Predictors of Atrial Fibrillation Development in Patients With Embolic Stroke of Undetermined Source: An Analysis of the RE-SPECT ESUS Trial

Abstract

Background: A proportion of patients with embolic stroke of undetermined source (ESUS) have silent atrial fibrillation (AF) or develop AF after the initial evaluation. Better understanding of risk for development of AF is critical to implement optimal monitoring strategies with the goal of preventing recurrent stroke due to underlying AF. The RE-SPECT ESUS trial provides an opportunity to assess predictors for developing AF and associated recurrent stroke. Methods: RE-SPECT ESUS was a randomized, controlled trial (564 sites, 42 countries) assessing dabigatran versus aspirin for the prevention of recurrent stroke in patients with ESUS. Of 5390 patients enrolled and followed for a median of 19 months, 403 (7.5%) were found to develop AF reported as an adverse event or using cardiac monitoring per standard clinical care. Univariable and multivariable regression analyses were performed to define predictors of AF. Results: In the multivariable model, older age (odds ratio [OR] for 10-year increase 1.99 [1.78-2.23]; P<0.001), hypertension (1.36 [1.03-1.79]; P=0.0304), diabetes (OR 0.74 [0.56-0.96]; P=0.022), and body mass index (OR for 5-unit increase 1.29 [1.16-1.43]; P<0.001) were independent predictors of AF during the study. In a sensitivity analysis restricted to 1117 patients with baseline N-terminal prohormone of brain natriuretic peptide (NT-proBNP) measurements, only older age and higher NT-proBNP were significant independent predictors of AF. Performances of several published predictive models were assessed, including the HAVOC and CHA2DS2-VASc scores, and higher scores were associated with higher rates of developing AF. Conclusions: Besides age as the most important variable, several other factors, including hypertension, higher body mass index, and lack of diabetes, are independent predictors of AF after ESUS. When baseline NT-proBNP was available, only older age and elevation of this biomarker were predictive of subsequent AF. Understanding who is at higher risk of developing AF will assist in identifying patients who may benefit from more intense, long-term cardiac monitoring.

Long-Term Cardiac Monitoring After Embolic Stroke of Undetermined Source: Search Longer, Look Harder

“…31 A recent analysis of the Randomized, Double-Blind, Evaluation in Secondary Stroke Prevention Comparing the Efficacy and Safety of the Oral Thrombin Inhibitor Dabigatran Etexilate Versus Acetylsalicylic Acid in Patients With Embolic Stroke of Undetermined Source (RE-SPECT ESUS) trial showed that a HAVOC score ≥3 was associated with a significantly higher possibility of incident atrial fibrillation compared with a score of 0 or 1 (HR: 2.68, 95% CI: 1.96-3.66). 38 Although an increase in the HAVOC score per 1 point was associated with an increased risk of atrial fibrillation (OR: 1.22, 95% CI:1.16-1.28), the score showed only modest ability to discriminate this risk (c-statistic: 0.62), 38 while the low rate of atrial fibrillation detection in patients with a low HAVOC score was not confirmed in external validation. 39 Similarly the recent Graz AF Risk Score developed among 150 patients with cryptogenic stroke showed good discriminating effect (AUC: 0.85, 95% CI 0.78-0.92).…”

Cryptogenic stroke. Part 3: atrial cardiopathy and silent atrial fibrillation

“…В исследовании Find-AF у пациентов с ИИ и значением NT-ProBNP > 240 пг/мл частота регистрации ФП при 7-дневном ХМ-ЭКГ составила 17,9% (по сравнению с 7,4% в группе с более низким уровнем NT-proBNP) [54]. При анализе данных RE-SPECT ESUS повышение NT-proBNP также было ассоциировано с выявлением ФП (ОР 1,74) [55]. Эксперты AF-SCREEN International Collaboration предлагают более высокий пороговый уровень натрийуретического пептида (400 пг/ мл) как предиктор выявления ФП после ИИ [31].…”

Machine learning in the detection and management of atrial fibrillation

“…Therefore, medical associations have stressed the importance of AF screening and current European guidelines recommend opportunistic screening of all adults > 65 years of age [ 1 ]. Nonetheless, many patients are diagnosed with AF only after a thromboembolic event has occurred [ 3 , 4 ]. Novel technologies such as machine learning may aid clinicians in identifying patients at a high risk of AF and may consequently improve patient outcome by reducing thromboembolic complications [ 5 ].…”