GRASPS、SEDAN、HAT模型对非溶栓性脑梗死出血转化的预测价值

Predic tive value of GRASPS, SEDAN and HAT model for hemorrhage transformation in non-thrombolysis acute cerebral infarction

目的探讨GRASPS、SEDAN、HAT模型在预测非溶栓性脑梗死出血转化中的临床应用价值。方法选择570例未经溶栓的急性脑梗死患者,其中男性375例,女性195例;年龄41~90岁,平均年龄68.41岁。根据头颅CT或MRI检查是否出血分为出血转化组和非出血转化组,其中出血转化组123例,非出血转化组447例。两组同时给予GRASPS、SEDAN、HAT模型评分。采用受试者工作特性曲线（ROC）获得HAT模型、SEDAN模型和GRASPS模型的灵敏度和特异度,计算曲线下面积。结果 HAT模型预测出血转化的灵敏度为63.4%,特异度为70.5%,曲线下面积0.717[95%可信区间（CI）0.661~0.772],最佳诊断界值为1.5。SEDAN模型预测出血转化的灵敏度为48.3%,特异度为51.7%,曲线下面积0.601（95%CI 0.546~0.656）,最佳诊断界值为1.5。GRASPS模型预测出血转化的灵敏度为58.5%,特异度为63.1%,曲线下面积0.620（95%CI 0.564~0.676）,最佳诊断界值为77.5。结论HAT、GRASPS、SEDAN模型用于非溶栓性脑梗死出血转化有一定的预测价值,但以HAT模型预测能力最强。

Objective To investigate the clinical application value of HAT（hemorrhage after thrombolysis）, GRASPS（glucose at presentation, race, age, sex, systolic blood pressure at presentation, severity of stroke at presentation） and SEDAN [baseline blood sugar early infarct signs hyperdense cerebral artery sign on admission CT, age, National Institutes of Health Stroke Scale （NIHSS） on admission] model for predicting hemorrhage transformation（HT） in non-thrombolysis acute cerebral infarction. Methods A total of 570 acute cerebral infarction patients were enrolled, which included 375 males and 195 females, aged 41 - 90 years old with mean age of 68.41 years old. All of them were divided into HT group（with HT, n = 123） and non- HT group（without HT, n = 447） by CT or MRI, which were performed GRASPS, SEDAN and HAT model score. The sensitivity and specificity of predictive performance were assessed by the curve of receiver operating characteristic0ROC）. Results For HAT model, the predicts sensitivity and specificity were 63.4 % and 70.5 %, respectively. The area under curve（AUC）[95 % con- fidence interval（CI） 0.661 - 0.772] and the best diagnostic boundary values were 0.717 and 1.5, respectively. For SEDAN mod- el, predict sensitivity and specificity were 48.3 % and 51.7 %, AUC（95 % CI 0.546 - 0.656） and the best diagnostic boundary values were 0.601 and 1.5, respectively. For GRASPS model, predict sensitivity and specificity were 58,5 % and 63.1%, respectively. The AUC（95 % CI 0.564 - 0.676） and the best diagnostic boundary values were 0.620 and 77.5, respectively. Conclusion It is demonstrated that HAT, GRASPS and SEDAN models are some predictive values for predicting HT in non- thrombolysis acute cerebral infarction, and HAT model shows the highest predictive value.