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ABSTRACT: The performance of scoring systems for risk stratification in patients with atrial fibrillation (AF) was not validated well in patients with stroke. The purpose of this study was to evaluate whether the risk scoring systems predict vascular outcomes in stroke patients with AF.Data were obtained from a nationwide multicenter registry for acute stroke with AF from January 1, 2013, to December 31, 2015. We investigated the predictive power of the CHADS2, CHA2DS2-VASc, ATRIA, and Essen stroke scores in stroke patients with AF. The subjects were further stratified into groups according to treatment with or without oral anticoagulants (OACs).A total of 3112 stroke with AF subjects were included. The rate of recurrent ischemic stroke and any stroke were not associated with the CHADS2, CHA2DS2-VASc, ATRIA, and Essen stroke risk scores. The risks of death and major adverse cerebrovascular and cardiovascular events (MACEs) increased sequentially with the increase of each risk score in OAC group. (the range of C-index 0.544-0.558 for recurrent ischemic stroke; 0.523-0.537 for any stroke; 0.580-0.597 for death; 0.564-0.583 for MACEs). However, in the group treated with OACs, all risk scores were significantly associated with the risk of MACEs. The C-statistics of the 4 scoring systems were 0.544 to 0.558, 0.523 to 0.537, 0.580 to 0.597, 0.564 to 0.583, respectively, for recurrent ischemic stroke, any stroke, death, and MACEs.The performance of the CHADS2, CHA2DS2-VASc, ATRIA, and Essen stroke risk scores for the prediction of recurrent stroke was unsatisfactory in stroke patients with AF whereas the performance for the prediction of recurrent stroke was not MACEs or death was good. A new risk stratification scheme that is specific for secondary stroke prevention in the AF population is needed.
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Fibrilación Atrial/complicaciones , Enfermedades Cardiovasculares/etiología , Indicadores de Salud , Medición de Riesgo/métodos , Accidente Cerebrovascular/etiología , Anciano , Anciano de 80 o más Años , Enfermedades Cardiovasculares/epidemiología , Femenino , Factores de Riesgo de Enfermedad Cardiaca , Humanos , Incidencia , Masculino , Persona de Mediana Edad , Valor Predictivo de las Pruebas , Modelos de Riesgos Proporcionales , Estudios Prospectivos , Recurrencia , Sistema de Registros , República de Corea/epidemiología , Accidente Cerebrovascular/epidemiologíaRESUMEN
OBJECTIVE: Intracranial atherosclerosis is a major cause of ischaemic stroke worldwide. A number of studies have shown the effects of statin treatment on coronary and carotid artery plaques, but there is little evidence on the effects of statin treatment on intracranial atherosclerotic plaques. METHODS: The Intensive Statin Treatment in Acute Ischaemic Stroke Patients with Intracranial Atherosclerosis - High-Resolution Magnetic Resonance Imaging (STAMINA-MRI) Trial is a single-arm, prospective, observational study monitoring imaging and clinical outcomes of high-dose statin treatment among statin-naive patients with acute ischaemic stroke caused by symptomatic intracranial atherosclerosis. The primary outcome was the change in vascular remodelling and plaque characteristics before and after 6 months (median: 179 days, IQR 163-189 days) of statin treatment measured by high-resolution MRI (HR-MRI). RESULTS: A total of 77 patients (mean age: 62.6±13.7 years, 61.0% women) were included in this study. Low-density lipoprotein cholesterol (LDL-C) levels (mg/dL) at initial and follow-up assessments were 125.81±35.69 and 60.95±19.28, respectively. Overall, statin treatment significantly decreased enhancement of plaque volume (mm3, 32.07±39.15 vs 17.06±34.53, p=0.013), the wall area index (7.50±4.28 vs 5.86±4.05, p=0.016) and stenosis degree (%, 76.47±20.23 vs 64.05±21.29, p<0.001), but not the remodelling index (p=0.195). However, 35% patients showed no change or increased enhancement volume and stenosis degree after statin treatment. Higher reduction of LDL-C and longer duration of statin treatment were associated with decreased enhancement volume after statin treatment. CONCLUSIONS: High-dose statin treatment effectively stabilised symptomatic intracranial atherosclerotic plaques as documented by HR-MRI. Further study is needed to determine laboratory and genetic factors associated with poor response to statins and alternative therapeutic options, such as proprotein convertase subtilisin-kexin type 9 inhibitors, for these patients. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov NCT02458755.
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Inhibidores de Hidroximetilglutaril-CoA Reductasas/uso terapéutico , Arteriosclerosis Intracraneal/tratamiento farmacológico , Accidente Cerebrovascular/tratamiento farmacológico , Encéfalo/diagnóstico por imagen , Femenino , Humanos , Inhibidores de Hidroximetilglutaril-CoA Reductasas/administración & dosificación , Arteriosclerosis Intracraneal/complicaciones , Arteriosclerosis Intracraneal/diagnóstico por imagen , Imagen por Resonancia Magnética , Masculino , Persona de Mediana Edad , Neuroimagen , Accidente Cerebrovascular/diagnóstico por imagen , Accidente Cerebrovascular/etiología , Resultado del TratamientoRESUMEN
Background and Purpose- We hypothesized that the pial collateral status at the time of presentation could predict the infarct size on magnetic resonance imaging in patients with similar degrees of early ischemic changes on computed tomography. We tested the association between serial changes in collateral status and infarct volume defined on diffusion-weighted imaging (DWI) in patients with large vessel occlusion and small core. Methods- Consecutive patients who were candidates for endovascular treatment (Alberta Stroke Program Early CT Score [ASPECTS] of ≥6 points) and who underwent both pretreatment multiphasic computed tomography angiography (mCTA) and multimodal magnetic resonance imaging were enrolled. The baseline early ischemic changes and collateral status were determined using both mCTA and magnetic resonance imaging-based collateral maps. Multivariable linear regression was used to evaluate adjusted estimates of the effect of collateral status on predicting MR DWI lesion volume before endovascular treatment. Results- Of 65 patients (39 men; median age, 76 years; median ASPECTS, 8 points [range, 6-10]), 10 (15.4%), 8 (12.3%), and 47 (72.3%) presented poor, intermediate, and good collaterals on mCTA, respectively. After adjusting for the initial stroke severity, ASPECTS, time to DWI, and mismatch volume, the mCTA collateral grade was the only factor independently associated with the DWI lesion volume (ß=-35.657, SE mean=3.539; P<0.0001). An excellent correlation between the mCTA- and magnetic resonance imaging-based collateral grades was observed (matching grade seen in 92.3%), suggesting a collateral status persistence during the hyperacute stroke phase. Conclusions- The mCTA assessed collateral adequacy is the sole predictor of eventual DWI lesion volume before endovascular treatment. The added value of collateral assessment in early ischemic changes and large vessel occlusion for decision-making regarding more aggressive revascularizations requires further evaluation. Clinical Trial Registration- URL: https://www.clinicaltrials.gov. Unique identifier: NCT03234634 and NCT02668627.
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Isquemia Encefálica , Angiografía por Tomografía Computarizada , Imagen de Difusión por Resonancia Magnética , Accidente Cerebrovascular , Anciano , Isquemia Encefálica/diagnóstico por imagen , Isquemia Encefálica/fisiopatología , Femenino , Humanos , Masculino , Persona de Mediana Edad , Estudios Prospectivos , Accidente Cerebrovascular/diagnóstico por imagen , Accidente Cerebrovascular/fisiopatologíaRESUMEN
Background and Purpose- Automatic segmentation of cerebral infarction on diffusion-weighted imaging (DWI) is typically performed based on a fixed apparent diffusion coefficient (ADC) threshold. Fixed ADC threshold methods may not be accurate because ADC values vary over time after stroke onset. Deep learning has the potential to improve the accuracy, provided that a large set of correctly annotated lesion data is used for training. The purpose of this study was to evaluate deep learning-based methods and compare them with commercial software in terms of lesion volume measurements. Methods- U-net, an encoder-decoder convolutional neural network, was adopted to train segmentation models. Two U-net models were developed: a U-net (DWI+ADC) model, trained on DWI and ADC data, and a U-net (DWI) model, trained on DWI data only. A total of 296 subjects were used for training and 134 for external validation. An expert neurologist manually delineated the stroke lesions on DWI images, which were used as the ground-truth reference. Lesion volume measurements from the U-net methods were compared against the expert's manual segmentation and Rapid Processing of Perfusion and Diffusion (RAPID; iSchemaView Inc) analysis. Results- In external validation, U-net (DWI+ADC) showed the highest intraclass correlation coefficient with manual segmentation (intraclass correlation coefficient, 1.0; 95% CI, 0.99-1.00) and sufficiently high correlation with the RAPID results (intraclass correlation coefficient, 0.99; 95% CI, 0.98-0.99). U-net (DWI+ADC) and manual segmentation resulted in the smallest 95% Bland-Altman limits of agreement (-5.31 to 4.93 mL) with a mean difference of -0.19 mL. Conclusions- The presented deep learning-based method is fully automatic and shows a high correlation of diffusion lesion volume measurements with manual segmentation and commercial software. The method has the potential to be used in patient selection for endovascular reperfusion therapy in the late time window of acute stroke.