Single-lead Electrocardiogram Device Research Articles

Accuracy of a Single-Lead ECG Device for Diagnosis of Cardiac Arrhythmias Compared Against Cardiac Electrophysiology Study

Single-lead electrocardiogram (ECG) devices may allow detection and diagnosis of cardiac rhythms. However, data on their accuracy for detecting cardiac arrhythmias beyond atrial fibrillation are limited. We aimed to determine the accuracy of the AliveCor KardiaMobile (AC) (AliveCor Inc, Mountain View, CA, USA) for the diagnosis of arrhythmias against gold standard cardiac electrophysiology study (EPS). Patients undergoing clinically indicated EPS underwent simultaneous rhythm recording with an AC, standard 12-lead ECG, and EP catheters for intracardiac electrograms. Rhythms recorded during EPS were classified based on electrogram, 12-lead ECG, and clinical findings. Blinded reviewers provided differential diagnoses for the single-lead AC tracings; a separate reviewer compared diagnoses made between the AC tracings and EPS findings. In 49 patients, 843 cardiac rhythms were captured during 502 AC recordings. Analysis of tracings containing sinus rhythm (n=273) returned an overall accuracy of 92%, with sensitivity and specificity values of 93% and 92%, respectively. Accuracy for tracings per rhythm was atrial fibrillation 91% (n=51); supraventricular tachycardia accuracy was 89% (n=191), ventricular tachycardia 91% (n=198), ventricular fibrillation 98% (n=11), and asystole 100% (n=5). Accuracy for supraventricular ectopy was 93% (n=28) and for premature ventricular complexes was 91% (n=86). Overall accuracy was 94% for solitary rhythms and 93% in tracings from patients with baseline bundle branch block. When compared against the gold standard EPS diagnosis, the interpretation of arrhythmias recorded by an AliveCor single-lead ECG device had reasonable diagnostic accuracy.

Use of a novel smartphone based single-lead electrocardiogram device in clinical practice to evaluate heart rhythm in horses – a case series

Use of a novel smartphone based single-lead electrocardiogram device in clinical practice to evaluate heart rhythm in horses – a case series

Factors for incorrect automated atrial fibrillation identification in large-scale screening

Abstract Introduction Correct heart rhythm diagnosis in single-lead electrocardiogram (ECG) screening for atrial fibrillation (AF) is paramount for large-scale implementation. Aim Examine the accuracy of automated AF detection and identify patient factors and ECG signal quality leading to incorrect measurements based on human overread and a machine learning approach. Methods Screening for AF using a single-lead ECG device (MyDiagnostick, Biomedical Systems, Netherlands) was performed on 7295 customers in community pharmacies in Germany. ECG rhythm strips were blinded for automatic heart rhythm analysis and were analyzed by human overread for heart rhythm and signal quality from excellent to uninterpretable (Figure 1A). A machine learning-based estimation was used to score signal quality (MLSQE). Inclusion criteria were 30 seconds of consecutive ECG recording and complete data of pharmacy visit. In total, 6732 participants were eligible for analysis. Results An incorrect automated heart rhythm diagnosis was found for 206 subjects (false positive: 112; false negative: 94). By human overread 80% (N=5377) of the recorded ECGs were rated with excellent and good signal quality in which heart rhythm diagnosis based on visible atrial electrical activity (Figure 1A). The proportion of incorrect automated rhythm diagnosis was higher in ECGs with poorer signal quality. A regression analysis revealed patient characteristics prone to incorrect heart rhythm diagnosis included male sex (OR 1.61 95% confidence interval (CI) 1.22-2.13, P<0.001), age (per year OR 1.06 95%CI 1.04-1.09, P<0.001), body height (per cm OR 1.02 95%CI 1.00-1.03, P=0.03), and body weight (per kg OR 0.99 95%CI 0.98-0.99, P=0.005). A multivariate model for prediction of correct heart rhythm diagnosis showed a modest area under the curve (AUC) of 0.65 (sensitivity 61%, specificity 58%; Figure 1B). The prediction improved by adding information on signal quality rated by human overread (AUC 0.77, sensitivity 77%, specificity 62%) and even more by the machine-learning based MLSQE score (AUC 0.87, sensitivity 71%, specificity 90%). Conclusion The patient characteristics prone to incorrect automated heart rhythm diagnosis of male sex, older age, greater height, and lower body weight probably reflect the target population of many AF screening cohorts. Automated MLSQE scoring for ECG signal quality could improve accuracy in clinical use and potentially reduces the need for cumbersome and time-consuming human overread.Figure 1A - Signal quality by human rating and proportion of incorrect automated heart rhythm diagnosis. B - Prediction of correct heart rhythm diagnosis. Blue line - Multivariate model with forward selection considering age, height, weight, body mass index, congestive heart failure, hypertension, diabetes mellitus, vascular disease, stroke/TIA and sex. Purple line - Model with human signal quality (SQ) score. Green line – Model with MLSQE score.

Heartbeat classification based on single lead-II ECG using deep learning

The analysis and processing of electrocardiogram (ECG) signals is a vital step in the diagnosis of cardiovascular disease. ECG offers a non-invasive and risk-free method for monitoring the electrical activity of the heart that can assist in predicting and diagnosing heart diseases. The manual interpretation of the ECG signals, however, can be challenging and time-consuming even for experts. Machine learning techniques are increasingly being utilized to support the research and development of automatic ECG classification, which has emerged as a prominent area of study. In this paper, we propose a deep neural network model with residual blocks (DNN-RB) to classify cardiac cycles into six ECG beat classes. The MIT-BIH dataset was used to validate the model resulting in a test accuracy of 99.51%, average sensitivity of 99.7%, and average specificity of 98.2%. The DNN-RB method has achieved higher accuracy than other state-of-the-art algorithms tested on the same dataset. The proposed method is effective in the automatic classification of ECG signals and can be used for both clinical and out-of-hospital monitoring and classification combined with a single-lead mobile ECG device. The method has also been integrated into a web application designed to accept digital ECG beats as input for analyses and to display diagnostic results.

Cost-effectiveness of atrial fibrillation screening in Canadian community practice.

Contemporary guidelines recommend opportunistic screening for atrial fibrillation (AF). The objective of this study was to assess the cost-effectiveness of single time point opportunistic AF screening for patients 65 years and older by using the single-lead electrocardiogram. An established Markov cohort model was adapted by updating the background mortality estimates, epidemiology, screening efficacy, treatment patterns, resource use, and cost inputs to reflect a Canadian health care setting. Inputs were derived from a contemporary prospective screening study performed in Canadian primary care settings (screening efficacy and epidemiology) and the published literature (unit costs, epidemiology, mortality, utility, and treatment efficacy). The impact of screening and oral anticoagulant treatment on the cost and clinical outcomes was analyzed. A Canadian payer perspective over lifetime was used for analysis, with costs expressed in 2019 Canadian dollars. Among the estimated screening-eligible population of 2,929,301 patients, the screening cohort identified an additional 127,670 AF cases compared with the usual care cohort. The model estimated avoidance of 12,236 strokes and incremental quality-adjusted life-years of 59,577 (0.02 per patient) over lifetime in the screening cohort. Cost savings were substantial because of improved health outcomes, reflecting screening being the dominant strategy (affordable and effective). Model results were robust across sensitivity and scenario analyses. Single time point opportunistic screening of AF using a single-lead electrocardiogram device in Canadian patients 65 years and older without known AF may provide improved health outcomes with cost savings from the perspective of a single payer health care environment.

Prevalence and risk factors for atrial fibrillation in a semi-rural sub-Saharan African population: The hEart oF ethiopia: Focus on Atrial Fibrillation (TEFF-AF) Study.

There is a scarcity of reported data on the prevalence of atrial fibrillation (AF) in sub-Saharan Africa. To undertake AF screening in semi-rural Ethiopia. The TEFF-AF (The hEart oF Ethiopia: Focus on Atrial Fibrillation) study conducted AF screening using a single-lead electrocardiogram device (KardiaMobile) on willing community participants at the Soddo Christian Hospital, Ethiopia. Participants' clinical parameters and medical history were obtained to characterize their risk factor profile, including calculation of CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology Atrial Fibrillation) score. A total of 3000 Ethiopians (median 31 [interquartile range 25-41] years of age; 65% men) were screened. The participants were generally well educated, from the local region and with a low burden of cardiovascular risk factors. A total of 50 participants had a CHARGE-AF score (5-year AF risk) of ≥2%. AF was detected in 13 (0.43%) individuals (median 50 [interquartile range 36-60] years of age; n = 7 men). The prevalence among participants over 40 years of age was 1% (n = 9 of 930). AF prevalence was higher for older age groups, with ≥70 years of age reaching 6.67% (n = 3 of 45). Population prevalence was estimated to be 234 (95% confidence interval 7-460) per 10,000 persons for ≥60 years of age. Four (31%) of the 13 participants with AF had a CHA2DS2-VASc (congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, prior stroke or transient ischemic attack or thromboembolism, vascular disease, age 65-74 years, sex category) score of ≥2, and others likely had rheumatic valvular AF, but only 2 of the 13 participants with AF were on oral anticoagulation therapy. In this semi-rural Ethiopian community of relatively younger participants, AF prevalence was found to be low but increased with increasing age. Mobile single-lead electrocardiogram technology can be used effectively for AF screening in low-resource settings.

Systematic Screening for Atrial Fibrillation in Patients at Moderate-to-High Risk of Stroke - Potential to Increase the Atrial Fibrillation Detection Rate (SCAN-AF).

To determine the rate of undiagnosed atrial fibrillation (AF) we screened for AF using an oscillometric blood pressure (BP) monitor device followed by a single-lead handheld electrocardiogram (ECG), with confirmation by 12-lead ECG as the reference standard.Methods and Results: From October 2017 to August 2019, 1,148 patients were enrolled without known AF, who were aged ≥65 years with moderate-to-high stroke risk, at 71 centers in Japan. After exclusion of 7 patients with confirmed AF at the index visit, 1,141 patients were asked to use an oscillometric BP monitor twice daily for 2 weeks (max: 4 weeks) to detect an irregular pulse. The BP monitor detected an irregular pulse in 481 patients, of which 1 patient had confirmed AF. Thereafter, 480 patients were instructed to acquire ECGs twice daily for an additional 2 weeks (max: 4 weeks) using a single-lead handheld ECG device. The handheld ECG device detected irregular rhythm in 41 patients, of which 1 patient had confirmed AF. In total, undiagnosed AF was confirmed in 9 (0.8%) patients of the overall study cohort during the 24-week follow-up period. Sequential use of a BP monitor and handheld ECG for 4 weeks is a practical strategy for identifying undiagnosed AF in Japanese people at heightened risk of stroke.

Piloting opportunistic cardiovascular risk assessment at a faith community event

Abstract Funding Acknowledgements Type of funding sources: None. Background Ethnic minority populations in the United Kingdom (UK) are known to have higher rates of undiagnosed and poorly controlled hypertension and metabolic disease (1). Muslims in the UK make up a significant proportion of these populations, and have lower reported rates of well-being and satisfaction with health services (2). Tailoring interventions to target under-served populations and activate positive health behaviours has been shown to improve uptake and outcomes (3). Purpose We undertook an opportunistic pilot intervention to explore the potential of using faith-placed community assets to identify undiagnosed and poorly managed cases of hypertension, diabetes, and explore cardiovascular disease (CVD) risk. Methods A community event was organised for Indian-origin Tamil speaking Muslims who gathered from across the UK in November 2021. We piloted a short CVD risk assessment intervention to take place during this event, delivered by doctors from the same community. Participants consented for assessment of body mass index (BMI), blood pressure (BP), random capillary blood glucose (CBG), and manual pulse rhythm check (with a commercially available smartphone based single-lead electrocardiogram device where needed). We also calculated the 10-year CVD risk using a nationally validated 10-year CVD risk calculator. The intervention was culturally tailored, specifically with men and women examined by the same sex and participants being given an Islamic reminder on good health to encourage their involvement. Results 35 attendees were assessed in total. 33/35 (94%) attendees were 60-years-old or less. 18/35 (51%) had an elevated blood pressure reading (systolic BP ≥ 140mmHg or diastolic BP ≥ 90mmHg on both arms), with only 3/18 (17%) having a prior diagnosis of hypertension. Only 17/35 consented to CBG measurement, of which 4/17 (23%) had random CBG levels of >11.1 mmol/L with 3/17 having CBG levels of >25 mmol/L - none of them were known to have diabetes. 9/35 (26%) had 10-year CVD risk of >10%. 6/9 (67%) of these were not on statin therapy. 24/35 (69%) had elevated BMI, with 9/24 (37%) having a BMI of ≥ 30 kg/m2. None of the attendees had an irregular pulse to suggest arrhythmia. Full characteristics are shown in Figure 1 and results are further detailed in Figure 2. In Summary, 29/35 (83%) had at least one abnormal finding on assessment. All participants with abnormal findings were signposted to seek formal diagnosis and management through their general practitioner, and were offered lifestyle advice. Conclusions This brief and targeted intervention was successful at detecting undiagnosed cases of probable hypertension and hyperglycaemia, as well as identifying cases who would benefit from primary CVD risk prevention. Further research is needed to understand the scalability and feasibility of this approach to wider populations.

Automatic Mobile Health Arrhythmia Monitoring for the Detection of Atrial Fibrillation: Prospective Feasibility, Accuracy, and User Experience Study

BackgroundAtrial fibrillation (AF) is the most common tachyarrhythmia and associated with a risk of stroke. The detection and diagnosis of AF represent a major clinical challenge due to AF’s asymptomatic and intermittent nature. Novel consumer-grade mobile health (mHealth) products with automatic arrhythmia detection could be an option for long-term electrocardiogram (ECG)-based rhythm monitoring and AF detection.ObjectiveWe evaluated the feasibility and accuracy of a wearable automated mHealth arrhythmia monitoring system, including a consumer-grade, single-lead heart rate belt ECG device (heart belt), a mobile phone application, and a cloud service with an artificial intelligence (AI) arrhythmia detection algorithm for AF detection. The specific aim of this proof-of-concept study was to test the feasibility of the entire sequence of operations from ECG recording to AI arrhythmia analysis and ultimately to final AF detection.MethodsPatients (n=159) with an AF (n=73) or sinus rhythm (n=86) were recruited from the emergency department. A single-lead heart belt ECG was recorded for 24 hours. Simultaneously registered 3-lead ECGs (Holter) served as the gold standard for the final rhythm diagnostics and as a reference device in a user experience survey with patients over 65 years of age (high-risk group).ResultsThe heart belt provided a high-quality ECG recording for visual interpretation resulting in 100% accuracy, sensitivity, and specificity of AF detection. The accuracy of AF detection with the automatic AI arrhythmia detection from the heart belt ECG recording was also high (97.5%), and the sensitivity and specificity were 100% and 95.4%, respectively. The correlation between the automatic estimated AF burden and the true AF burden from Holter recording was >0.99 with a mean burden error of 0.05 (SD 0.26) hours. The heart belt demonstrated good user experience and did not significantly interfere with the patient’s daily activities. The patients preferred the heart belt over Holter ECG for rhythm monitoring (85/110, 77% heart belt vs 77/109, 71% Holter, P=.049).ConclusionsA consumer-grade, single-lead ECG heart belt provided good-quality ECG for rhythm diagnosis. The mHealth arrhythmia monitoring system, consisting of heart-belt ECG, a mobile phone application, and an automated AF detection achieved AF detection with high accuracy, sensitivity, and specificity. In addition, the mHealth arrhythmia monitoring system showed good user experience.Trial RegistrationClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335

Portable single-lead electrocardiogram device is accurate for QTc evaluation in hospitalized patients.

BackgroundMany commonly used drugs can prolong the QTc interval (QTc), which can lead to potentially life-threatening arrhythmias. In the current era of the COVID-19 pandemic, it is worth mentioning that the disease itself and several drugs used for its treatment have been associated with QTc prolongation.ObjectiveTo evaluate the agreement and clinical precision of a portable single-lead electrocardiogram (ECG) device to measure the QTc interval compared to the standard 12-lead ECG.MethodsIn sequential tests, QTc of ECG recordings obtained with the KardiaMobile (KM-1L) device (AliveCor, San Francisco, CA) were compared to QTc obtained with conventional 12-lead ECG. Agreement was evaluated using Bland-Altman plots and Lin’s concordance coefficient. Consistency between the 2 devices in determining QTc prolongation (QTc ≥470 ms in males or ≥480 ms in females) was evaluated with kappa statistics.ResultsA total of 128 patients with a presumed or confirmed diagnosis of COVID-19 admitted to a university hospital were included. QTc intervals measured with KM-1L were similar to QTc measured with conventional ECG (442.45 ± 40.5 vs 441.65 ± 40.3 ms, P = .15). Bland–Altman analysis showed no significant difference in QTc values (average difference of -0.797, 95% limits of agreement:-13.179; 11.585). Lin’s concordance coefficient showed an excellent agreement (0.988, P < .001). Concordance between the 2 devices for determining QTc prolongation was excellent (kappa >0.90).ConclusionECG recordings obtained with KM-1L allow an accurate QTc interval assessment. Considering its simplicity of use, this approach has advantages over conventional ECG and can provide an alternative for the evaluation of QTc in hospitalized patients, during the current time of the COVID-19 pandemic and beyond.

Performance of automated single-lead ECG atrial fibrillation screening is associated with patient characteristics

Abstract Funding Acknowledgements Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): The investigators received and unrestricted research grant by Pfizer/BMS. Pfizer/BMS was not involved in the planning, conduction, analysis, or interpretation of the data. Introduction Screening for atrial fibrillation (AF) with a single-lead electrocardiogram device is on the rise. However, little is known about influence of automated AF screening performance related to patient characteristics. Aim We tested the accuracy of automated AF detection of a single-lead ECG device and identified factors associated with diagnostic performance. Methods In 6482 subjects of community-pharmacies a single-time point AF screening was performed. All ECGs were analyzed by blinded human overread and compared to the automated results in context of patient characteristics. Results Automated screening showed good prediction of AF with an area under the receiver operating curve of 0.89; sensitivity 80%; specificity 98%; positive predictive value 71%; negative predictive value 99%. Good ECG signal quality was highly associated with correct measurement, while low signal quality leads to incorrect measurements. In a multivariate model we determined factors associated with excellent signal quality and as counterexample incorrect automatic AF identification. The Odds’ ratio (OR) for excellent signal quality was strongly associated with female sex, lower age, lower height, and higher body weight index (table). Conclusion The performance of automated AF screening is influenced by sex, age, height and body mass index. Potential target population groups, with high AF prevalence, have a higher chance of incorrect automatic measurement. We recommend an expert over-read, at least for all AF positive ECG recordings. Table 1 Excellent signal quality Incorrect measurement Univariate analysis Multivariate analysis Univariate analysis Multivariate analysis Parameter OR 95% CI P OR 95% CI P OR 95% CI P OR 95% CI P Sex [Female] 2.33 1.97-2.75 &amp;lt;0.001 1.92 1.53-2.41 &amp;lt;0.001 0.64 0.49-0.84 0.001 0.57 0.43-0.76 &amp;lt;0.001 Age [years] 0.97 0.96-0.98 &amp;lt;0.001 0.97 0.96-0.98 &amp;lt;0.001 1.07 1.04-1.09 &amp;lt;0.001 1.06 1.04-1.09 &amp;lt;0.001 Height [cm] 0.96 0.95-0.97 &amp;lt;0.001 0.98 0.97-0.99 0.003 1.01 0.99-1.03 0.068 Weight [kg] 0.99 0.99-1.00 0.418 0.99 0.98-0.99 0.003 BMI [kg/cm2] 1.04 1.03-1.06 &amp;lt;0.001 1.04 1.03-1.06 &amp;lt;0.001 0.91 0.88-0.95 &amp;lt;0.001 0.91 0.87-0.94 &amp;lt;0.001 CHADSVASC 1 0.95-1.06 0.912 1.06 0.97-1.17 0.205 Heart failure 0.62 0.41-0.93 0.022 1.86 1.13-3.05 0.015 Hypertension 0.96 0.83-1.11 0.58 1.06 0.80-1.39 0.689 Diabetes mellitus 0.85 0.68-1.07 0.159 0.82 0.54-1.25 0.359 Stroke / TIA 0.82 0.66-1.01 0.066 1.19 0.83-1.69 0.341 Vascular disease 0.89 0.75-1.07 0.213 1.31 0.98-1.77 0.70 OR – odd’s ratio, CI – confidence interval

Validation of Adhesive Single-Lead ECG Device Compared with Holter Monitoring among Non-Atrial Fibrillation Patients.

There are few reports on head-to-head comparisons of electrocardiogram (ECG) monitoring between adhesive single-lead and Holter devices for arrhythmias other than atrial fibrillation (AF). This study aimed to compare 24 h ECG monitoring between the two devices in patients with general arrhythmia. Twenty-nine non-AF patients with a workup of pre-diagnosed arrhythmias or suspicious arrhythmic episodes were evaluated. Each participant wore both devices simultaneously, and the cardiac rhythm was monitored for 24 h. Selective ECG parameters were compared between the two devices. Two cardiologists independently compared the diagnoses of each device. The two most frequent monitoring indications were workup of premature atrial contractions (41.4%) and suspicious arrhythmia-related symptoms (37.9%). The single-lead device had a higher noise burden than the Holter device (0.04 ± 0.05% vs. 0.01 ± 0.01%, p = 0.024). The number of total QRS complexes, ventricular ectopic beats, and supraventricular ectopic beats showed an excellent degree of agreement between the two devices (intraclass correlation coefficients = 0.991, 1.000, and 0.987, respectively). In addition, the minimum/average/maximum heart rates showed an excellent degree of agreement. The two cardiologists made coherent diagnoses for all 29 participants using both monitoring methods. In conclusion, the single-lead adhesive device could be an acceptable alternative for ambulatory ECG monitoring in patients with general arrhythmia.

57Accuracy of automated atrial fibrillation detection of a single-lead electrocardiogram device in a pharmacy-based mass screening

Abstract Funding Acknowledgements The investigators received an unrestricted research grant by Pfizer/BMS. Pfizer/BMS was not involved in the planning, conduct, analysis, or interpreta Background Recently, various devices specifically designed to automatically detect atrial fibrillation, became available. Utility of automated atrial fibrillation detection in mass-screening remains unclear, because accuracy and validation of automated AF detection were mostly tested retrospectively in preselected cohorts with high atrial fibrillation prevalence. Purpose The aim of this analysis was to determine the accuracy of automated atrial fibrillation detection by a single-lead hand-held ECG device in a prospective population-based screening approach with several thousand subjects. Methods For automated atrial fibrillation detection, we investigated the MyDiagnostick a 30 cm long hand-held ECG-stick. All customers aged 65 years or older in the pharmacies were eligible to participate. After informed consent was obtained, a one-minute single-lead ECG was recorded by trained staff. Five levels of human ECG expert validation were performed to determine heart rhythm. All recorded unfiltered ECGs (Figure 1) were analyzed at least by two independent, blinded investigators, in case of an automated AF detection by at least 3 independent, blinded investigators. In case of contradiction in the first three levels of heart rhythm assessment, ECGs were analyzed in level 4 by two independent cardiologists. In level 5 all single-lead ECGs were unblinded and any single-lead ECG with a contradiction between the human ECG expert results and the automated ECG-Stick result were analyzed by a rhythm consortium consisting of 3 cardiologists. Final results for heart rhythm and ECG quality were stipulated by a hierarchical approach, whereas results in higher levels of analysis vote down results in earlier levels of analysis in case of a contradiction. Results In total, 7209 single-lead ECGs were available for analysis. The mean age was 74 ± 6 years, 42% (N = 3049) of participants were male, mean CHA2DS2-VASc score was 3.3 ± 1.4. We identified 5.3% (N = 383) patients with atrial fibrillation identified by human investigators. The ECG-Stick diagnosed 79% (N = 304) of the atrial fibrillation patients correctly. In 21% (N = 79/383) the result was false-negative, and in 1% (N = 72/7209) false-positive. In another 1% (N = 81/7209) single-lead ECG recordings were uninterpretable by expert investigators, although the automated analysis provided a result (SR: 27, atrial fibrillation: 54). Using final level results by expert ECG reading as reference, automated ECG-Stick analysis showed good prediction in receiver operating calculation of atrial fibrillation with an area under the curve of 0.89, sensitivity: 80%, specificity: 98%, and a good interclass correlation coefficient of 0.75, 95%CI (0.74; 0.76)). Conclusions The results encourage the implementation of single-lead ECG devices for automated atrial fibrillation screening. A rhythm diagnosis by single-lead ECG seems possible for recordings with excellent ECG signal quality. Abstract Figure. Single-lead ECG example (5 of 60 sec)

Accuracy of a smartwatch based single-lead electrocardiogram device in detection of atrial fibrillation

ObjectiveThe AliveCor KardiaBand (KB) is an Food and Drug Administration-approved smartwatch-based cardiac rhythm monitor that records a lead-Intelligent ECG (iECG). Despite the appeal of wearable integrated ECG devices, there is...

P572Accuracy of a smartwatch based single-lead electrocardiogram device in screening for atrial fibrillation

P572Accuracy of a smartwatch based single-lead electrocardiogram device in screening for atrial fibrillation

Design and rationale of a pragmatic trial integrating routine screening for atrial fibrillation at primary care visits: The VITAL-AF trial

Given the preventable morbidity and mortality associated with atrial fibrillation (AF), increased awareness of undiagnosed AF, and advances in mobile electrocardiogram (ECG) technology, there is a critical need to assess the effectiveness of using such technology to routinely screen for AF in clinical practice. VITAL-AF is a pragmatic trial that will test whether screening for AF using a single-lead handheld ECG in individuals 65 years or older during primary care visits will lead to an increased rate of AF detection. The study is a cluster-randomized trial, with 8 primary care practices randomized to AF screening and 8 primary care practices randomized to usual care. We anticipate studying approximately 16,000 patients in each arm. During the 1-year enrollment period, practice medical assistants will screen eligible patients who agree to participate during office visits using a single-lead ECG device. Automated screening results are documented in the electronic health record, and patients can discuss screening results with their provider during the scheduled visit. All single-lead ECGs are overread by a cardiologist. Screen-detected AF is managed at the discretion of the patient's physician. The primary study end point is incident AF during the screening period. Key secondary outcomes include new oral anticoagulation prescriptions, incident ischemic stroke, and major hemorrhage during a 24-month period following the study start. Outcomes are ascertained based on electronic health record documentation and are manually adjudicated. The results of this pragmatic trial may help identify a model for widespread adoption of AF screening as part of routine clinical practice.

Modified positioning of a smartphone based single-lead electrocardiogram device improves detection of atrial flutter

IntroductionThe AliveCor Kardia Mobile (AKM) is a handheld, smartphone based cardiac rhythm monitor that records a lead-I electrocardiogram (ECG). Despite being efficacious for detection of atrial fibrillation (AF), it is unclear whether atrial flutter (AFL) may be misdiagnosed as sinus rhythm due to regular R-R intervals. We hypothesised that generating lead-II tracings through repositioning of the AKM may improve visualisation of flutter waves and clinician diagnosis of AFL compared to traditional lead-I tracings. Materials and methodsA prospective, multi-centre, validation study was conducted comparing standard lead-I AKM positioning with lead-II in AFL. A mixed cohort of lead I tracings from patients in AF and sinus rhythm were also included. Two independent electrophysiologists (EP) analysed all ECGs blinded to the automated device diagnosis. ResultsFifty patients were recruited, 11 in atrial flutter, 14 in atrial fibrillation, and 25 in sinus rhythm. Lead-I AFL sensitivity was 27.3% for both EP's which individually improved to 72.7% and 54.6% in lead-II. AKM appropriately diagnosed lead-I AFL as unclassified in 18.2% of cases, compared to 54.5% in lead-II. Overall clinician agreement (AF, SR and AFL) was modest utilising AFL lead-I (EP1: κ = 0.71, EP2: κ = 0.73, p < 0.001), which improved with lead-II tracings (EP1: κ = 0.87, EP2: κ = 0.83, both p < 0.001). ConclusionRepositioning of the AKM device improves clinician diagnosis of atrial flutter. A lead-II tracing may be considered in high-risk patients to improve detection of atrial flutter.

Cost-effectiveness of screening for atrial fibrillation in primary care with a handheld, single-lead electrocardiogram device in the Netherlands.

Atrial fibrillation (AF) is the most common arrhythmia and prevalence increases with age. Patients with AF have a high risk of stroke, and screening for AF is recommended in all people aged 65 years or older to identify patients eligible for stroke prevention. A handheld, single-lead electrocardiogram (ECG) device can be used for systematic screening in the population at risk. The objective of this study is to estimate the cost-effectiveness of screening for AF in primary care with the MyDiagnostick® during seasonal influenza vaccination in the Netherlands. Lifetime costs and effects of a single screening session for AF detection were assessed from a societal perspective with a decision analytic model consisting of a straightforward decision tree and a joining Markov model. The decision model simulated all patients aged 65 years and over attending the seasonal influenza vaccination in the Netherlands. Event probabilities were derived from clinical trials. Sensitivity analyses were performed to assess the impact of important model assumptions as well as determining the relative effect of individual parameters. Screening for AF with the MyDiagnostick® in all patients older than 65 years that attend seasonal influenza vaccination in the Netherlands would decrease the overall costs by €764 and increase the quality-adjusted life-years (QALYs) by 0.27 years per patient. Early detection of AF would prevent strokes and leads to beneficial health effects with subsequent cost savings. This screening method would have an estimated probability of 99.8% for being cost-effective at a conservative willingness-to-pay of €20 000/QALY. Screening for AF in primary care with a handheld, single-lead ECG during seasonal influenza vaccination is very likely to be cost saving for identifying new cases of AF in the Dutch population aged 65 years and over. Active screening for AF with a single-lead, handheld ECG device during seasonal influenza vaccination could be implemented in primary care.

Yield of screening for atrial fibrillation in primary care with a hand-held, single-lead electrocardiogram device during influenza vaccination.

AimsTo assess the yield of screening for atrial fibrillation (AF) with a hand-held single-lead electrocardiogram (ECG) device during influenza vaccination in primary care in the Netherlands.Methods and resultsWe used the MyDiagnostick to screen for AF in persons who participated in influenza vaccination sessions of ten Dutch primary care practices. In case of suspected AF detection by the stick, the recorded 1-min ECG registrations were analysed by a cardiologist. We scrutinized electronic medical files of the general practitioners to obtain information about the cases screened. Multivariable logistic regression analysis was performed to predict the relation between patient characteristics and a new screen-detected diagnosis of AF. In total, 3269 persons were screened for AF during the influenza vaccination sessions of 10 general practitioner practices. As a result, 37 (1.1%) new cases of AF were detected. Prior transient ischeamic attack or stroke (OR 6.05; 95%CI 1.93–19.0), and age (OR 1.09 per year; 95% CI 1.05–1.14) were independent predictors for such newly screen-detected AF. Of the 37 screen-detected AF cases, 2.7% had a CHA2DS2-VASc of 0, 18.9% a score of 1, and 78.4% a score of 2 or more. The majority needed oral anticoagulant therapy.ConclusionsScreening seems feasible with an easy to use single-lead, hand-held ECG device with automatic AF detection during influenza vaccination in primary care and results in a ‘1-day’ yield of 1.1% new cases of AF.Trial registration clinicaltrials.govNCT02006524.