Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
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.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927656 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0226671 | PLOS |
Publication Analysis
Top Keywords
Similar Publications
Are single-lead consumer ECG devices enough for accurate QTc monitoring? A calibrated uncertainty AI approach.
J Electrocardiol
August 2025
PulseAI Ltd., Belfast, UK.
Background: While single‑lead ECGs offer accessibility, their performance and reliability for QTc assessment remains uncertain. Current State-of-the-art AI systems, though promising, often lack transparency, raising concerns about clinical trustworthiness.
Methods: We developed an uncertainty-aware AI model to measure RR/QT intervals from single‑lead ECGs.
Beyond the type 1 pattern: comprehensive risk stratification in Brugada syndrome.
J Interv Card Electrophysiol
August 2025
Department of Natural Sciences, Middlesex University, The Burroughs, London, NW4 4BT, UK.
Brugada Syndrome (BrS) is an inherited cardiac ion channelopathy associated with an elevated risk of sudden cardiac death, particularly due to ventricular arrhythmias in structurally normal hearts. Affecting approximately 1 in 2,000 individuals, BrS is most prevalent among middle-aged males of Asian descent. Although diagnosis is based on the presence of a Type 1 electrocardiographic (ECG) pattern, either spontaneous or induced, accurately stratifying risk in asymptomatic and borderline patients remains a major clinical challenge.
View Article and Find Full Text PDFDevelopment and multinational validation of an ensemble deep learning algorithm for detecting and predicting structural heart disease using noisy single-lead electrocardiograms.
Eur Heart J Digit Health
July 2025
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06510, USA.
Aims: Artificial intelligence (AI)-enhanced 12-lead electrocardiogram (ECG) can detect a range of structural heart diseases (SHDs); however, it has a limited role in community-based screening. We developed and externally validated a noise-resilient single-lead AI-ECG algorithm that can detect SHDs and predict the risk of their development using wearable/portable devices.
Methods And Results: Using 266 740 ECGs from 99 205 patients with paired echocardiographic data at Yale New Haven Hospital, we developed AI Deep learning for Adapting Portable Technology in HEART disease detection (ADAPT-HEART), a noise-resilient, deep learning algorithm, to detect SHDs using lead I ECG.
Convolutional Neural Network-Transformer Model to Predict and Classify Early Arrhythmia Using Electrocardiogram Signal.
J Vis Exp
July 2025
Faculty of Education and Liberal Arts, Persiaran perdana BBN Putra Nolai, INTI international university.
As a major cause of death worldwide, cardiovascular diseases-especially arrhythmias-require the creation of precise and automated technologies for early diagnosis and detection. To identify arrhythmias from electrocardiogram (ECG) signals, this paper introduces a deep learning-based classification model that focuses on five main heartbeat types: Normal (N), Left Bundle Branch Block (L), Right Bundle Branch Block (R), Atrial Premature Beat (A), and Premature Ventricular Contraction (V). We leverage Lead I signals from several sources, such as the INCART 12-lead, Sudden Cardiac Death Holter, Supraventricular, and MIT-BIH Arrhythmia databases, yielding more than 3.
View Article and Find Full Text PDFVentricular Arrhythmias Originating From Different Portions of the Anterior Interventricular Vein: Electrocardiographic Characteristics and Ablation Strategy.
J Cardiovasc Electrophysiol
September 2025
Department of Cardiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China.
Introduction: The anterior interventricular vein (AIV) is an important and recognized source of ventricular arrhythmias (VAs) within the coronary venous system. However, no studies have specifically investigated the electrocardiographic (ECG) characteristics and ablation strategies for VAs originating from the AIV.
Objective: This study aimed to investigate the ECG characteristics and optimal ablation strategies for VAs originating from the AIV.