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CTA联合CTP在预测急性脑梗死预后中的价值 被引量:21

The Value of CTA Combined with CTP in Predicting the Prognosis of Acute Cerebral Infarction
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摘要 本研究探讨CT血管成像(CTA)联合CT灌注成像(CTP)在预测急性脑梗死预后的价值。选取急性脑梗死患者102例,分为预后良好组和不良组,分析预后良好和不良患者ASPECTS、CTP参数差异。预后不良患者CTA图像ASPECTS评分<3分明显高于预后良好患者(P<0.05);预后不良患者病灶处脑血容量(CBV)和脑血流量(CBF)明显低于预后良好患者(P<0.05),达峰时间(TTP)和平均通过时间(MTT)明显高于预后良好患者(P<0.05);ASPECTS评分联合CBV、CBF及MTT预测预后不良的ROC曲线下面积为0.895(P<0.05),灵敏性和特异性分别为90.00%和84.10%。CTA联合CTP预测急性脑梗死患者预后有一定应用价值。 This study explores the value of CT angiography(CTA) combined with CT perfusion imaging(CTP) in predicting the prognosis of acute cerebral infarction. 102 patients with acute cerebral infarction were selected, the patients were divided into good prognosis group and poor prognosis group, the differences of ASPECTS and CTP parameters between good prognosis and poor prognosis were analyzed. The NIHSS score of poor prognosis group was significantly higher than that of good prognosis group(P<0.05). The proportion of CTA image ASPECTS score < 3 in patients with poor prognosis was significantly higher than that in patients with good prognosis(P<0.05). The cerebral blood volume(CBV) and cerebral blood flow(CBF) at the lesion of patients with poor prognosis were significantly lower than those of patients with good prognosis(P<0.05), the peak time(TTP) and mean passage time(MTT) were significantly higher than those of patients with good prognosis(P<0.05). The area under the ROC curve of the ASPECTS score combined with CBV, CBF and MTT for predicting poor prognosis was 0.895(P<0.05), and the sensitivity and specificity were 90.00% and 84.10%, respectively. CTA combined with CTP has a certain application value in predicting the prognosis of patients with acute cerebral infarction.
作者 杜静波 刘迎新 李振武 DU Jingbo;LIU Yingxin;LI Zhenwu(Radiology Department,Beijing Daxing District People’s Hospital,Beijing 102600,P.R.China)
出处 《影像科学与光化学》 CAS 北大核心 2022年第1期78-82,共5页 Imaging Science and Photochemistry
关键词 CT血管成像 CT灌注成像 急性脑梗死 预后 预测价值 CT angiography CT perfusion acute cerebral infarction prognosis predictive value
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