The determinants of stroke phenotypes were different from the predictors (CHADS₂ and CHA₂DS₂-VASc) of stroke in patients with atrial fibrillation: a comprehensive approach

We analyzed demographic, clinical, laboratory, and radiographic data collected prospectively from consecutive patients admitted to Samsung Medical Centre for acute cerebral infarction from January 2005 through June 2011. Inclusion criteria for this study were: (a) presentation within three days of the onset of symptoms, (b) acute ischemic lesions within the MCA distribution on diffusion-weighted imaging (DWI), and (c) AF diagnosed previously or detected during the admission period. The exclusion criteria were: (a) patients who met the Trial of Org 10172 in the Acute Stroke Treatment algorithm competing diagnosis of large artery disease, small vessel, or other causes, (b) patients who did not have INR determination and DWI performed within 12 hours of initial presentation, and (c) patients who had INR levels greater than 1.6 (achieving INRs distributed equally in the range of 1.6 to 2.5 has been reported to provide 90% of the protection of INRs between 2.0 and 3.0 for primary prevention of stroke in patients with nonvalvular AF) [6]. The local Institutional Review Board approved the study and informed consent was obtained from participants.

All patients underwent MRI (3.0T, Achieva, Philips Medical Systems) using a protocol that also included DWI, gradient-recalled echo, fluid-attenuated inversion recovery, and magnetic resonance angiography (MRA) imaging of the cervical and intracranial vessels [16].

Variables that could potentially affect the acute infarct volume were recorded for each patient. These included sex, age, prior history of hypertension, diabetes, hyperlipidemia, valvular heart disease, coronary heart disease, time from symptom onset to MRI, and medications used before the onset of stroke including the statins, angiotensin receptor blockers, antiplatelet agent and warfarin. The CHADS₂ score,[17] which is a simple method of estimating the risk of stroke in patients with non-rheumatic AF, was estimated from the clinical data and modified to correlate the score with stroke phenotype. Five risk factors are considered when determining the CHADS₂ score: (a) C, recent congestive heart failure, (b) H, hypertension, (c) A, age ≥ 75 years, (d) D, diabetes mellitus, and (e) S, history of stroke or transient ischemic attack. In the present study, we used prior stroke or transient ischemic attack instead of the stroke index. In addition, we investigated use of the new CHA₂DS₂-VASc [18] score system for predicting the stroke phenotype, including additional stroke risk factors: (f) V, indicates vascular disease, (g) A, indicates age between 65-74 and (h) S, indicates sex category(female). The National Institutes of Health Stroke Scale (NIHSS) score on admission was also used to assess the severity of stroke. Stroke onset time was defined as the last time the patient was known to be free of symptoms. All patients underwent routine blood tests and high-sensitivity C-reactive protein (CRP) was taken as the impending cerebrovascular marker [19, 20]. Hemostatic markers of prothrombotic tendency, including D-dimer and fibrinogen, were also evaluated. Patients underwent electrocardiography and cardiac telemetry for at least 24 hours to evaluate the persistence of AF. Paroxysmal AF is defined as intermittent periods of AF interposed with episodes of normal sinus rhythm, normally < 7 days [21]. In addition, echocardiogram was conducted in all but 12 patients who showed poor cooperation or acute illness owing to medical conditions or the stroke itself. During the echocardiogram, the size of the left atrium and left ventricular ejection fraction were measured,[22‐25] and the E/e' ratio and DT were measured to diagnose the diastolic dysfunction [26].

DWI Infarct volume measurement: Methods and Image Analysis

DWI was obtained using two levels of diffusion sensitization (b values of 0 and 1000 s/mm²; 5-7 mm slice thickness; no gap). For each patient, the DWI lesion volumes were automatically outlined with subsequent manual corrections. The volumes were calculated using a computer-assisted volumetric analysis program (Medical Image Processing, Analysis and Visualization, Version 3.0, National Institutes of Health, Bethesda, Md). Raters outlined regions of acute diffusion abnormality on the b = 1000 s/mm² image while consulting the apparent diffusion coefficient and FLAIR sequences to distinguish acute from nonacute changes in diffusion. All acute DWI lesions were defined on a slice-by-slice basis using a semiautomatic threshold approach by a rater who was blinded to all clinical information.

We divided the patients into three infarct topography pattern groups based on the observed DWI patterns: (a) territorial infarcts involving two or three MCA subdivisions (superior, inferior, or deep), (b) lobar infarcts involving one subdivision, large deep infarcts, and mixed cortical and deep infarcts, and (c) small (< 1 cm in diameter) scattered patterns, suggesting microembolism. Two readers blinded to the clinical data analyzed the DWI data, and the interobserver agreement (κ-value) was 0.92 (P < 0.001) for interpretation of the DWI lesion pattern (territory vs. lobar/deep vs. small scattered). The opinion of a third reader was obtained in cases of disagreement.

All numeric data are presented as the mean ± SD or median (interquartile range [IQR]). The differences in DWI pattern and infarct volume and the clinical and laboratory parameters were evaluated by one-way ANOVA with post hoc analysis using the Fisher's least-square difference or Kruskall-Wallis test for continuous variables and Pearson's χ², Fisher's exact test or linear by linear association for categorical variables. An independent t-test or the Mann-Whitney U test was used to evaluate differences in the factors between the groups. The agreement rate and κ-value between the infarct patterns reported by inter-observers was also obtained. Moreover, multivariate logistic regression analysis (stepwise method) was conducted to predict the independent contribution of factors that influenced stroke phenotypes (territorial pattern or highest quartile DWI lesion volumes). Variables from univariate analyses at P < 0.05 were considered to represent the explanatory variables and the other variables that we were specifically interested in; age, CHADS₂ and CHA₂DS₂-VASc score and PT were entered for multivariate analysis. Among males in the present study, there were 29 current smokers and 24 ex smokers; however, there were only 2 female smokers and no female ex smokers. Accordingly, we did not analyze the impact of smoking on stroke phenotypes. P values < 0.05 were considered to be statistically significant.