Lead-I ECG for detecting atrial fibrillation in patients attending primary care with an irregular pulse using single-time point testing: A systematic review and economic evaluation

Development of an Artificial Intelligence-Enabled Electrocardiography to Detect 23 Cardiac Arrhythmias and Predict Cardiovascular Outcomes

Arrhythmias are common and can affect individuals with or without structural heart disease. Deep learning models (DLMs) have shown the ability to recognize arrhythmias using 12-lead electrocardiograms (ECGs). However, the limited types of arrhythmias and dataset robustness have hindered widespread adoption. This study aimed to develop a DLM capable of detecting various arrhythmias across diverse datasets. This algorithm development study utilized 22,130 ECGs, divided into development, tuning, validation, and competition sets. External validation was conducted on three open datasets (CODE-test, PTB-XL, CPSC2018) comprising 32,495 ECGs. The study also assessed the long-term risks of new-onset atrial fibrillation (AF), heart failure (HF), and mortality in individuals with false-positive AF detection by the DLM. In the validation set, the DLM achieved area under the receiver operating characteristic curve above 0.97 and sensitivity/specificity exceeding 90% across most arrhythmia classes. It demonstrated cardiologist-level performance, ranking first in balanced accuracy in a human-machine competition. External validation confirmed comparable performance. Individuals with false-positive AF detection had a significantly higher risk of new-onset AF (hazard ration [HR]: 1.69, 95% confidence interval [CI]: 1.11-2.59), HF (HR: 1.73, 95% CI: 1.20-2.51), and mortality (HR: 1.40, 95% CI: 1.02-1.92) compared to true-negative individuals after adjusting for age and sex. We developed an accurate DLM capable of detecting 23 cardiac arrhythmias across multiple datasets. This DLM serves as a valuable screening tool to aid physicians in identifying high-risk patients, with potential implications for early intervention and risk stratification.

Subclinical Atrial Fibrillation Screening in Dialytic Chronic Kidney Disease Patients Using Portable Device

BACKGROUND

Cardiovascular morbidity and mortality rates are higher in hemodialysis (HD) patients, with an increased prevalence of arrhythmias. Atrial fibrillation (AF) is an independent risk factor for mortality and thromboembolic events in dialysis patients. For a better understanding and management of AF in these patients, it is important to know its prevalence. The use of a portable device would be pioneering for this group of patients.

OBJECTIVE

To screen HD patients for AF using a portable gadget and evaluate the device's diagnostic performance.

METHODS

HD patients at a tertiary hospital underwent AF screening during HD sessions using MyDiagnostick® (Applied Biomedical Systems). Multiple data were collected to evaluate potential associations. Statistical significance was defined as p < 0.05.

RESULTS

388 patients were evaluated (female, 40.7%; mean age of 56.8 years old, SD ± 14.7; and HD time of 27 months, 10-57). Screening was positive in 16 (4.1%) patients. AF was confirmed by electrocardiogram in 7 (1.8%) patients. Male sex (p = 0.019), older age (p = 0.007), altered baseline electrocardiogram (p < 0.001), increased serum potassium (p = 0.021), reduced systolic blood pressure at the beginning of dialysis (p = 0.007), and stable angina (0.011) were associated with positive screening for AF. The device presented a 91.74% specificity (95% CI, 86.65% to 96.91%) and 100% sensitivity (95% CI, 100% to 100%), with a negative predictive value of 100% (95% CI, 100% to 100%) for AF screening.

CONCLUSION

The use of this device proved to be practical, with high sensitivity and excellent negative predictive value. Subclinical AF has a high prevalence and may be underestimated in this population.

Catch-AF-Early Diagnosis of Symptomatic Arrythmias in the Waiting Period Prior to Seeing a Cardiologist in Victoria, British Columbia

Background

Atrial fibrillation (AF) is the most common cardiac arrhythmia. Given its often-paroxysmal nature, screening at a single time point, using a 12-lead electrocardiogram (ECG) or a Holter monitor, has limited benefit. The AliveCor KardiaMobile device is a validated ECG recorder that can be used for patient-directed arrhythmia diagnosis and symptom-rhythm correlation. The aim of this study was to evaluate whether using the KardiaMobile device could reduce the time-to-diagnosis, for AF as well as other arrhythmias. We hypothesized that providing patients with a KardiaMobile device during their waiting period for specialist care could reduce the length of time that passes before ECG detection of arrhythmia.

Methods

Patients were randomized 1:1 to receive either standard monitoring (ECG and a Holter monitor) or enhanced monitoring (ECG, a Holter monitor, and a KardiaMobile device). Patients were instructed to upload ECG recordings if they had cardiac symptoms, so that symptom-rhythm correlation could be achieved. The primary outcome was the time-to-diagnosis for AF. The secondary endpoint was the time-to-diagnosis for any arrhythmias.

Results

From October 2018 to October 2022, a total of 69 patients were enrolled, and they were followed up to 12 months. Overall, 6 of the 7 patients diagnosed with AF were in the enhanced-monitoring group (P = 0.106). The time-to-diagnosis was not significantly different in the 2 groups (P = 0.053). Overall arrhythmias were diagnosed in 10 patients (29%) in the standard-monitoring arm, compared to 22 patients (63%) in the enhanced-monitoring arm (P = 0.008). The time-to-diagnosis was reduced in the enhanced-monitoring arm (P = 0.010).

Conclusions

The time-to-diagnosis of any arrhythmia was reduced significantly in patients randomized to receive KardiaMobile device monitoring. Providing patients with a KardiaMobile device may expedite the diagnosis of arrhythmias during the waiting period for specialist care.

Clinical Trial Registration

NCT04302311.

Automatic detection of cardiac conditions from photos of electrocardiogram captured by smartphones

BACKGROUND

Researchers have developed machine learning-based ECG diagnostic algorithms that match or even surpass cardiologist level of performance. However, most of them cannot be used in real-world, as older generation ECG machines do not permit installation of new algorithms.

OBJECTIVE

To develop a smartphone application that automatically extract ECG waveforms from photos and to convert them to voltage-time series for downstream analysis by a variety of diagnostic algorithms built by researchers.

METHODS

A novel approach of using objective detection and image segmentation models to automatically extract ECG waveforms from photos taken by clinicians was devised. Modular machine learning models were developed to sequentially perform waveform identification, gridline removal, and scale calibration. The extracted data were then analysed using a machine learning-based cardiac rhythm classifier.

RESULTS

Waveforms from 40 516 scanned and 444 photographed ECGs were automatically extracted. 12 828 of 13 258 (96.8%) scanned and 5399 of 5743 (94.0%) photographed waveforms were correctly cropped and labelled. 11 604 of 12 735 (91.1%) scanned and 5062 of 5752 (88.0%) photographed waveforms achieved successful voltage-time signal extraction after automatic gridline and background noise removal. In a proof-of-concept demonstration, an atrial fibrillation diagnostic algorithm achieved 91.3% sensitivity, 94.2% specificity, 95.6% positive predictive value, 88.6% negative predictive value and 93.4% F1 score, using photos of ECGs as input.

CONCLUSION

Object detection and image segmentation models allow automatic extraction of ECG signals from photos for downstream diagnostics. This novel pipeline circumvents the need for costly ECG hardware upgrades, thereby paving the way for large-scale implementation of machine learning-based diagnostic algorithms.

Comprehensive atrial fibrillation burden and symptom reduction post-ablation: insights from DECAAF II

AIMS

Traditional atrial fibrillation (AF) recurrence after catheter ablation is reported as a binary outcome. However, a paradigm shift towards a more granular definition, considering arrhythmic or symptomatic burden, is emerging. We hypothesize that ablation reduces AF burden independently of conventional recurrence status in patients with persistent AF, correlating with symptom burden reduction.

METHODS AND RESULTS

Ninety-eight patients with persistent AF from the DECAAF II trial with pre-ablation follow-up were included. Patients recorded daily single-lead electrocardiogram (ECG) strips, defining AF burden as the proportion of AF days among total submitted ECG days. The primary outcome was atrial arrhythmia recurrence. The AF severity scale was administered pre-ablation and at 12 months post-ablation. At follow-up, 69 patients had atrial arrhythmia recurrence and 29 remained in sinus rhythm. These patients were categorized into a recurrence (n = 69) and a no-recurrence group (n = 29). Both groups had similar baseline characteristics, but recurrence patients were older (P = 0.005), had a higher prevalence of hyperlipidaemia (P = 0.007), and had a larger left atrial (LA) volume (P = 0.01). There was a reduction in AF burden in the recurrence group when compared with their pre-ablation burden (65 vs. 15%, P < 0.0001). Utah Stage 4 fibrosis and diabetes predicted less improvement in AF burden. The symptom severity score at 12 months post-ablation was significantly reduced compared with the pre-ablation score in the recurrence group, and there was a significant correlation between the reduction in symptom severity score and the reduction in AF burden (R = 0.39, P = 0.001).

CONCLUSION

Catheter ablation reduces AF burden, irrespective of arrhythmia recurrence post-procedure. There is a strong correlation between AF burden reduction and symptom improvement post-ablation. Notably, elevated LA fibrosis impedes AF burden decrease following catheter ablation.

Evaluation of the introduction of a single-lead ECG device and digital cardiologist consultation platform among general practitioners in the Netherlands

AIM

To evaluate the use of a single-lead electrocardiography (1L-ECG) device and digital cardiologist consultation platform in diagnosing arrhythmias among general practitioners (GPs).

BACKGROUND

Handheld 1L-ECG offers a user-friendly alternative to conventional 12-lead ECG in primary care. While GPs can safely rule out arrhythmias on 1L-ECG recordings, expert consultation is required to confirm suspected arrhythmias. Little is known about GPs' experiences with both a 1L-ECG device and digital consultation platform for daily practice.

METHODS

We used two distinct methods in this study. First, in an observational study, we collected and described all cases shared by GPs within a digital cardiologist consultation platform initiated by a local GP cooperative. This GP cooperative distributed KardiaMobile 1L-ECG devices among all affiliated GPs (n = 203) and invited them to this consultation platform. In the second part, we used an online questionnaire to evaluate the experiences of these GPs using the KardiaMobile and consultation platform.

FINDINGS

In total, 98 (48%) GPs participated in this project, of whom 48 (49%) shared 156 cases. The expert panel was able to provide a definitive rhythm interpretation in 130 (83.3%) shared cases and answered in a median of 4 min (IQR: 2-18). GPs responding to the questionnaire (n = 43; 44%) thought the KardiaMobile was of added value for rhythm diagnostics in primary care (n = 42; 98%) and easy to use (n = 41; 95%). Most GPs (n = 36; 84%) valued the feedback from the cardiologists in the consultation platform. GPs experienced this project to have a positive impact on both the quality of care and diagnostic efficiency for patients with (suspected) cardiac arrhythmias. Although we lack a comprehensive picture of experienced impediments by GPs, solving technical issues was mentioned to be helpful for further implementation. More research is needed to explore reasons of GPs not motivated using these tools and to assess real-life clinical impact.

Effectiveness of single-lead ECG devices for detecting atrial fibrillation: An overview of systematic reviews

BACKGROUND

Individuals with atrial fibrillation (AF) are at an increased risk for stroke. Early detection of undiagnosed AF by screening is recommended. Single-lead electrocardiogram (ECG) is the most widely used technology in AF detection. Several systematic reviews on the diagnostic accuracy of single-lead ECG devices for AF detection have been performed but have yielded inconclusive results.

AIMS

The aim of this study was to synthesize the available evidence on the effectiveness of single-lead ECG devices in detecting AF.

METHODS

An overview of systematic reviews was conducted. Five English databases (Cochrane Database of Systematic Reviews, PubMed, Embase, Ovid, and Web of Science) and two Chinese databases (Wanfang and CNKI) were searched from inception to July 31, 2021. Systematic reviews that examined the accuracy of tools based on single-lead ECG technology for detecting AF were included. A narrative data synthesis was performed.

RESULTS

Eight systematic reviews were finally included. Systematic reviews with meta-analysis showed that single-lead ECG-based devices had good sensitivity and specificity (both ≥90%) in detecting AF. According to subgroup analysis, the sensitivities of tools used in populations with a history of AF were all >90%. However, among handheld and thoracic placed single-lead ECG devices, large variations in diagnostic performance were observed.

LINKING EVIDENCE TO ACTION

Single-lead ECG devices can potentially be used for AF detection. Due to the heterogeneity in the study population and tools, future studies are warranted to explore the suitable circumstances in which each tool could be applied for AF screening in an effective and cost-effective manner.

Hearables: feasibility of recording cardiac rhythms from single in-ear locations

The ear is well positioned to accommodate both brain and vital signs monitoring, via so-called hearable devices. Consequently, ear-based electroencephalography has recently garnered great interest. However, despite the considerable potential of hearable based cardiac monitoring, the biophysics and characteristic cardiac rhythm of ear-based electrocardiography (ECG) are not yet well understood. To this end, we map the cardiac potential on the ear through volume conductor modelling and measurements on multiple subjects. In addition, in order to demonstrate real-world feasibility of in-ear ECG, measurements are conducted throughout a long-time simulated driving task. As a means of evaluation, the correspondence between the cardiac rhythms obtained via the ear-based and standard Lead I measurements, with respect to the shape and timing of the cardiac rhythm, is verified through three measures of similarity: the Pearson correlation, and measures of amplitude and timing deviations. A high correspondence between the cardiac rhythms obtained via the ear-based and Lead I measurements is rigorously confirmed through agreement between simulation and measurement, while the real-world feasibility was conclusively demonstrated through efficacious cardiac rhythm monitoring during prolonged driving. This work opens new avenues for seamless, hearable-based cardiac monitoring that extends beyond heart rate detection to offer cardiac rhythm examination in the community.

Smartphone AF Burden During the Blanking Period Predicts Catheter Ablation Outcomes: Insights From DECAAF II

BACKGROUND

Atrial fibrillation (AF) recurrence during the blanking period is under investigated. With the rise of smartphone-based electrocardiogram (ECG) monitoring, there's potential for better prediction and understanding of AF recurrence trends.

OBJECTIVES

In this study the authors hypothesize that AF burden derived from a single-lead Smartphone ECG during the blanking period predicts recurrence of atrial arrhythmias after ablation.

METHODS

630 patients with persistent AF undergoing ablation were included from the DECAAF II (Effect of MRI-Guided Fibrosis Ablation vs Conventional Catheter Ablation on Atrial Arrhythmia Recurrence in Patients With Persistent Atrial Fibrillation) trial. Patients recorded daily ECG strips using a smartphone device. AF burden was defined as the ratio of ECG strips with AF to the total number of strips submitted. The primary outcome was the recurrence of atrial arrhythmia.

RESULTS

Recurrence occurred in 301 patients during the 18-month follow-up period. In patients who developed recurrent arrhythmia after 90 days of follow-up, AF burden during the blanking period was significantly higher when compared with patients who remained in sinus rhythm (31.3% vs 7.5%; P < 0.001). AF burden during the blanking period was an independent predictor of arrhythmia recurrence (HR: 1.41; 95% CI: 1.36-1.47; P < 0.001). Through grid searching, an AF burden of 18% best discriminates between recurrence and no-recurrence groups, yielding a C-index of 0.748. After a follow-up period of 18 months, recurrence occurred in 33.7% of patients (147 of 436) with an AF burden <18% and in 79.4% of patients (154 of 194) with an AF burden >18% (HR: 4.57; 95% CI: 3.63-5.75; P < 0.001).

CONCLUSIONS

A high AF burden derived from a smartphone ECG during the blanking period is a strong predictor of atrial arrhythmia recurrences after ablation.

Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices

Artificial intelligence (AI) can detect left ventricular systolic dysfunction (LVSD) from electrocardiograms (ECGs). Wearable devices could allow for broad AI-based screening but frequently obtain noisy ECGs. We report a novel strategy that automates the detection of hidden cardiovascular diseases, such as LVSD, adapted for noisy single-lead ECGs obtained on wearable and portable devices. We use 385,601 ECGs for development of a standard and noise-adapted model. For the noise-adapted model, ECGs are augmented during training with random gaussian noise within four distinct frequency ranges, each emulating real-world noise sources. Both models perform comparably on standard ECGs with an AUROC of 0.90. The noise-adapted model performs significantly better on the same test set augmented with four distinct real-world noise recordings at multiple signal-to-noise ratios (SNRs), including noise isolated from a portable device ECG. The standard and noise-adapted models have an AUROC of 0.72 and 0.87, respectively, when evaluated on ECGs augmented with portable ECG device noise at an SNR of 0.5. This approach represents a novel strategy for the development of wearable-adapted tools from clinical ECG repositories.

A deep learning system for heart failure mortality prediction

Heart failure (HF) is the final stage of the various heart diseases developing. The mortality rates of prognosis HF patients are highly variable, ranging from 5% to 75%. Evaluating the all-cause mortality of HF patients is an important means to avoid death and positively affect the health of patients. But in fact, machine learning models are difficult to gain good results on missing values, high dimensions, and imbalances HF data. Therefore, a deep learning system is proposed. In this system, we propose an indicator vector to indicate whether the value is true or be padded, which fast solves the missing values and helps expand data dimensions. Then, we use a convolutional neural network with different kernel sizes to obtain the features information. And a multi-head self-attention mechanism is applied to gain whole channel information, which is essential for the system to improve performance. Besides, the focal loss function is introduced to deal with the imbalanced problem better. The experimental data of the system are from the public database MIMIC-III, containing valid data for 10311 patients. The proposed system effectively and fast predicts four death types: death within 30 days, death within 180 days, death within 365 days and death after 365 days. Our study uses Deep SHAP to interpret the deep learning model and obtains the top 15 characteristics. These characteristics further confirm the effectiveness and rationality of the system and help provide a better medical service.

Atrioventricular Synchronization for Detection of Atrial Fibrillation and Flutter in One to Twelve ECG Leads Using a Dense Neural Network Classifier

This study investigates the use of atrioventricular (AV) synchronization as an important diagnostic criterion for atrial fibrillation and flutter (AF) using one to twelve ECG leads. Heart rate, lead-specific AV conduction time, and P-/f-wave amplitude were evaluated by three representative ECG metrics (mean value, standard deviation), namely RR-interval (RRi-mean, RRi-std), PQ-interval (PQi-mean, PQI-std), and PQ-amplitude (PQa-mean, PQa-std), in 71,545 standard 12-lead ECG records from the six largest PhysioNet CinC Challenge 2021 databases. Two rhythm classes were considered (AF, non-AF), randomly assigning records into training (70%), validation (20%), and test (10%) datasets. In a grid search of 19, 55, and 83 dense neural network (DenseNet) architectures and five independent training runs, we optimized models for one-lead, six-lead (chest or limb), and twelve-lead input features. Lead-set performance and SHapley Additive exPlanations (SHAP) input feature importance were evaluated on the test set. Optimal DenseNet architectures with the number of neurons in sequential [1st, 2nd, 3rd] hidden layers were assessed for sensitivity and specificity: DenseNet [16,16,0] with primary leads (I or II) had 87.9-88.3 and 90.5-91.5%; DenseNet [32,32,32] with six limb leads had 90.7 and 94.2%; DenseNet [32,32,4] with six chest leads had 92.1 and 93.2%; and DenseNet [128,8,8] with all 12 leads had 91.8 and 95.8%, indicating sensitivity and specificity values, respectively. Mean SHAP values on the entire test set highlighted the importance of RRi-mean (100%), RR-std (84%), and atrial synchronization (40-60%) for the PQa-mean (aVR, I), PQi-std (V2, aVF, II), and PQi-mean (aVL, aVR). Our focus on finding the strongest AV synchronization predictors of AF in 12-lead ECGs would lead to a comprehensive understanding of the decision-making process in advanced neural network classifiers. DenseNet self-learned to rely on a few ECG behavioral characteristics: first, characteristics usually associated with AF conduction such as rapid heart rate, enhanced heart rate variability, and large PQ-interval deviation in V2 and inferior leads (aVF, II); second, characteristics related to a typical P-wave pattern in sinus rhythm, which is best distinguished from AF by the earliest negative P-peak deflection of the right atrium in the lead (aVR) and late positive left atrial deflection in lateral leads (I, aVL). Our results on lead-selection and feature-selection practices for AF detection should be considered for one- to twelve-lead ECG signal processing settings, particularly those measuring heart rate, AV conduction times, and P-/f-wave amplitudes. Performances are limited to the AF diagnostic potential of these three metrics. SHAP value importance can be used in combination with a human expert's ECG interpretation to change the focus from a broad observation of 12-lead ECG morphology to focusing on the few AV synchronization findings strongly predictive of AF or non-AF arrhythmias. Our results are representative of AV synchronization findings across a broad taxonomy of cardiac arrhythmias in large 12-lead ECG databases.

Cross-Channel Dynamic Weighting RPCA: A De-Noising Algorithm for Multi-Channel Arterial Pulse Signal

Pulse wave analysis (PWA) has been widely used in the medical field. A novel multi-channel sensor is employed in arterial pulse acquisition and brings richer physiological information to PWA. However, the noise of this sensor is distributed in the main frequency band of the pulse signal, which seriously interferes with subsequent analyses and is difficult to eliminate by existing methods. This study proposes a cross-channel dynamic weighting robust principal component analysis algorithm. A channel-scaled factor technique is used to manipulate the weighting factors in the nuclear norm. This factor can adaptively adjust the weights among the channels according to the signal pattern of each channel, optimizing the feature extraction in multi-channel signals. A series of performance evaluations were conducted, and four well-known de-noising algorithms were used for comparison. The results reveal that the proposed algorithm achieved one of the best de-noising performances in the time and frequency domains. The mean of h1 in the amplitude relative error (ARE) was 23.4% smaller than for the WRPCA algorithm. Moreover, our algorithm could accelerate convergence and reduce the computational time complexity by approximately 34.6%. These results demonstrate the performance and efficiency of the algorithm. Meanwhile, the idea can be extended to other multi-channel physiological signal de-noising and feature extraction fields.

Impact of Mobile Health Devices for the Detection of Atrial Fibrillation: Systematic Review

Background

Atrial fibrillation (AF) is the most common arrhythmia, and its prevalence is increasing. Early diagnosis is important to reduce the risk of stroke. Mobile health (mHealth) devices, such as single-lead electrocardiogram (ECG) devices, have been introduced to the worldwide consumer market over the past decade. Recent studies have assessed the usability of these devices for detection of AF, but it remains unclear if the use of mHealth devices leads to a higher AF detection rate.

Objective

The goal of the research was to conduct a systematic review of the diagnostic detection rate of AF by mHealth devices compared with traditional outpatient follow-up. Study participants were aged 16 years or older and had an increased risk for an arrhythmia and an indication for ECG follow-up—for instance, after catheter ablation or presentation to the emergency department with palpitations or (near) syncope. The intervention was the use of an mHealth device, defined as a novel device for the diagnosis of rhythm disturbances, either a handheld electronic device or a patch-like device worn on the patient’s chest. Control was standard (traditional) outpatient care, defined as follow-up via general practitioner or regular outpatient clinic visits with a standard 12-lead ECG or Holter monitoring. The main outcome measures were the odds ratio (OR) of AF detection rates.

Methods

Two reviewers screened the search results, extracted data, and performed a risk of bias assessment. A heterogeneity analysis was performed, forest plot made to summarize the results of the individual studies, and albatross plot made to allow the P values to be interpreted in the context of the study sample size.

Results

A total of 3384 articles were identified after a database search, and 14 studies with a 4617 study participants were selected. All studies but one showed a higher AF detection rate in the mHealth group compared with the control group (OR 1.00-35.71), with all RCTs showing statistically significant increases of AF detection (OR 1.54-19.16). Statistical heterogeneity between studies was considerable, with a Q of 34.1 and an I² of 61.9, and therefore it was decided to not pool the results into a meta-analysis.

Conclusions

Although the results of 13 of 14 studies support the effectiveness of mHealth interventions compared with standard care, study results could not be pooled due to considerable clinical and statistical heterogeneity. However, smartphone-connectable ECG devices provide patients with the ability to document a rhythm disturbance more easily than with standard care, which may increase empowerment and engagement with regard to their illness. Clinicians must beware of overdiagnosis of AF, as it is not yet clear when an mHealth-detected episode of AF must be deemed significant.

General population screening for atrial fibrillation with an automated rhythm-detection blood pressure device

BACKGROUND

Screening strategies to diagnose previously undetected atrial fibrillation (AF), especially silent AF (SAF), in at-risk populations may help reduce the number of strokes. We prospectively assessed the incidence rate of AF, including SAF, using an automated AF-detection capable sphygmomanometer in the General Practitioner (GP) setting.

METHODS

This was a population-based prospective study of unselected general population of ≥65 years without prior AF. Participating GPs were requested, in the period February 2018-April 2019, to record all AF diagnoses including those derived from the AF-detection capable sphygmomanometer and confirmed by 12‑lead ECG or ECG Holter in asymptomatic patients.

RESULTS

Overall, 14,987 patients assisted by 76 GPs accumulated 16,838 patient-years of follow up. The incidence rate of AF was 2.25% patient-years (95%CI 2.03-2.48). AF was more frequently detected in male, older, overweight, and patients with prior stroke, congestive heart failure, and chronic kidney disease. One in four patients had device-detected SAF (0.56% patient-years, 95%CI 0.46-0.69). Age, overweight, and the number of annual visits, were independent predictors of both SAF and AF. In addition, congestive heart failure, mitral valve disease were independent predictors of AF. Due to the interaction between blood pressure and age the risk of AF increased exponentially after 75 years of age in patients with higher systolic blood pressure values.

CONCLUSION

We found a higher than previously reported incidence rate of AF possibly by capturing SAF. Our simple protocol might be feasible in large-scale screening for AF and SAF in routine GP care.

The Role of Electrocardiography in Occupational Medicine, from Einthoven's Invention to the Digital Era of Wearable Devices

Clinical-instrumental investigations, such as electrocardiography (ECG), represent a corollary of a procedures that, nowadays, is called upon as part of the principles of precision medicine. However when carrying out the professional routine examinations, most tend to ignore how a “simple” instrument can offer indispensable support in clinical practice, even in occupational medicine. The advent of the digital age, made of silicon and printed circuit boards, has allowed the miniaturization of the electronic components of these electro-medical devices. Finally, the adoption of patient wearables in medicine has been rapidly expanding worldwide for a number of years. This has been driven mainly by consumers’ demand to monitor their own health. With the ongoing research and development of new features capable of assessing and transmitting real-time biometric data, the impact of wearables on cardiovascular management has become inevitable. Despite the potential offered by this technology, as evident from the scientific literature, the application of these devices in the field of health and safety in the workplace is still limited. This may also be due to the lack of targeted scientific research. While offering great potential, it is very important to consider and evaluate ethical aspects related to the use of these smart devices, such as the management of the collected data relating to the physiological parameters and the location of the worker. This technology is to be considered as being aimed at monitoring the subject’s physiological parameters, and not at the diagnosis of any pathological condition, which should always be on charge of the medical specialist We conducted a review of the evolution of the role that electrophysiology plays as part of occupational health and safety management and on its possible future use, thanks to ongoing technological innovation.

Lead-I ECG for detecting atrial fibrillation in patients with an irregular pulse using single time point testing: a systematic review and economic evaluation

BACKGROUND

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia and is associated with an increased risk of stroke and congestive heart failure. Lead-I electrocardiogram (ECG) devices are handheld instruments that can be used to detect AF at a single time point in people who present with relevant signs or symptoms.

OBJECTIVE

To assess the diagnostic test accuracy, clinical impact and cost-effectiveness of using single time point lead-I ECG devices for the detection of AF in people presenting to primary care with relevant signs or symptoms, and who have an irregular pulse compared with using manual pulse palpation (MPP) followed by a 12-lead ECG in primary or secondary care.

DATA SOURCES

MEDLINE, MEDLINE Epub Ahead of Print and MEDLINE In-Process & Other Non-Indexed Citations, EMBASE, PubMed, Cochrane Databases of Systematic Reviews, Cochrane Central Database of Controlled Trials, Database of Abstracts of Reviews of Effects and the Health Technology Assessment Database.

METHODS

The systematic review methods followed published guidance. Two reviewers screened the search results (database inception to April 2018), extracted data and assessed the quality of the included studies. Summary estimates of diagnostic accuracy were calculated using bivariate models. An economic model consisting of a decision tree and two cohort Markov models was developed to evaluate the cost-effectiveness of lead-I ECG devices.

RESULTS

No studies were identified that evaluated the use of lead-I ECG devices for patients with signs or symptoms of AF. Therefore, the diagnostic accuracy and clinical impact results presented are derived from an asymptomatic population (used as a proxy for people with signs or symptoms of AF). The summary sensitivity of lead-I ECG devices was 93.9% [95% confidence interval (CI) 86.2% to 97.4%] and summary specificity was 96.5% (95% CI 90.4% to 98.8%). One study reported limited clinical outcome data. Acceptability of lead-I ECG devices was reported in four studies, with generally positive views. The de novo economic model yielded incremental cost-effectiveness ratios (ICERs) per quality-adjusted life-year (QALY) gained. The results of the pairwise analysis show that all lead-I ECG devices generated ICERs per QALY gained below the £20,000-30,000 threshold. Kardia Mobile (AliveCor Ltd, Mountain View, CA, USA) is the most cost-effective option in a full incremental analysis.

LIMITATIONS

No published data evaluating the diagnostic accuracy, clinical impact or cost-effectiveness of lead-I ECG devices for the population of interest are available.

CONCLUSIONS

Single time point lead-I ECG devices for the detection of AF in people with signs or symptoms of AF and an irregular pulse appear to be a cost-effective use of NHS resources compared with MPP followed by a 12-lead ECG in primary or secondary care, given the assumptions used in the base-case model.

FUTURE WORK

Studies assessing how the use of lead-I ECG devices in this population affects the number of people diagnosed with AF when compared with current practice would be useful.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42018090375.

FUNDING

The National Institute for Health Research Health Technology Assessment programme.

Lead-I ECG for detecting atrial fibrillation in patients attending primary care with an irregular pulse using single-time point testing: A systematic review and economic evaluation

BACKGROUND

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia and is associated with increased risk of stroke and congestive heart failure. Lead-I electrocardiogram (ECG) devices are handheld instruments that can detect AF at a single-time point.

PURPOSE

To assess the diagnostic test accuracy, clinical impact and cost effectiveness of single-time point lead-I ECG devices compared with manual pulse palpation (MPP) followed by a 12-lead ECG for the detection of AF in symptomatic primary care patients with an irregular pulse.

METHODS

Electronic databases (MEDLINE, MEDLINE Epub Ahead of Print and MEDLINE In-Process, EMBASE, PubMed and Cochrane Databases of Systematic Reviews, Cochrane Central Database of Controlled Trials, Database of Abstracts of Reviews of Effects, Health Technology Assessment Database) were searched to March 2018. Two reviewers screened the search results, extracted data and assessed study quality. Summary estimates of diagnostic accuracy were calculated using bivariate models. Cost-effectiveness was evaluated using an economic model consisting of a decision tree and two cohort Markov models.

RESULTS

Diagnostic accuracy The diagnostic accuracy (13 publications reporting on nine studies) and clinical impact (24 publications reporting on 19 studies) results are derived from an asymptomatic population (used as a proxy for people with signs or symptoms of AF). The summary sensitivity of lead-I ECG devices was 93.9% (95% confidence interval [CI]: 86.2% to 97.4%) and summary specificity was 96.5% (95% CI: 90.4% to 98.8%). Cost effectiveness The de novo economic model yielded incremental cost effectiveness ratios (ICERs) per quality adjusted life year (QALY) gained. The results of the pairwise analysis show that all lead-I ECG devices generate ICERs per QALY gained below the £20,000-£30,000 threshold. Kardia Mobile is the most cost effective option in a full incremental analysis. Lead-I ECG tests may identify more AF cases than the standard diagnostic pathway. This comes at a higher cost but with greater patient benefit in terms of mortality and quality of life.

LIMITATIONS

No published data evaluating the diagnostic accuracy, clinical impact or cost effectiveness of lead-I ECG devices for the target population are available.

CONCLUSIONS

The use of single-time point lead-I ECG devices in primary care for the detection of AF in people with signs or symptoms of AF and an irregular pulse appears to be a cost effective use of NHS resources compared with MPP followed by a 12-lead ECG, given the assumptions used in the base case model.

REGISTRATION

The protocol for this review is registered on PROSPERO as CRD42018090375.