Electrocardiogram Transmission Research Articles

Impact of a prehospital mobile cloud ECG transmission system on door-to-balloon time, myocardial damage and mid-term all-cause mortality in patients with ST-elevation myocardial infarction

Abstract Background Guidelines recommend to establish a regional reperfusion strategy to maximize the efficiency of care for patients diagnosed with ST-elevation myocardial infarction (STEMI). In Addition, the recently developed mobile cloud-based 12-lead electrocardiogram (ECG) transmission system enables the transmission of prehospital ECGs at a low cost. Medical staff can use electronic devices to access and review ECGs from anywhere. However, the effectiveness of the prehospital mobile cloud ECG system has not yet been clearly established. Objective This single-center study aims to evaluate the impact of the prehospital mobile cloud ECG system on reducing Door-to-Balloon Time (DTBT), myocardial damage, and mid-term all-cause mortality in patients with STEMI. Methods In June 2018, eight prehospital Mobile Cloud ECG Transmission systems were integrated into the regional Emergency Medical Service (EMS) in our region. This study examined and compared Door-to-Catheterization Laboratory Time (DTCT), DTBT, Onset-to-Reperfusion Time (OTRT), peak creatine kinase-MB (CK-MB) levels, and one-year mortality rates of STEMI patients in the five years before and after introducing the prehospital mobile cloud ECG system. From June 2013 to May 2023, our institute received a total of 426 STEMI patients via ambulances from these two EMS departments. After excluding 23 patients with OTRT exceeding 24 hours and 76 patients who were transferred without the system post-introduction, 327 consecutive STEMI patients who underwent emergency percutaneous coronary intervention (PCI) were included in the study. The patients were divided into two groups: 169 patients before the prehospital mobile cloud ECG system introduction and 158 patients after that. Results There were no significant differences in age, gender, past medical history, and Killip Class IV between the two groups, except for the proportion of left main trunk (LMT) lesions, which was significantly higher in the ECG group. Significant reductions were observed in both DTCT and DTBT in the group after introduction, with differences noted as (DTCT: 42 min vs. 26 min; p < 0. 001, and DTBT: 74 min vs. 56 min; p < 0.001, respectively). However, the OTRT did not show a significant difference. Peak creatine kinase-MB (CK-MB) levels did not differ between the two groups, and Kaplan-Meier analysis revealed no significant difference in one-year mortality between them. Multivariate Cox regression analysis, which included variables such as age, peak CK-MB levels, Killip Class IV, LMT lesions, OTRT and the prehospital mobile cloud ECG transmission system usage, revealed that only Killip Class IV was significantly associated with one-year mortality (hazard ratio of 2.82 and a 95% confidence interval of 1.61-4.94; p=0.0003). Conclusion The prehospital mobile cloud ECG transmission system significantly reduced DTBT, but did not lead to a reduction in myocardial damage or an improvement in mid-term prognosis.Effect of the ECG Transmission SystemKaplan-Meier analysis

Classification of Prehospital Electrocardiograms Performed in Ambulances According to Severity Using a Deep Learning Neural Network

Prehospital electrocardiogram (PH-ECG) transmission is an important technology for reducing door-to-balloon time, but the decision to transmit often depends on the discretion of emergency medical technicians (EMTs). Additionally, studies based on real-world data remain insufficient. This study reports a machine learning-based method for classifying the severity of PH-ECG images and explores its feasibility. PH-ECG data were compiled from 120 patients between September 2017 and September 2020. The model we created from these data was the first classification model for PH-ECG images using data from a Japanese study population and showed a weighted F1-score of 0.85 and an Area Under the Curve (AUC) of 0.93. This result can be interpreted as having an excellent balance of sensitivity and specificity. The Cohen’s Kappa coefficient between AI’s inferences and the correct labels created by two cardiologists was 0.68 (p < 0.05), which is considered “substantial” according to the guidelines presented by Landis and Koch. In this study, although we were not able to remove noise caused by patient movement or electrode detachment, the results indicate that image-based abnormality detection from PH-ECGs is feasible and effective, particularly in regions like Japan where ECG data are often stored and transmitted as images. In addition, in our region, paramedics follow a multi-step process to decide whether to transmit an ECG, which takes time for the first screening. However, if the ECG is transmitted when either the paramedics or the deep learning model detects an abnormality, it is expected to reduce reading time and door-to-balloon time, as well as decrease false negatives.

Implementation of a Novel Prehospital Clinical Decision Tool and ECG Transmission for STEMI Significantly Reduces Door-to-Balloon Time and Sex-Based Disparities

Background An important method employed to reduce door to balloon time (DTBT) for ST segment elevation Myocardial Infarctions (STEMIs) is a prehospital MI alert. The purpose of this retrospective study was to examine the effects of an educational intervention using a novel decision support method of STEMI notification and prehospital electrocardiogram (ECG) transmission on DTBT. Methods An ongoing database (April 4, 2000 – present) is maintained to track STEMI alerts. In 2007, an MI alert program began; emergency medicine physicians could activate a “prehospital MI alert”. In October 2015, modems were purchased for Emergency Medical Services personnel to transmit ECGs. There was concurrent implementation of a decision support tool for identifying STEMI. Sex was assigned as indicated in the medical record. Data were analyzed in two groups: Pre-2016 (PRE) and 2016–2022 (POST). Results In total, 3,153 patients (1,301 PRE; 1,852 POST) were assessed; the average age was 65.2 years, 32.6% female, 87.7% white with significant differences in age and race between the two cohorts. Of the total 3,153 MI alerts, 239 were false activations, leaving 2,914 for analysis. 2,115 (72.6%) had cardiac catheterization while 16 (6.7%) of the 239 had a cardiac catheterization. There was an overall decrease in DTBT of 27.5% PRE to POST of prehospital ECG transmission (p < 0.001); PRE median time was 74.5 min vs. 55 min POST. There was no significant difference between rates of cardiac catheterization PRE and POST for all patients. After accounting for age, race, and mode of arrival, DTBT was 12.2% longer in women, as compared to men (p < 0.001) PRE vs. POST. DTBT among women was significantly shorter when comparing PRE to POST periods (median 77 min vs. 60 min; p = 0.0001). There was no significant sex difference in the proportion of those with cardiac catheterization between the two cohorts (62.5% vs. 63.5%; p = 0.73). Conclusion Introduction of a decision support tool with prehospital ECG transmission with prehospital ECG transmission decreased overall DTBT by 20 min (27.5%). Women in the study had a 17-minute decrease in DTBT (22%), but their DTBT remained 12.2% longer than men for reasons that remain unclear.

Establishment of a digital electrocardiogram telemonitoring system and remote consultation service.

The use of telemonitoring electrocardiogram (ECG) improves the detection rate of various arrhythmias as it offers an extended recording time and does not impose activity restrictions. However, many community hospitals lack the resources to conduct this test due to a lack of cardiac function specialists and antiquated screening equipment. To establish a digital ECG telemonitoring system and remote consultation service between tertiary hospitals and community medical service stations to improve diagnosis and treatment effectiveness. We used the PI ECG telemonitoring data acquisition system, with a personal digital assistant (PDA) or computer serving as the platform for ECG acquisition, storage, display, printing, and transmission. We introduced this system to standardize the storage and transmission of ECG telemonitoring data, and ensure accurate transmission, reproducibility, and preservation of data. The implementation of the PI ECG telemonitoring data acquisition system enabled the sharing of remote ambulatory electrocardiography data between Beijing Shijitan Hospital and its affiliated community hospitals. This has resulted in reduced waiting times for patients to receive reports (from 0.3 to 1 hour, to 0.1 or less), shortened consultation times (from 2 hours to 0.1 hour), and improved patient satisfaction with consultations (> 20% of the patients reported they were highly satisfied). The system successfully combined the goals of intelligence, real-time synchronization, and efficiency, revolutionizing ECG telemonitoring diagnosis in the network era. Sharing digital telematics information between tertiary and community hospitals enables a tiered approach to diagnosis and treatment and allows for more efficient, accurate, and convenient remote diagnostics.

Design and Development of an Android-based Remote Cardiac Monitoring Device for Continuous Real-time ECG Signal Acquisition, Transmission, and Analysis

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, necessitating innovative solutions for early detection and continuous monitoring. This research aims to develop an Android-based remote cardiac monitoring device for real-time electrocardiogram (ECG) signal acquisition, transmission, and analysis. The system comprises hardware for acquiring ECG signals, algorithms for processing and machine learning models for anomaly classification. The hardware unit captures ECG data using electrodes and sensors. The signals are filtered, processed, and transmitted to the cloud infrastructure enabling real-time monitoring and analysis. Machine learning models including support vector machines, ensemble methods and Artificial neural networks are trained on ECG datasets to classify signals and detect cardiac abnormalities. Comprehensive testing validates the system's capabilities in real-time signal acquisition, processing, anomaly detection and data transmission. The integration of hardware, algorithms and machine learning enables round-the-clock monitoring of cardiac activity, facilitating prompt interventions and improved patient outcomes. This affordable and user-friendly system demonstrates potential for enhanced accessibility and effectiveness of preventive cardiac care.

Digitalization and clinical decision tools

Digitalization in cardiovascular emergencies is rapidly evolving, analogous to the development in medicine, driven by the increasingly broader availability of digital structures and improved networks, electronic health records and the interconnectivity of systems. The potential use of digital health in patients with acute chest pain starts even in the prehospital phase with the transmission of adigital electrocardiogram (ECG) as well as telemedical support and digital emergency management, which facilitate optimization of the rescue pathways and reduce critical time intervals. The increasing dissemination and acceptance of guideline apps and clinical decision support tools as well as integrated calculators and electronic scores are anticipated to improve guideline adherence, translating into abetter quality of treatment and improved outcomes. Implementation of artificial intelligence to support image analysis and also the prediction of coronary artery stenosis requiring interventional treatment or impending cardiovascular events, such as heart attacks or death, have an enormous potential especially as conventional instruments frequently yield suboptimal results; however, there are barriers to the rapid dissemination of corresponding decision aids, such as the regulatory rules related to approval as amedical product, data protection issues and other legal liability aspects, which must be considered.

Cloud Security System for ECG Transmission and Monitoring Based on Chaotic Logistic Maps

Biomedical data or information must be transmitted securely via the internet for smart healthcare. The Electrocardiogram (ECG) signal is amongst the most essential clinical signals which must be delivered to hospital facilities. Prime focus of this research is on the encryption of ECG for secure transmission. Chaos theory is used for the development of deterministic nonlinear systems, that can be used to create random numbers for the Chaotic Logistic Map (CLM) based encryption. This study describes a cryptographic algorithm for encrypting ECG signals that uses a mix of the CLM and fingerprint data. The common factor between the patient section and monitoring section is the operation on sample data points of ECG. The choice of proper encryption and decryption theme can save more amount of time and is invulnerable both to noise-based attacks and hacking instances. The proposed framework is implemented on Dropbox based cloud storage and access is possible from any given locations. Simulation tests are used to assess the system performance in terms of Structural Similarity Index Matrix (SSIM), Histogram, Spectral Distortion (SD), Correlation and Log-Likelihood Ratio (LLR). The incorporation of complex layers of CLM encryption increases security.

Acute Coronary Syndrome Registry in Iwate Prefecture

Background: In Iwate Prefecture, the registration project for acute coronary syndrome started as a pilot project in 2014 and became a main project in 2016, enabling us to understand and verify the current situation in Iwate Prefecture.Methods: To obtain a comprehensive understanding of the present status of the region, we compiled data on the registration project for acute coronary syndrome in Iwate Prefecture.Results: The incidence/death ratio, which indicates the accuracy of registration, and the ambulance use rate increased over time. The percentage of 12-lead electrocardiogram (ECG) transmission implementation in all prefectures was still insufficient, at 7.5% in 2019. In contrast, in medical regions where transmission had already been introduced, 23.6–62.5% of cases had ECG transmission. The atrial fibrillation complication rate showed a slight upward trend over time, exceeding 10% in 2019. The incidence of pre-infarction angina was approximately 30% in this 2019 registry. For urgent recanalization therapy, urgent coronary angiography was performed in 89% of cases in 2019, and recanalization therapy was performed in an additional 83% of cases. These rates have also increased over time.Conclusion: It is necessary to continue to promote registration projects that provide an overall view of the region, provide data that can be returned to residents, and advise the government.

A Digital Infrastructure for Cardiovascular Patient Care Based on Mobile Health Data and Patient-Reported Outcomes: Concept Details of the Helios TeleWear Project Including Preliminary Experiences.

Mobile health (mHealth) approaches are already having a fundamental impact on clinical practice in cardiovascular medicine. A variety of different health apps and wearable devices for capturing health data such as electrocardiograms (ECGs) exist. However, most mHealth technologies focus on distinct variables without integrating patients' quality of life, and the impact on clinical outcome measures of implementing those digital solutions into cardiovascular health care is still to be determined. Within this document, we describe the TeleWear project, which was recently initiated as an approach for contemporary patient management integrating mobile-collected health data and the standardized mHealth-guided measurement of patient-reported outcomes (PROs) in patients with cardiovascular disease. The specifically designed mobile app and clinical frontend form the central elements of our TeleWear infrastructure. Because of its flexible framework, the platform allows far-reaching customization with the possibility to add different mHealth data sources and respective questionnaires (patient-reported outcome measures). With initial focus on patients with cardiac arrhythmias, a feasibility study is currently carried out to assess wearable-recorded ECG and PRO transmission and its evaluation by physicians using the TeleWear app and clinical frontend. First experiences made during the feasibility study yielded positive results and confirmed the platform's functionality and usability. TeleWear represents a unique mHealth approach comprising PRO and mHealth data capturing. With the currently running TeleWear feasibility study, we aim to test and further develop the platform in a real-world setting. A randomized controlled trial including patients with atrial fibrillation that investigates PRO- and ECG-based clinical management based on the established TeleWear infrastructure will evaluate its clinical benefits. Widening the spectrum of health data collection and interpretation beyond the ECG and use of the TeleWear infrastructure in different patient subcohorts with focus on cardiovascular diseases are further milestones of the project with the ultimate goal to establish a comprehensive telemedical center entrenched by mHealth.

Prehospital Electrocardiogram Transmission and Prehospital Scene Time: A Retrospective Cohort Study.

Background: Telemedical transmission of prehospital electrocardiograms (ECGs) to a target clinic may improve clinical workflows and speed of intervention. However, whether ECG transmission delays prehospital workflows remains controversial. Therefore, we aimed to clarify whether ECG transmission prolongs prehospital scene time in patients diagnosed with acute coronary syndrome (ACS). Methods: We retrospectively included all patients diagnosed with ACS by prehospital emergency physicians from July 2016 to June 2019 at a single academic center. The primary endpoint was the effect of ECG transmission on prehospital scene time. The secondary endpoints were the effects of ECG diagnosis on prehospital scene time and quality of care. Multivariable regression was used to account for patients' age, physician specialty, completion of specialty training, and whether emergencies occurred throughout the day or night shifts as potential confounders. Results: A total of 1,106 cases were included, of which 154 ECG transmissions were performed. ECG transmission prolonged prehospital scene time by an average of 3 min: adjusted regression coefficient [95% confidence interval (95% CI)]: 3.24 (1.7-4.8), p < 0.001. Prehospital treatment time was not influenced by prehospital ECG-based diagnosis (ST-elevation myocardial infarction [STEMI] vs. non-ST-elevation ACS [NSTE-ACS]): adjusted regression coefficient (95% CI): 0.7 (-1.3 to 2.7), p = 0.490. Emergency physicians adhered to local standard operating procedures in 739 of 1,007 (73%) patients diagnosed with NSTE-ACS and 93 of 99 (94%) patients diagnosed with STEMI. A STEMI diagnosis compared with NSTE-ACS was associated with five times higher odds of adhering to standard operating procedures (odds ratio [95% CI]: 5.6 [2.7-14.6], p < 0.001). Conclusion: The observed delay of ∼3 min in the prehospital scene time by ECG transmission is clinically irrelevant. For patients prehospitally diagnosed with NSTE-ACS who do not meet STEMI criteria, adherence to standard operating procedures seems unjustifiably low and should be improved.

Digitally Printed Liquid Metal Composite Antenna for Energy Harvesting: Toward Energy‐Autonomous Battery‐Free Wearable Bioelectronics

AbstractRapid progress on wearable bioelectronics holds the promise for remote patient monitoring. However, there are concerns related with the use of batteries, including the need for frequent charging, user safety, and environmental pollution. The development of rapidly deployable battery‐free patches that harvest their energy from external sources is highly desired. Here, untethered battery‐free patches through far‐field energy harvesting are demonstrated. Taking advantage of a stretchable biphasic liquid metal‐based ink and digital fabrication techniques, that is, direct printing, and laser patterning, tailor‐made customized antennas are fabricated over various substrates, including polydimethylsiloxane, Kapton, and wound‐dressing medical‐grade adhesives. Folded dipole and planar inverted‐F antennas (PIFA) are evaluated and they are optimized for an over‐the‐body scenario. Compared to previous works that only show communication of data at &lt;1 Hz, here, battery‐free acquisition and transmission of electrocardiogram (ECG) data at 100 Hz via Bluetooth is demonstrated. It is shown that using 3 parallel PIFA antennas, it is possible to harvest ≈10 mW at 30 cm from the transmitter, which is ≈7× the required power by the ECG circuit. This study proves the feasibility of next generation of battery‐free wearable biomonitoring patches/e‐textiles and demonstrates materials and methods for fabrication of optimized printed antennas.

Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care.

ObjectiveTo implement an all-day online artificial intelligence (AI)-assisted detection of ST-elevation myocardial infarction (STEMI) by prehospital 12-lead electrocardiograms (ECGs) to facilitate patient triage for timely reperfusion therapy.MethodsThe proposed AI model combines a convolutional neural network and long short-term memory (CNN-LSTM) to predict STEMI on prehospital 12-lead ECGs obtained from mini-12-lead ECG devices equipped in ambulance vehicles in Central Taiwan. Emergency medical technicians (EMTs) from the 14 AI-implemented fire stations performed the on-site 12-lead ECG examinations using the mini portable device. The 12-lead ECG signals were transmitted to the AI center of China Medical University Hospital to classify the recordings as “STEMI” or “Not STEMI”. In 11 non-AI fire stations, the ECG data were transmitted to a secure network and read by available on-line emergency physicians. The response time was defined as the time interval between the ECG transmission and ECG interpretation feedback.ResultsBetween July 17, 2021, and March 26, 2022, the AI model classified 362 prehospital 12-lead ECGs obtained from 275 consecutive patients who had called the 119 dispatch centers of fire stations in Central Taiwan for symptoms of chest pain or shortness of breath. The AI's response time to the EMTs in ambulance vehicles was 37.2 ± 11.3 s, which was shorter than the online physicians' response time from 11 other fire stations with no AI implementation (113.2 ± 369.4 s, P < 0.001) after analyzing another set of 335 prehospital 12-lead ECGs. The evaluation metrics including accuracy, precision, specificity, recall, area under the receiver operating characteristic curve, and F1 score to assess the overall AI performance in the remote detection of STEMI were 0.992, 0.889, 0.994, 0.941, 0.997, and 0.914, respectively. During the study period, the AI model promptly identified 10 STEMI patients who underwent primary percutaneous coronary intervention (PPCI) with a median contact-to-door time of 18.5 (IQR: 16–20.8) minutes.ConclusionImplementation of an all-day real-time AI-assisted remote detection of STEMI on prehospital 12-lead ECGs in the field is feasible with a high diagnostic accuracy rate. This approach may help minimize preventable delays in contact-to-treatment times for STEMI patients who require PPCI.

Profile of Acute Coronary Syndromes in Serving Personnel Presenting to a Field Cardiology Center Without Cath Lab Facilities

Introduction: Acute coronary syndrome (ACS) is common in serving personnel and requires prompt diagnosis and treatment. This retrospective study was done to see the profile of ACS in serving personnel presenting to a tertiary hospital without cardiac catheterization laboratory (CCL) facilities. Methods: ACS was diagnosed based on an electrocardiogram (ECG), clinical features, and cardiac biomarkers. History of comorbidities, risk factors, and preceding exertion was taken. Immediate medical management was done as per the standard guidelines, and follow-up angiography was done at a dependent cardiology center with CCL. Results: A total of 75 personnel reported with ACS. The mean age of personnel was 42.01 ± 8.59 years. Acute ST-elevation myocardial infarction (STEMI) was noted in 48% (36/75) of the patients, 32% (24/75) had non-STEMI, and 20% (15/75) had unstable angina (UA). Low ejection fraction was more common in patients with STEMI (P ≤ 0.001). History of preceding exertion was noted in 55 personnel. Eighteen patients with STEMI were thrombolysed. Angiographically significant lesions were noted only in 30.6% (23/75) individuals with ACS. Conclusion: ACS is common in deployed serving personnel. The most common precipitators were immediate preceding exertion and smoking. Morbidity and mortality due to ACS can be decreased by digital transmission of ECG, prehospital thrombolysis, graded physical exertion, and counseling about a healthy diet.

Utility of prehospital electrocardiogram interpretation in ST-segment elevation myocardial infarction utilizing computer interpretation and transmission for interventional cardiologist consultation.

ObjectivesWe examined the appropriateness of prehospital cardiac catheter laboratory activation (CCL‐A) in ST‐segment elevation myocardial infarction (STEMI) utilizing the University of Glasgow algorithm (UGA) and remote interventional cardiologist consultation.BackgroundThe incremental benefit of prehospital electrocardiogram (PH‐ECG) transmission on the diagnostic accuracy and appropriateness of CCL‐A has been examined in a small number of studies with conflicting results.MethodsWe identified consecutive PH‐ECG transmissions between June 2, 2010 and October 6, 2016. Blinded adjudication of ECGs, appropriateness of CCL‐A, and index diagnoses were performed using the fourth universal definition of MI. The primary outcome was the appropriate CCL‐A rate. Secondary outcomes included rates of false‐positive CCL‐A, inappropriate CCL‐A, and inappropriate CCL nonactivation.ResultsAmong 1088 PH‐ECG transmissions, there were 565 (52%) CCL‐As and 523 (48%) CCL nonactivations. The appropriate CCL‐A rate was 97% (550 of 565 CCL‐As), of which 4.9% (n = 27) were false‐positive. The inappropriate CCL‐A rate was 2.7% (15 of 565 CCL‐As) and the inappropriate CCL nonactivation rate was 3.6% (19 of 523 CCL nonactivations). Reasons for appropriate CCL nonactivation (n = 504) included nondiagnostic ST‐segment elevation (n = 128, 25%), bundle branch block (n = 132, 26%), repolarization abnormality (n = 61, 12%), artefact (n = 72, 14%), no ischemic symptoms (n = 32, 6.3%), severe comorbidities (n = 26, 5.2%), transient ST‐segment elevation (n = 20, 4.0%), and others.ConclusionsPH‐ECG interpretation utilizing UGA with interventional cardiologist consultation accurately identified STEMI with low rates of inappropriate and false‐positive CCL‐As, whereas using UGA alone would have almost doubled CCL‐As. The benefits of cardiologist consultation were identifying “masquerading” STEMI and avoiding unnecessary CCL‐As.

Early detection of atrial fibrillation in patients with heart failure reduces the risk of subsequent hospitalization: a subanalysis of the randomized TIM-HF2 trial.

To evaluate the rate of new-onset atrial fibrillation (AF) and the potential improved outcome in heart failure (HF) patients using non-invasive remote patient management (RPM) compared with usual care (UC). This analysis assessed a subgroup of 1538 patients of the TIM-HF2 trial with chronic HF, New York Heart Association Class II or III, admission to hospital for HF within 12 months before randomization, and a left ventricular ejection fraction (LVEF) of 45% or lower. Patients with AF in the baseline electrocardiogram (ECG), with an implanted cardiac device, a history of ablation therapy, and recent anticoagulation were excluded, leaving 347 patients for final analysis (RPM = 175; UC = 172). The percentage of days lost due to unplanned cardiovascular hospitalization or death of any cause (primary endpoint of TIM-HF2), the rate of newly detected AF, and the hospitalization rate due to AF were analysed. For patients with new AF, there was a significant reduction for the primary endpoint in the RPM group [5.5%, 95% confidence interval (CI) 0-11.6 vs. UC: 14.6%, 95% CI 8.0-21.2; P < 0.001]. Within the first 3 months, the detection rate of new AF was significantly higher in the RPM group (5.1%) compared with UC (1.2%), P = 0.035. After 1 year, 23 patients (13.1%) assigned to RPM and 12 patients (7.0%) assigned to UC had newly detected AF, P = 0.056. Unplanned hospitalizations related to AF were significantly lower in the RPM group (2 out of 23 patients vs. UC: 10 out of 12 patients; P < 0.001). In this subgroup of HF patients in the TIM-HF2 trial, non-invasive daily ECG transmission leads to a four times higher detection rate of new AF compared with UC. This was associated with a significant reduction of days lost due to unplanned cardiovascular hospitalizations, especially hospitalizations related to AF.

A nurse-led pre-hospital triage service for identifying patients with occlusive myocardial infarction: a service evaluation

Background/Aims Acute coronary syndromes include unstable angina and myocardial infarction, of which occlusive myocardial infarction is a high-risk subset that is often missed because of a lack of ST elevation. Pre-hospital electrocardiograms may be able to identify myocardial infarction early and reduce mortality. However, it is unclear whether pre-hospital electrocardiograms can accurately detect occlusive myocardial infarction and how this affects outcomes. This study will analyse the outcomes of patients with occlusive myocardial infarction who had a pre-hospital electrocardiogram. Method Electrocardiograms transmitted to the coronary care unit triage service were identified, along with data regarding patient demographics, referrals and mortality. Data were analysed for correlations between demographic and clinical factors and type of myocardial infarction. Results A total of 838 electrocardiograms were identified; 69 (8.2%) showed myocardial infarction and eight (1.0%) showed occlusive myocardial infarction, of which 50% had ST elevation. Patients with occlusive myocardial infarction were more likely to be triaged to the coronary care unit than patients with non-occlusive myocardial infarction (P=0.04). However, 38 (55.1%) of the patients with myocardial infarction and four (50.0%) of the patients with occlusive myocardial infarction were not directed to a coronary care unit or the catheterisation laboratory. Patients with occlusive myocardial infarction had higher index episode mortality rates (P=0.03) and 30-day mortality rates (P&lt;0.01). Conclusions Despite pre-hospital electrocardiogram transmission and adherence to the guidelines, triage of myocardial infarction and occlusive myocardial infarction is imperfect. Refinement of risk scores and adaptation of new technology is required to help identify patients with occlusive myocardial infarction so they can be prioritised for immediate reperfusion therapy.

Cardiac catheterization laboratory activation by social media reduces reperfusion time of patients transferred for primary percutaneous coronary intervention in community hospital

Objective: The objective of the study is to evaluate the impact of cardiac catheterization laboratory (CCL) activation by WeChat in community hospitals transfer for primary percutaneous coronary intervention (PCI) bypassing emergency department (ED) on time delay in ST-segment elevation myocardial infarction (STEMI) patients. Methods: This was a retrospective, cohort study of STEMI patients who were transferred from community hospitals to Luhe Hospital for primary PCI. Patients were divided into two groups: in CCL group, electrocardiogram (ECG) was transmitted through WeChat in the community hospital and the patients were transferred directly to CCL by emergency medical services (EMS) (n = 43); in control group, patients without ECG transmission were transferred by EMS to ED before CCL arrival (n = 57). The primary endpoint was median first medical contact (FMC)-to-device (FMC2D) times. The secondary endpoint was door-to-device (D2D) times. Results: The baseline clinical data and angiographic features among the two groups were similar (all P &gt; 0.05. There was no difference in the symptom onset-to-FMC time (median interquartile range [IQR] min, 100.00 [74.50–247.00] vs. 105.00 [70.00–180.00], P &gt; 0.05) between the two groups. Compared to control group, CCL group had shortened FMC2D times (median IQR min, 95.00 [72.60–160.58] vs. 160.79 [124.72–205.50], P &lt; 0.001) and shortened D2D time (median IQR min, 17.18 [13.77–21.15] vs. 49.27 [40.26–64.90], P &lt; 0.001). Achievement of the FMC2D time goal of &lt;120 min rose from 29.82% in the control group to 76.74% in the CCL group (P &lt; 0.001). Conclusion: CCL activated by WeChat can reduce reperfusion time of STEMI patients who are transferred for PCI from community hospitals.

The Effectiveness of Mobile Cloud 12-Lead Electrocardiogram Transmission System in Patients with ST-Segment Elevation Myocardial Infarction.

Backgroundand Objectives: Delay of reperfusion therapy is related to high mortality in cases of ST-segment elevation myocardial infarction (STEMI). Guidelines emphasize that the first-medical-contact-to-balloon (FMCTB) time should be within 90 min. A mobile cloud-based 12-lead electrocardiogram (MC-ECG) transmission system might be useful in such cases, especially in rural areas. Materials and Methods: From April 2019 to June 2021, both an MC-ECG transmission system and the conventional method in which a physician checks the ECG in a hospital (Conventional) were used for transport by emergency medical services in Shin-Yukuhashi Hospital, Fukuoka, Japan. During this period, 8684 consecutive patients were transported to this hospital. Among them, we investigated 48 STEMI patients. The MC-ECG group (n = 23) and the Conventional group (n = 25) were enrolled. Results: There was no significant difference in FMCTB time between the MC-ECG and Conventional groups (MC-ECG: 72.0 (60.5–107) min vs. Conventional: 80.0 (63.0–92.0) min, p = 0.77). The length of hospital stay in the MC-ECG group was significantly shorter than that in the Conventional group (12.0 (10.0–15.0) days vs. 16.0 (12.0–19.0) days, p = 0.039). The logistic regression model showed that patients’ non-use of MC-ECG was associated with a risk of more than 15-day length of hospital stay with an adjusted odd ratio of 0.08 (95% CI: 0.013–0.55, p = 0.0098). Conclusions: Using the MC-ECG, the length of hospital stay in patients with STEMI was significantly reduced.

Patient-Led Mass Screening for Atrial Fibrillation in the Older Population Using Handheld Electrocardiographic Devices Integrated With a Clinician-Coordinated Remote Central Monitoring System: Protocol for a Randomized Controlled Trial and Process Evaluation.

BackgroundAtrial fibrillation (AF) is common in older people and increases the risk of stroke. The feasibility and effectiveness of the implementation of a patient-led AF screening program for older people are unknown.ObjectiveThis study aims to examine the feasibility and effectiveness of an AF screening program comprising patient-led monitoring of single-lead electrocardiograms (ECGs) with clinician-coordinated central monitoring to diagnose AF among community-dwelling people aged ≥75 years in Australia.MethodsThis is a nationwide randomized controlled implementation trial conducted via the internet and remotely among 200 community-dwelling adults aged ≥75 years with no known AF. Randomization will be performed in a 1:1 allocation ratio for the intervention versus control. Intervention group participants will be enrolled in the monitoring program at randomization. They will receive a handheld single-lead ECG device and training on the self-recording of ECGs on weekdays and submit their ECGs via their smartphones. The control group participants will receive usual care from their general practitioners for the initial 6 months and then commence the 6-month monitoring program. The ECGs will be reviewed centrally by trained personnel. Participants and their general practitioners will be notified of AF and other clinically significant ECG abnormalities.ResultsThis study will establish the feasibility and effectiveness of implementing the intervention in this patient population. The primary clinical outcome is the AF detection rate, and the primary feasibility outcome is the patient satisfaction score. Other outcomes include appropriate use of anticoagulant therapy, participant recruitment rate, program engagement (eg, frequency of ECG transmission), agreement in ECG interpretation between the device automatic algorithm and clinicians, the proportion of participants who complete the trial and number of dropouts, and the impact of frailty on feasibility and outcomes. We will conduct a qualitative evaluation to examine the barriers to and acceptability and enablers of implementation. Ethics approval was obtained from the human research ethics committee at the University of Sydney (project number 2020/680). The results will be disseminated via conventional scientific forums, including peer-reviewed publications and presentations at national and international conferences.ConclusionsBy incorporating an integrated health care approach involving patient empowerment, centralized clinician-coordinated ECG monitoring, and facilitation of primary care and specialist services, it is possible to diagnose and treat AF early to reduce stroke risk. This study will provide new information on how to implement AF screening using digital health technology practicably and feasibly for older and frail populations residing in the community.Trial RegistrationAustralian New Zealand Clinical Trials Registry ACTRN12621000184875; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380877International Registered Report Identifier (IRRID)DERR1-10.2196/34778

A Review of the Commercially Available ECG Detection and Transmission Systems-The Fuzzy Logic Approach in the Prevention of Sudden Cardiac Arrest.

Sudden cardiac death (SCD) constitutes a major clinical and public health problem, whose death burden is comparable to the current worldwide pandemic. This comprehensive review encompasses the following topics: available rescue systems, wearable electrocardiograms (ECG), detection and transmission technology, and a newly developed fuzzy logic algorithm (FA) for heart rhythm classification which is state-of-the art in the field of SCD prevention. Project “PROTECTOR”, the Polish Rapid Transtelephonic ECG to Obtain Resuscitation for development of a rapid rescue system for patients at risk of sudden cardiac arrest (SCA), is presented. If a lethal arrhythmia is detected on the basis of FA, the system produces an alarm signal audible for bystanders and transmits the alarm message along with location to the emergency medical center. Phone guided resuscitation can be started immediately because an automated external defibrillator (AED) localization map is available. An automatic, very fast diagnosis is a unique feature of the PROTECTOR prototype. The rapid detection of SCA is based on a processor characterized by 100% sensitivity and 97.8% specificity (as measured in the pilot studies). An integrated circuit which implements FA has already been designed and a diagnosis is made within few seconds, which is extremely important in ischemic brain damage prophylaxis. This circuit could be implemented in smart implants (Sis).