Prolonged Cardiac Monitoring for Atrial Fibrillation Detection After Stroke: In Search of the Elusive Sweet Spot

Embolic Stroke of Undetermined Source: New Data and New Controversies on Cardiac Monitoring and Anticoagulation

Embolic strokes of undetermined source (ESUS) represent 9%-25% of all ischemic strokes. Based on the suspicion that a large proportion of cardioembolic sources remain undetected among embolic stroke of undetermined source patients, it has been hypothesized that a universal approach of anticoagulation would be better than aspirin for preventing recurrent strokes. However, 4 randomized controlled trials (RCTs), with different degrees of patient selection, failed to confirm this hypothesis. In parallel, several RCTs consistently demonstrated that prolonged cardiac monitoring increased atrial fibrillation detection and anticoagulation initiation compared with usual care in patients with ESUS, and later in individuals with ischemic stroke of known cause (e.g., large or small vessel disease). However, none of these trials or subsequent meta-analyses of all available RCTs have shown a reduction in stroke recurrence associated with the use of prolonged cardiac monitoring. In this article, we review the clinical and research implications of recent RCTs of antithrombotic therapy in patients with ESUS and in high-risk populations with and without stroke, with device-detected asymptomatic atrial fibrillation.

Prediction of underlying atrial fibrillation in patients with a cryptogenic stroke: results from the NOR-FIB Study

BACKGROUND

Atrial fibrillation (AF) detection and treatment are key elements to reduce recurrence risk in cryptogenic stroke (CS) with underlying arrhythmia. The purpose of the present study was to assess the predictors of AF in CS and the utility of existing AF-predicting scores in The Nordic Atrial Fibrillation and Stroke (NOR-FIB) Study.

METHOD

The NOR-FIB study was an international prospective observational multicenter study designed to detect and quantify AF in CS and cryptogenic transient ischaemic attack (TIA) patients monitored by the insertable cardiac monitor (ICM), and to identify AF-predicting biomarkers. The utility of the following AF-predicting scores was tested: AS5F, Brown ESUS-AF, CHA₂DS₂-VASc, CHASE-LESS, HATCH, HAVOC, STAF and SURF.

RESULTS

In univariate analyses increasing age, hypertension, left ventricle hypertrophy, dyslipidaemia, antiarrhythmic drugs usage, valvular heart disease, and neuroimaging findings of stroke due to intracranial vessel occlusions and previous ischemic lesions were associated with a higher likelihood of detected AF. In multivariate analysis, age was the only independent predictor of AF. All the AF-predicting scores showed significantly higher score levels for AF than non-AF patients. The STAF and the SURF scores provided the highest sensitivity and negative predictive values, while the AS5F and SURF reached an area under the receiver operating curve (AUC) > 0.7.

CONCLUSION

Clinical risk scores may guide a personalized evaluation approach in CS patients. Increasing awareness of the usage of available AF-predicting scores may optimize the arrhythmia detection pathway in stroke units.

Validation of Risk Scores for Predicting Atrial Fibrillation Detected After Stroke Based on an Electronic Medical Record Algorithm: A Registry-Claims-Electronic Medical Record Linked Data Study

Background

Poststroke atrial fibrillation (AF) screening aids decisions regarding the optimal secondary prevention strategies in patients with acute ischemic stroke (AIS). We used an electronic medical record (EMR) algorithm to identify AF in a cohort of AIS patients, which were used to validate eight risk scores for predicting AF detected after stroke (AFDAS).

Methods

We used linked data between a hospital stroke registry and a deidentified database including EMRs and administrative claims data. EMR algorithms were constructed to identify AF using diagnostic and medication codes as well as free clinical text. Based on the optimal EMR algorithm, the incidence rate of AFDAS was estimated. The predictive performance of 8 risk scores including AS5F, C₂HEST, CHADS₂, CHA₂DS₂-VASc, CHASE-LESS, HATCH, HAVOC, and Re-CHARGE-AF scores, were compared using the C-index, net reclassification improvement, integrated discrimination improvement, calibration curve, and decision curve analysis.

Results

The algorithm that defines AF as any positive mention of AF-related keywords in electrocardiography or echocardiography reports, or presence of diagnostic codes of AF was used to identify AF. Among the 5,412 AIS patients without known AF at stroke admission, the incidence rate of AFDAS was 84.5 per 1,000 person-year. The CHASE-LESS and AS5F scores were well calibrated and showed comparable C-indices (0.741 versus 0.730, p = 0.223), which were significantly higher than the other risk scores.

Conclusion

The CHASE-LESS and AS5F scores demonstrated adequate discrimination and calibration for predicting AFDAS. Both simple risk scores may help select patients for intensive AF monitoring.