Background and Purpose-Occult paroxysmal atrial fibrillation (AF) is found in a substantial minority of patients with cryptogenic stroke. Identifying reliable predictors of paroxysmal AF after cryptogenic stroke would allow clinicians to more effectively use outpatient cardiac monitoring and ultimately reduce secondary stroke burden. Methods-We analyzed a retrospective cohort of consecutive patients who underwent 28-day mobile cardiac outpatient telemetry after cryptogenic stroke or transient ischemic stroke. Univariate and multivariable analyses were performed to identify clinical, echocardiographic, and radiographic features associated with the detection of paroxysmal AF. Results-Of 227 patients with cryptogenic stroke (179) or transient ischemic stroke (48), 14% (95% confidence interval, 9%-18%) had AF detected on mobile cardiac outpatient telemetry, 58% of which was ≥30 seconds in duration. Age >60 years (odds ratio, 3.7; 95% confidence interval, 1.3-11) and prior cortical or cerebellar infarction seen on neuroimaging (odds ratio, 3.0; 95% confidence interval, 1.2-7.6) were independent predictors of AF. AF was detected in 33% of patients with both factors, but only 4% of patients with neither. No other clinical features (including demographics, CHA 2 DS 2 -VASc [combined stroke risk score: congestive heart failure, hypertension, age, diabetes, prior stroke/transient ischemic attack, vascular disease, sex] score, or stroke symptoms), echocardiographic findings (including left atrial size or ejection fraction), or radiographic characteristics of the acute infarction (including location, topology, or number) were associated with AF detection. Conclusions-Mobile cardiac outpatient telemetry detects AF in a substantial proportion of cryptogenic stroke patients. Age >60 years and radiographic evidence of prior cortical or cerebellar infarction are robust indicators of occult AF. Patients with neither had a low prevalence of AF.
Background and Purpose: Prolonged cardiac monitoring may identify paroxysmal atrial fibrillation (AF) in patients with cryptogenic stroke. We aimed to identify clinical, echocardiographic, and neuroimaging features which may increase the efficiency of detecting AF on cardiac monitoring. Methods: We studied a retrospective cohort of 227 subjects with cryptogenic ischemic stroke referred for 28 day mobile cardiac outpatient telemetry (MCOT). Patients with large artery disease or high risk sources of cardioembolism were excluded. We reviewed medical records, brain images, and echocardiograms, blinded to MCOT results. Acute and/or chronic infarctions were characterized by size, location, and as cortical, subcortical, or both; wedge-shaped; lacunar; borderzone; and/or multiple territories. Cardiac features included left atrial (LA) size, ejection fraction, aortic arch atheroma, and PFO. Variables were tested in univariate analyses and further incorporated in a multivariate logistic regression model to determine independent predictors of detecting AF. Results: The cohort age was 62.9±2.9 years, 42% were men, and 53% were white. Median CHADS was 3 and CHADS2Vasc was 5. Infarcts were >1.5 cm in 62% of subjects, predominantly cortical in 47%, subcortical in 39%. Only 9% were single, deep, and <1.5 cm. LA size was 3.6±0.7 cm and ejection fraction was 61±9%. MCOT detected AF in 30 (13%) patients. In multivariate analysis, AF was only associated with age>60 (OR 3.6 [1.2-10.4], p=0.02) and prior (chronic) cortical or cerebellar infarction (OR 3.3 [1.3-8.6], p=0.013) (C-statistic 0.72). There was no association with any other clinical, echocardiographic, or radiographic parameter. AF was detected in 32% of patients with age >60 and the presence of prior cortical or cerebellar infarction, compared to 4% with neither of these factors. Conclusion: Atrial fibrillation is detected on MCOT in a substantial minority of cryptogenic stroke patients. Age>60 and the presence of prior cortical or cerebellar strokes are predictive of detecting AF in these patients. Other brain and cardiac characteristics were not found to be helpful. These data may aid in the selection of patients for prolonged arrhythmia monitoring.
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