Chiti Alberto, Giannini Nicola, Terni Eva, Massimetti Gabriele, Gialdini Gino, Mancuso Michelangelo, Bonuccelli Ubaldo, Orlandi Giovanni
Neurology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.
Neurology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.
J Stroke Cerebrovasc Dis. 2015 Jun;24(6):1287-90. doi: 10.1016/j.jstrokecerebrovasdis.2015.01.033. Epub 2015 Apr 20.
Basing on easily available clinical and instrumental data, we aimed to define an "atrial fibrillation profile" able to discriminate cases of stroke due to atrial fibrillation from cases due to atherothrombosis of large vessels or small-vessel disease.
A total of 1037 consecutive patients with ischemic stroke were enrolled. Cases with undetermined stroke, rare causes, and cardioembolic sources of emboli other than atrial fibrillation were excluded from further analysis. Thus, 653 patients were evaluated, dividing them into 2 groups for comparison (164 with stroke due to atrial fibrillation and 489 with atherothrombotic/lacunar stroke). Clinical, echocardiography, and neuroradiologic data were considered to characterize such groups.
Atrial fibrillation and atherothrombotic-lacunar group presented a differential phenotypic profile. Binary multiple logistic regression identified age older than 75 years, female sex, left atrial dilation, cortical-subcortical cerebral index infarct, ischemic lesions in multiple vascular grounds, and spontaneous hemorrhagic transformation of brain infarction as significant predictors of cardioembolic stroke due to atrial fibrillation.
A simple profile, based on commonly available data, seems suitable to characterize patients with stroke due to atrial fibrillation. If further validated, it may be useful to identify patients with undetermined stroke (or other well-defined causes of stroke) at high risk of being affected by undetected subclinical paroxysmal atrial fibrillation, prompting further diagnostic work-up and with potential therapeutic implication.
基于容易获得的临床和影像学数据，我们旨在定义一种“房颤特征”，以区分因房颤导致的中风病例与因大血管动脉粥样硬化血栓形成或小血管疾病导致的中风病例。
共纳入1037例连续性缺血性中风患者。未确定病因的中风病例、罕见病因以及除房颤外的其他心脏栓塞源病例被排除在进一步分析之外。因此，对653例患者进行了评估，并将他们分为两组进行比较（164例因房颤导致中风，489例因动脉粥样硬化血栓形成/腔隙性中风）。临床、超声心动图和神经放射学数据被用于描述这些组的特征。
房颤组和动脉粥样硬化血栓形成-腔隙性中风组呈现出不同的表型特征。二元多因素逻辑回归分析确定，年龄大于75岁、女性、左心房扩大、皮质-皮质下脑梗死指数、多血管区域的缺血性病变以及脑梗死的自发性出血转化是房颤导致的心源性栓塞性中风的重要预测因素。
基于常见数据的简单特征似乎适合描述因房颤导致中风的患者。如果进一步得到验证，它可能有助于识别未确定病因的中风患者（或其他明确的中风病因）中，有未被检测到的亚临床阵发性房颤影响的高风险患者，从而促使进一步的诊断检查，并具有潜在的治疗意义。
