Dataset Information

Predicting risk of AF in ischaemic stroke using sinus rhythm ECG abnormalities: A meta-analysis.

ABSTRACT:

Objective

To identify ECG changes in sinus rhythm that may be used to predict subsequent development of new AF.

Method

We identified prospective and retrospective cohort or case control studies evaluating ECG patterns from a 12-lead ECG in sinus rhythm taken in hospital or community predicting subsequent development of new AF. For each identified ECG predictor, we then identify absolute event rates and pooled risk ratios (RR) using an aggregate level random effects meta-analysis.

Results

We identified 609,496 patients from 22 studies. ECG patterns included P wave terminal force V1 (PTFV1), interatrial block (IAB) and advanced interatrial block (aIAB), abnormal P wave axis (aPWA), PR prolongation and atrial premature complexes (APCs). Pooled risk ratios reached significance for each of these; PTFV1 RR 1.48 (95% CI 1.04-2.10), IAB 2.54 (95% CI 1.64-3.93), aIAB 4.05 (95% CI 2.64-6.22), aPWA 1.89 (95% CI 1.25-2.85), PR prolongation 2.22 (95% CI 1.27-3.87) and APCs 3.71 (95% CI 2.23-6.16). Diabetes reduced the predictive value of PR prolongation.

Conclusion

APC and aIAB were most predictive of AF, while IAB, PR prolongation, PTFV1 and aPWA were also significantly associated with development of AF. These support their use in a screening tool to identify at risk cohorts who may benefit from further investigation, or following stroke, with empirical anticoagulation.

SUBMITTER: Berry-Noronha A

PROVIDER: S-EPMC10472966 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predicting risk of AF in ischaemic stroke using sinus rhythm ECG abnormalities: A meta-analysis.

Berry-Noronha Alexander A Bonavia Luke L Wilson Duncan D Eranti Antti A Rasmussen Maria Uggen MU Sajadieh Ahmad A Kreimer Fabienne F Gotzmann Michael M Sahathevan Ramesh R

European stroke journal 20230502 3

<h4>Objective</h4>To identify ECG changes in sinus rhythm that may be used to predict subsequent development of new AF.<h4>Method</h4>We identified prospective and retrospective cohort or case control studies evaluating ECG patterns from a 12-lead ECG in sinus rhythm taken in hospital or community predicting subsequent development of new AF. For each identified ECG predictor, we then identify absolute event rates and pooled risk ratios (RR) using an aggregate level random effects meta-analysis.< ...[more]

PMID: 37641552

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Project description:IntroductionAtrial fibrillation (AF) is a major risk factor for ischaemic stroke and transient ischaemic attack (TIA), and AF detection can be challenged by asymptomatic and paroxysmal presentation. Long-term ECG monitoring after ischaemic stroke or TIA is recommended by all major societies in cardiology and cerebrovascular medicine as a secondary prophylactic measure. However, data on stroke reduction are lacking, and the recommendations show significant diversity.Methods and analysisAF SPICE is a multicentre, national, investigator-initiated, randomised, parallel-group, register-based trial comparing extended ECG monitoring versus standard ECG monitoring in patients admitted with ischaemic stroke or TIA, with a composite endpoint of stroke, all-cause-mortality and intracerebral bleeding. Patients aged ≥70 years without previous AF will be randomised 1:1 to control (standard ECG monitoring) or intervention (extended ECG monitoring). In the control arm, patients will undergo 48±24 hours (ie, a range of 24-72 hours) of continuous ECG monitoring according to national recommendations. In the intervention arm, patients will undergo 14+14 days of continuous ECG monitoring 3 months apart using an ECG patch device, which will provide an easy-accessed, well-tolerated 14-day continuous ECG recording. All ECG patch recordings will be read in a core facility. In cases of AF detection, oral anticoagulation will be recommended if not contraindicated. A pilot phase has been concluded in 2022, which will transcend into the main trial during 2023-2026, including approximately 30 stroke units. The sample size was calculated to be 3262 patients. The primary outcome will be collected from register data during a 36-month follow-up.Ethics and disseminationEthical approval has been provided by the Swedish Ethical Review Authority, reference 2021-02770. The trial will be conducted according to the ethical principles of the Declaration of Helsinki and national regulatory standards. Positive results from the study have the potential for rapid dissemination in clinical practice.Trial registration numberNCT05134454.

Project description:ObjectivesThe role of cardiac arrhythmia in ischaemic stroke is widely studied, but the size of the stroke risk in patients with sinus node dysfunction (SND) with and without atrial fibrillation (AF) is unclear. This systematic review and meta-analysis aimed to compare the risk of stroke and its associated factors in patients with SND with and without AF.DesignA systematic review and meta-analysis was conducted based on the Grading of Recommendations, Assessment, Development and Evaluation approach.Data sourcesPubMed, EMBASE and Cochrane Database were searched until December 2022.Eligibility criteria for selecting studiesStudies that investigate stroke in patients with SND diagnosed with or without AF/atrial flutter.Data extraction and synthesisTwo independent authors screened studies for inclusion and extracted data. Literature quality assessment was performed using the Newcastle-Ottawa Scale and the Cochrane Collaboration Tool. The overall risk of stroke was estimated using the random-effects model. The generic inverse variance method was used to calculate the pooled estimates of stroke-associated factors. We performed a sensitivity analysis using a fixed-effects model.ResultsOf the 929 records retrieved, 6 papers (106 163 patients) met the inclusion criteria. The average yearly stroke incidence in patients with SND was 1.542% (95% CI: 1.334% to 1.749%). The stroke incidence was similar between the isolated SND (1.587%; 95% CI: 1.510% to 1.664%) and non-isolated (SND+AF) (1.660%; 95% CI: 0.705% to 2.615%) groups. AF (HR, 95% CI: 1.53 (1.01 to 2.33)), stroke/transient ischaemia attack/other thrombotic events (HR, 95% CI: 2.54 (1.14 to 5.69)), hypertension (HR, 95% CI: 1.51 (1.11 to 2.07)) and heart failure (HR, 95% CI: 1.41 (1.01 to 1.97)) were associated with stroke in the SND population.ConclusionOur findings suggest that patients with SND carry a similar risk of stroke to those with combined SND and AF. Future studies are needed to investigate whether interventions targeting stroke prevention, such as anticoagulation therapy, can help to prevent stroke in patients with SND.Prospero registration numberCRD42023408436.

Project description:BackgroundUndetected atrial fibrillation (AF) poses a significant risk of stroke and cardiovascular mortality. However, diagnosing AF in real-time can be challenging as the arrhythmia is often not captured instantly. To address this issue, a deep-learning model was developed to diagnose AF even during periods of arrhythmia-free windows.MethodsThe proposed method introduces a novel approach that integrates clinical data and electrocardiograms (ECGs) using a colorization technique. This technique recolors ECG images based on patients' demographic information while preserving their original characteristics and incorporating color correlations from statistical data features. Our primary objective is to enhance atrial fibrillation (AF) detection by fusing ECG images with demographic data for colorization. To ensure the reliability of our dataset for training, validation, and testing, we rigorously maintained separation to prevent cross-contamination among these sets. We designed a Dual-input Mixed Neural Network (DMNN) that effectively handles different types of inputs, including demographic and image data, leveraging their mixed characteristics to optimize prediction performance. Unlike previous approaches, this method introduces demographic data through color transformation within ECG images, enriching the diversity of features for improved learning outcomes.ResultsThe proposed approach yielded promising results on the independent test set, achieving an impressive AUC of 83.4%. This outperformed the AUC of 75.8% obtained when using only the original signal values as input for the CNN. The evaluation of performance improvement revealed significant enhancements, including a 7.6% increase in AUC, an 11.3% boost in accuracy, a 9.4% improvement in sensitivity, an 11.6% enhancement in specificity, and a substantial 25.1% increase in the F1 score. Notably, AI diagnosis of AF was associated with future cardiovascular mortality. For clinical application, over a median follow-up of 71.6 ± 29.1 months, high-risk AI-predicted AF patients exhibited significantly higher cardiovascular mortality (AF vs. non-AF; 47 [18.7%] vs. 34 [4.8%]) and all-cause mortality (176 [52.9%] vs. 216 [26.3%]) compared to non-AF patients. In the low-risk group, AI-predicted AF patients showed slightly elevated cardiovascular (7 [0.7%] vs. 1 [0.3%]) and all-cause mortality (103 [9.0%] vs. 26 [6.4%]) than AI-predicted non-AF patients during six-year follow-up. These findings underscore the potential clinical utility of the AI model in predicting AF-related outcomes.ConclusionsThis study introduces an ECG colorization approach to enhance atrial fibrillation (AF) detection using deep learning and demographic data, improving performance compared to ECG-only methods. This method is effective in identifying high-risk and low-risk populations, providing valuable features for future AF research and clinical applications, as well as benefiting ECG-based classification studies.

Project description:AimsStroke is often a devastating event among patients with heart failure with reduced ejection (HFrEF). In COMMANDER HF, rivaroxaban 2.5 mg b.i.d. did not reduce the composite of first occurrence of death, stroke, or myocardial infarction compared with placebo in patients with HFrEF, coronary artery disease (CAD), and sinus rhythm. We now examine the incidence, timing, type, severity, and predictors of stroke or a transient ischaemic attack (TIA), and seek to establish the net clinical benefit of treatment with low-dose rivaroxaban.Methods and resultsIn this double-blind, randomized trial, 5022 patients who had HFrEF(≤40%), elevated natriuretic peptides, CAD, and who were in sinus rhythm were treated with rivaroxaban 2.5 mg b.i.d. or placebo in addition to antiplatelet therapy, after an episode of worsening HF. The primary neurological outcome for this post hoc analysis was time to first event of any stroke or TIA. Over a median follow-up of 20.5 (25th-75th percentiles 20.0-20.9) months, 150 all-cause stroke (127) or TIA (23) events occurred (ischaemic stroke in 82% and haemorrhagic stroke in 11% of stroke events). Overall, 47.5% of first-time strokes were either disabling (16.5%) or fatal (31%). Prior stroke, low body mass index, geographic region, and the CHA2DS2-VASc score were predictors of stroke/TIA. Rivaroxaban significantly reduced the primary neurological endpoint of all-cause stroke or TIA compared with placebo by 32% (1.29 events vs. 1.90 events per 100 patient-years), adjusted for the time from index HF event to randomization and stratified by geographic region (adjusted hazard ratio 0.68, 95% confidence interval 0.49-0.94), with a number needed to treat of 164 patients per year to prevent one stroke/TIA event. The principal safety endpoint of fatal bleeding or bleeding into a critical space, occurred at a similar rate on rivaroxaban and placebo (0.44 events vs. 0.55 events per 100 patient-years).ConclusionsPatients with HFrEF and CAD are at risk for stroke or TIA in the period following an episode of worsening heart failure in the absence of atrial fibrillation. Most strokes are of ischaemic origin and nearly half are either disabling or fatal. Rivaroxaban at a dose of 2.5 mg b.i.d. reduced rates of stroke or TIA compared with placebo in this population.Trial registrationCOMMANDER HF (A Study to Assess the Effectiveness and Safety of Rivaroxaban in Reducing the Risk of Death, Myocardial Infarction, or Stroke in Participants with Heart Failure and Coronary Artery Disease Following an Episode of Decompensated Heart Failure); ClinicalTrials.gov NCT01877915.

Dataset Information

Predicting risk of AF in ischaemic stroke using sinus rhythm ECG abnormalities: A meta-analysis.

Objective

Method

Results

Conclusion

Publications

Predicting risk of AF in ischaemic stroke using sinus rhythm ECG abnormalities: A meta-analysis.

Similar Datasets

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