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Predicting Atrial Fibrillation after Ischemic Stroke: Clinical, Genetics, and Electrocardiogram Modelling.

Mervyn Qi Wei Poh¹, Carol Huilian Tham¹, Jeremiah David Ming Siang Chee¹

¹Department of Neurology, National Neuroscience Institute, Tan Tock Seng Campus, Singapore, Singapore.

Cerebrovascular Diseases Extra

|December 15, 2022

Summary

Predicting atrial fibrillation (AF) after stroke is crucial. A model combining clinical factors and electrocardiogram (ECG) measures effectively predicts AF in post-stroke patients, while genetic markers showed limited utility.

Keywords:

P-wave terminal force Prediction model QT corrected interval Single nucleotide polymorphism

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Area of Science:

Cardiology
Neurology
Genetics

Background:

Atrial fibrillation (AF) detection post-ischaemic stroke is challenging due to its paroxysmal nature.
Early AF detection is vital for stroke management and secondary prevention.

Purpose of the Study:

To evaluate a combined model of clinical, electrocardiographic, and genetic variables for predicting AF in patients following an ischaemic stroke.
To identify key predictors of AF in this high-risk population.

Main Methods:

A cohort study involving 709 patients diagnosed with acute ischaemic stroke or transient ischaemic attacks.
Evaluation of clinical data, electrocardiographic variables (p-wave terminal force, corrected QT interval), and genetic markers (4q25 SNP).
Multiple logistic regression and receiver operating characteristic analyses were used to assess predictive capabilities.

Main Results:

Out of 709 patients, 8.5% developed AF. Key predictors included age, hypertension, and valvular heart disease.
A model incorporating clinical and electrocardiographic variables achieved an area under the receiver operating curve of 0.82.
Single nucleotide polymorphism (SNP) analysis did not enhance AF prediction.

Conclusions:

A predictive model combining clinical and electrocardiographic variables offers robust AF prediction in post-stroke patients.
The utility of specific genetic markers (SNPs) in predicting AF in this cohort was limited.