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      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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          Risk Factors, Outcome, and Treatment in Subtypes of Ischemic Stroke: The German Stroke Data Bank

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            Differences in vascular risk factors between etiological subtypes of ischemic stroke: importance of population-based studies.

            To understand the mechanisms of stroke and to target prevention, we need to know how risk factors differ between etiological subtypes. Hospital-based studies may be biased because not all stroke patients are admitted. If risk factors differ between patients who are admitted and those who are not, then case-control studies will be biased. If the likelihood of admission also depends on stroke subtype, then case-case comparisons may also be biased. We compared risk factors and ischemic stroke subtypes (TOAST classification) in hospitalized and nonhospitalized patients in 2 population-based stroke incidence studies: the Oxford Vascular Study (OXVASC) and Oxfordshire Community Stroke Project (OCSP). We also performed a meta-analysis of risk factor-stroke subtype associations with other published population-based studies. In OXVASC and OCSP, stroke subtypes differed between hospitalized (293 of 647) and nonhospitalized patients (P<0.0001), with more cardioembolic strokes (odds ratio [OR], 1.8; 95% CI, 1.3 to 2.6) and fewer lacunar strokes (OR, 0.4; 95% CI, 0.3 to 0.7). Premorbid blood pressure and cholesterol were higher in hospitalized patients (both P<0.0001). Risk factor-stroke subtype associations in hospitalized patients were consequently biased (P=0.001). Meta-analysis of data from all patients in OXVASC, OCSP, and 2 other studies demonstrated consistent risk factor-stroke subtype associations. However, contrary to previous hospital-based studies, there was only a weak (OR, 1.4; 95% CI, 1.1 to 1.8) and inconsistent (P(heterogeneity)=0.01) association between small-vessel stroke and hypertension and no association with diabetes (OR, 1.0; 95% CI, 0.7 to 1.3). Prevalences of risk factors and stroke subtypes differ between hospitalized and nonhospitalized patients with ischemic stroke, which may bias hospital-based risk factor studies. Meta-analysis of population-based studies suggests that vascular risk factors differ between stroke subtypes.
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              New Approach to Stroke Subtyping: The A-S-C-O (Phenotypic) Classification of Stroke

              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).
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                Author and article information

                Journal
                New England Journal of Medicine
                N Engl J Med
                Massachusetts Medical Society
                0028-4793
                1533-4406
                June 26 2014
                June 26 2014
                : 370
                : 26
                : 2478-2486
                Article
                10.1056/NEJMoa1313600
                24963567
                © 2014
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