He Dan, Shi Qiang, Xu Guangjing, Hu Zheng, Li Xuefei, Li Qian, Guo Yinping, Xu Shabei, Lin Yongbo, Yu Zhiyuan, Wang Wei, Luo Xiang
Department of Neurology National Key Clinical Department and Key Discipline of Neurology The First Affiliated Hospital Sun Yat-sen University Guangzhou Guangdong China.
School of Software Engineering Huazhong University of Science & Technology Wuhan China.
Ann Clin Transl Neurol. 2018 Sep 24;5(11):1323-1337. doi: 10.1002/acn3.647. eCollection 2018 Nov.
The higher than expected PFO rate in CS patients has raised concerns that paradoxical embolism maybe the pathophysiologic mechanism for strokes. However, only a small proportion of pathogenic PFOs cause CS. Therefore, accurate recognition of patients with pathogenic PFOs among all CS patients could guide clinical decision making in selecting the most appropriate treatment. The aim of this study was to devise a new algorithm to stratify cryptogenic stroke (CS) patients into pathogenic patent foramen ovale (p-PFO)- and non-p-PFO-related patients.
A total of 1201 patients with acute ischemic stroke were recruited from two different medical centers, and 253 CS patients were identified. Of the 253 patients, 111 were diagnosed with PFO using contrast transcranial Doppler. Data on medical histories, neuroimaging and laboratory tests were compared in CS patients with or without PFO.
Compared with PFO-negative CS patients, PFO-positive CS patients showed younger onset age, lower incidence of hypertension and dyslipidemia, characteristic infarction pattern in magnetic resonance imaging and specifically altered platelet activity and coagulation function. Based on the above information, we constructed a PFO judgment formula (Hr-PFOJ) by means of feature weight estimation and predictive performance evaluation to predict pathogenic PFO in CS patients with a sensitivity of 76.3% and a specificity of 66.5%.
Hr-PFOJ judgment formula is a useful screening tool for identification of patients with pathogenic PFO who may benefit from PFO-related treatment.
CS患者中卵圆孔未闭(PFO)发生率高于预期,这引发了人们对反常栓塞可能是卒中病理生理机制的担忧。然而,只有一小部分致病性PFO会导致CS。因此,在所有CS患者中准确识别出致病性PFO患者,可为选择最合适治疗方法的临床决策提供指导。本研究的目的是设计一种新算法,将隐源性卒中(CS)患者分为致病性卵圆孔未闭(p-PFO)相关患者和非p-PFO相关患者。
从两个不同的医疗中心招募了1201例急性缺血性卒中患者,其中253例被确定为CS患者。在这253例患者中,111例通过对比经颅多普勒诊断为PFO。对有或无PFO的CS患者的病史、神经影像学和实验室检查数据进行了比较。
与PFO阴性的CS患者相比,PFO阳性的CS患者发病年龄更小,高血压和血脂异常的发生率更低,磁共振成像有特征性梗死模式,血小板活性和凝血功能有特异性改变。基于上述信息,我们通过特征权重估计和预测性能评估构建了一个PFO判断公式(Hr-PFOJ),以预测CS患者中的致病性PFO,灵敏度为76.3%,特异度为66.5%。
Hr-PFOJ判断公式是一种有用的筛查工具,可用于识别可能从PFO相关治疗中获益的致病性PFO患者。