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Updated: Feb 18, 2026
Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
Published on: July 20, 2022
Use of Atrial Strain to Predict Atrial Fibrillation After Cerebral Ischemia
Faraz Pathan1, Eswar Sivaraj2, Kazuaki Negishi1
1Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia; Department of Cardiology, Royal Hobart Hospital, Hobart, Australia.
Insights
Atrial strain, measured by echocardiogram, can predict atrial fibrillation (AF) in patients with cryptogenic stroke. Lower atrial strain values indicate a higher risk of developing AF, adding to existing risk models.
Area of Science:
- Cardiology
- Medical Imaging
- Biomarkers
Background:
- Atrial fibrillation (AF) is a significant cause of cryptogenic cerebrovascular accidents (CVAs).
- Identifying individuals at risk for AF post-stroke is crucial for prevention strategies.
Purpose of the Study:
- To investigate atrial strain as an imaging biomarker for predicting incident atrial fibrillation (AF) in patients with cryptogenic CVA.
- To assess the incremental predictive value of atrial strain over established clinical risk scores.
Main Methods:
- Observational study of patients with cryptogenic CVA undergoing transthoracic echocardiogram (TTE).
- Speckle tracking echocardiography used to quantify reservoir (ƐR), contractile (ƐCt), and conduit (ƐCd) atrial strain.
- Comparison of clinical and echocardiographic characteristics between patients who developed AF and those who did not over 5 years.
Main Results:
- 11% of patients developed AF within 2 years; those who did were older, had higher risk scores, and lower atrial strain.
- Atrial strain parameters (ƐR, ƐCt, ƐCd) showed significant discriminatory ability for AF prediction (AUC 0.76–0.85).
- Reservoir and contractile atrial strain independently predicted AF, improving upon clinical risk models.
Conclusions:
- Left atrial strain provides independent and incremental predictive value for AF in cryptogenic CVA patients.
- Specific cutpoints for atrial strain (e.g., ƐR ≤21.4%) combined with clinical risk identify high-risk individuals.
- Further research is needed to validate these findings for clinical application in AF monitoring and anticoagulation.
Objectives:
This study sought to identify whether atrial strain could be used as an imaging biomarker to predict atrial fibrillation (AF).
Background:
AF is found in up to 30% of cryptogenic cerebrovascular accidents (CVAs), which themselves account for 30% to 40% of ischemic CVA.
Methods:
This observational study evaluated all patients who had an echocardiogram (transthoracic echocardiogram [TTE]) following presentation with cryptogenic CVA from 2010 to 2014. The TTEs were evaluated for reservoir strain (ƐR), contractile strain (ƐCt), and conduit atrial strain (ƐCd) using speckle tracking. Baseline clinical and TTE characteristics of patients who developed AF over 5 years of follow-up and those who did not were compared. The independent and incremental predictive value of atrial strain over established clinical models was assessed. Discriminatory cutpoints were defined using a Classification and Regression Tree (CART) analysis to identify patients at risk of developing AF.
Results:
Of 538 patients, 61 (11%) developed AF, and this occurred within 2 years in 85% of patients. Patients who developed AF were older, had higher clinical risk scores, had higher LA volume, and had lower atrial strain than did those who did not develop AF. The area under the receiver-operating characteristic curve was 0.85 for ƐR, 0.83 for ƐCt, and 0.76 for ƐCd (all p < 0.001). The nested Cox regression model showed that ƐR (p = 0.03) and ƐCt (p < 0.001) demonstrated independent and incremental predictive value over the clinical risk. CART analysis identified ƐR ≤21.4%, ƐCd >10.4%, and CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology Atrial Fibrillation) score >7.8% as discriminatory for AF, with a 13-fold greater hazard of AF (p < 0.001) in patients with increased clinical risk and reduced ƐR. However, validation is needed for these strain cutoffs for detection of AF.
Conclusions:
Left atrial strain adds independent and incremental predictive value to current risk-prediction models for AF following cryptogenic CVA. Further studies should examine the implications of these findings for AF monitoring or empiric anticoagulation.
