Use of Atrial Strain to Predict Atrial Fibrillation After Cerebral Ischemia.

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.