2-VASc increase ranged from 1.09 (confidence interval 1.04-1.14; P<.001) with AHRE burden ≥15 minutes to 1.26 (confidence interval 1.11-1.42; P <.001) with AHRE burden ≥7 days. At receiver operating curve analysis, CHA2DS2-VASc ≥2 was estimated to predict persistent forms of AHREs with 95.8% sensitivity but 11.7% specificity at 3 years. CHA2DS2-VASc ≥5 had 77.0% specificity but 34.6% sensitivity. In a CIED population with no previous diagnosis of clinical AF, AHRE incidence increased with increasing CHA2DS2-VASc score. The association was stronger with longer AHREs, but the accuracy of CHA2DS2-VASc as AHRE predictor was moderate.