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Electrocardiogram prediction of atrial fibrillation risk after stroke: A protocol for systematic review and
1Medical Imaging Department II, Shaanxi Kangfu Hospital, Xi'an, Shaanxi Province, China.
Insights
Electrocardiogram (ECG) abnormalities can predict atrial fibrillation (AF) risk after stroke. Specific ECG patterns, like advanced interatrial block (alAB), significantly increase the likelihood of developing AF.
Area of Science:
- Cardiology
- Electrophysiology
- Stroke Medicine
Background:
- Atrial fibrillation (AF) is a prevalent clinical arrhythmia.
- Predicting post-stroke AF risk is crucial for patient management.
- Electrocardiographic (ECG) changes during sinus rhythm may offer predictive insights.
Purpose of the Study:
- To evaluate the association between specific ECG patterns in sinus rhythm and the risk of developing AF post-stroke.
- To identify ECG markers that can serve as screening tools for high-risk individuals.
Main Methods:
- A systematic literature search was conducted on MEDLINE (PubMed) and EMBASE up to August 2023.
- Data from relevant studies, including meta-analyses and systematic reviews, were screened and extracted.
- A random-effects meta-analysis was performed to synthesize the data on AF detection rates.
Main Results:
- 32 studies with 330,284 participants were analyzed.
- Individuals with abnormal ECGs had a significantly higher risk of developing AF (Risk Ratio = 2.45).
- Advanced interatrial block (alAB) showed the highest predictability (Risk Ratio = 4.12).
Conclusions:
- Specific ECG patterns are significantly correlated with AF occurrence.
- ECG findings, particularly alAB, can identify individuals at high risk for AF.
- These ECG markers can guide further investigations and treatment decisions, including anticoagulation therapy.
Background:
Atrial fibrillation (AF) is one of the most common clinical arrhythmias. This study aims to predict the risk of post-stroke AF through electrocardiographic changes in sinus rhythm.
Methods:
We searched the MEDLINE (PubMed) and EMBASE databases to identify relevant research articles published until August 2023. Prioritized items from systematic reviews and meta-analyses were screened, and data related to AF detection rate were extracted. A meta-analysis using a random-effects model was conducted for data synthesis and analysis.
Results:
A total of 32 studies involving electrocardiograms (ECG) were included, with a total analysis population of 330,284 individuals. Among them, 16,662 individuals (ECG abnormal group) developed AF, while 313,622 individuals (ECG normal group) did not. ECG patterns included terminal P-wave terminal force V1, interatrial block (IAB), advanced interatrial block, abnormal P-wave axis, pulse rate prolongation, and atrial premature complexes. Overall, 15,762 patients experienced AF during the study period (4.77%). In the ECG abnormal group, the proportion was 14.21% (2367/16,662), while in the control group (ECG normal group), the proportion was 4.27% (13,395/313,622). The pooled risk ratio for developing AF was 2.45 (95% confidence interval [CI]: 2.02-2.98, P < .001), with heterogeneity (I2) of 95%. The risk ratio values of alAB, P-wave terminal force V1, interatrial block, abnormal P-wave axis, pulse rate prolongation and atrial premature complexes were 4.12 (95% CI, 2.99-5.66), 1.47 (95% CI, 1.19-1.82), 2.54 (95% CI, 1.83-3.52), 1.70 (95% CI, 0.98-2.97), 2.65 (95% CI, 1.88-3.72), 3.79 (95% CI, 2.12-6.76), respectively.
Conclusion:
There is a significant correlation between ECG patterns and the occurrence of AF. The alAB exhibited the highest level of predictability for the occurrence of AF. These indicators support their use as screening tools to identify high-risk individuals who may benefit from further examinations or empirical anticoagulation therapy following stroke.
