Cryptogenic Stroke and Underlying Atrial Fibrillation

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Abstract

Current guidelines recommend at least 24 hours of electrocardiographic (ECG) monitoring after an ischemic stroke to rule out atrial fibrillation. However, the most effective duration and type of monitoring have not been established, and the cause of ischemic stroke remains uncertain despite a complete diagnostic evaluation in 20 to 40% of cases (cryptogenic stroke). Detection of atrial fibrillation after cryptogenic stroke has therapeutic implications. We conducted a randomized, controlled study of 441 patients to assess whether long-term monitoring with an insertable cardiac monitor (ICM) is more effective than conventional follow-up (control) for detecting atrial fibrillation in patients with cryptogenic stroke. Patients 40 years of age or older with no evidence of atrial fibrillation during at least 24 hours of ECG monitoring underwent randomization within 90 days after the index event. The primary end point was the time to first detection of atrial fibrillation (lasting >30 seconds) within 6 months. Among the secondary end points was the time to first detection of atrial fibrillation within 12 months. Data were analyzed according to the intention-to-treat principle. By 6 months, atrial fibrillation had been detected in 8.9% of patients in the ICM group (19 patients) versus 1.4% of patients in the control group (3 patients) (hazard ratio, 6.4; 95% confidence interval [CI], 1.9 to 21.7; P<0.001). By 12 months, atrial fibrillation had been detected in 12.4% of patients in the ICM group (29 patients) versus 2.0% of patients in the control group (4 patients) (hazard ratio, 7.3; 95% CI, 2.6 to 20.8; P<0.001). ECG monitoring with an ICM was superior to conventional follow-up for detecting atrial fibrillation after cryptogenic stroke. (Funded by Medtronic; CRYSTAL AF ClinicalTrials.gov number, NCT00924638.).

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Most cited references 16

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Performance of a new leadless implantable cardiac monitor in detecting and quantifying atrial fibrillation: Results of the XPECT trial.

, Evgueny Pokushalov, Helmut Pürerfellner … (2010)

Current methods for detecting atrial fibrillation (AF) have limited diagnostic yield. Continuous monitoring with automatic arrhythmia detection and classification may improve detection of symptomatic and asymptomatic AF and subsequent patient treatment. The study purpose was to quantify the performance of the first implantable leadless cardiac monitor (ICM) with dedicated AF detection capabilities. Patients (n=247) with an implanted ICM (Reveal XT, Medtronic Inc, Minneapolis, Minn) who were likely to present with paroxysmal AF were selected. A special Holter device stored 46 hours of subcutaneously recorded ECG, ICM markers, and 2 surface ECG leads. The ICM automatic arrhythmia classification was compared with the core laboratory classification of the surface ECG. Of the 206 analyzable Holter recordings collected, 76 (37%) contained at least 1 episode of core laboratory classified AF. The sensitivity, specificity, positive predictive value, and negative predictive value for identifying patients with any AF were 96.1%, 85.4%, 79.3%, and 97.4%, respectively. The AF burden measured with the ICM was very well correlated with the reference value derived from the Holter (Pearson coefficient=0.97). The overall accuracy of the ICM for detecting AF was 98.5%. In this ICM validation study, the dedicated AF detection algorithm reliably detected the presence or absence of AF and the AF burden was accurately quantified. The ICM is a promising new diagnostic and monitoring tool for the clinician to treat AF patients independently of symptoms. Long-term studies are needed to evaluate the clinical benefits of the technology.

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New Approach to Stroke Subtyping: The A-S-C-O (Phenotypic) Classification of Stroke

P Amarenco, J Bogousslavsky, L.R. Caplan … (2009)

We now propose a new approach to stroke subtyping. The concept is to introduce a complete ‘stroke phenotyping’ classification (i.e. stroke etiology and the presence of all underlying diseases, divided by grade of severity) as distinguished from past classifications that subtype strokes by characterizing only the most likely cause(s) of stroke. In this phenotype-based classification, every patient is characterized by A-S-C-O: A for atherosclerosis, S for small vessel disease, C for cardiac source, O for other cause. Each of the 4 phenotypes is graded 1, 2, or 3. One for ‘definitely a potential cause of the index stroke’, 2 for ‘causality uncertain’, 3 for ‘unlikely a direct cause of the index stroke (but disease is present)’. When the disease is completely absent, the grade is 0; when grading is not possible due to insufficient work-up, the grade is 9. For example, a patient with a 70% ipsilateral symptomatic stenosis, leukoaraiosis, atrial fibrillation, and platelet count of 700,000/mm 3 would be classified as A1-S3-C1-O3. The same patient with a 70% ipsilateral stenosis, no brain imaging, normal ECG, and normal cardiac imaging would be identified as A1-S9-C0-O3. By introducing the ‘level of diagnostic evidence’, this classification recognizes the completeness, the quality, and the timing of the evaluation to grade the underlying diseases. Diagnostic evidence is graded in levels A, B, or C: A for direct demonstration by gold-standard diagnostic tests or criteria, B for indirect evidence or less sensitive or specific tests or criteria, and C for weak evidence in the absence of specific tests or criteria. With this new way of classifying patients, no information is neglected when the diagnosis is made, treatment can be adapted to the observed phenotypes and the most likely etiology (e.g. grade 1 in 1 of the 4 A-S-C-O phenotypes), and analyses in clinical research can be based on 1 of the 4 phenotypes (e.g. for genetic analysis purpose), while clinical trials can focus on 1 or several of these 4 phenotypes (e.g. focus on patients A1-A2-A3).