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What is next for screening for undiagnosed atrial fibrillation? Artificial intelligence may hold the key
Ramesh Nadarajah1,2,3, Jianhua Wu2,4, Alejandro F Frangi1,2,5,6
1Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, 6 Clarendon Way, Leeds LS2 9DA, UK.
Insights
Systematic screening for atrial fibrillation (AF) in older populations can increase detection and oral anticoagulation prescriptions. However, landmark trials show conflicting clinical outcomes, suggesting targeted screening for high-risk individuals may be more effective.
Area of Science:
- Cardiology
- Public Health
- Artificial Intelligence
Background:
- Atrial fibrillation (AF) is a common condition often undiagnosed, increasing the risk of ischemic stroke.
- Current European guidelines do not advocate for systematic AF screening in the general population.
- Previous studies suggest rhythm monitoring increases AF detection and oral anticoagulation use in older adults.
Purpose of the Study:
- To discuss the conflicting results of the STROKESTOP and LOOP trials regarding AF screening and clinical outcomes.
- To explore optimizing AF screening efficiency by targeting high-risk individuals.
- To review the role of artificial intelligence in identifying individuals at elevated risk for AF.
Main Methods:
- Discussion of findings from landmark trials STROKESTOP and LOOP.
- Review of evidence on serial or continuous rhythm monitoring in older populations.
- Examination of artificial intelligence prediction models using electronic health records for AF risk stratification.
Main Results:
- Landmark trials STROKESTOP and LOOP provide conflicting evidence on the clinical benefits of AF screening.
- Rhythm monitoring in older populations has shown increased AF detection and oral anticoagulation prescription.
- AI-driven prediction models demonstrate strong performance in identifying individuals at high risk for AF.
Conclusions:
- Targeted screening for atrial fibrillation in high-risk individuals may optimize the benefits and efficiency of detection.
- Artificial intelligence in electronic health records shows promise for identifying individuals who would benefit most from AF screening.
- Future research should focus on aligning screening strategies with evidence-based risk stratification to improve stroke prevention.
Abstract:
Atrial fibrillation (AF) is increasingly common, though often undiagnosed, leaving many people untreated and at elevated risk of ischaemic stroke. Current European guidelines do not recommend systematic screening for AF, even though a number of studies have shown that periods of serial or continuous rhythm monitoring in older people in the general population increase detection of AF and the prescription of oral anticoagulation. This article discusses the conflicting results of two contemporary landmark trials, STROKESTOP and the LOOP, which provided the first evidence on whether screening for AF confers a benefit for people in terms of clinical outcomes. The benefit and efficiency of systematic screening for AF in the general population could be optimized by targeting screening to only those at higher risk of developing AF. For this purpose, evidence is emerging that prediction models developed using artificial intelligence in routinely collected electronic health records can provide strong discriminative performance for AF and increase detection rates when combined with rhythm monitoring in a clinical study. We consider future directions for investigation in this field and how this could be best aligned to the current evidence base to target screening in people at elevated risk of stroke.
