Objective To observe the value of whole-brain CT perfusion(CTP)parameters at different time and clinical data for predicting delayed cerebral ischemia(DCI)and 3-month poor prognosis in patients with aneurysmal subarachnoid hemorrhage(aSAH).Methods Totally 127 aSAH patients were retrospectively enrolled.Clinical and CTP data within 24 h of symptom onset and during DCI time window(DCITW)were collected.The patients were divided into DCI group(n=34)and non-DCI group(n=93)based on DCI occurred or not during hospitalization,also into poor outcome group(modified Rankin scale[mRS]≥3,n=36)and good outcome group(mRS≤2)based on 3-month's follow-up.Multivariate logistic regression was performed to select independent predictive factors among variates being significantly different between groups.Then receiver operating characteristic curve was drawn,and the area under the curve(AUC)was calculated to evaluate the predictive performance of logistic regression model.Results Patients'age,modified Fisher score(mFS),subarachnoid hemorrhage early brain edema score(SEBES)and mean flow extraction product(mFEP)within 24 h of onset were all identified as independent predictive factors of DCI,and the AUC of their combination for predicting DCI during hospitalization was 0.817.Patients' age and mFS within 24 h of onset,alternatively,World Federation of Neurosurgical Societies(WFNS)grade and mFEP during DCITW were all independent predictive predictors of 3 months' prognosis,and the combination of the latter two showed better predictive performance(AUC=0.922)tahn the former two(AUC=0.822,P＜0.05).Conclusion Whole-brain CTP parameters combined with clinical data within 24 h of onset of aSAH could be used to predict the occurrence of DCI during hospitalization,whole-brain CTP parameters during DCITW could be used to predict 3 months'poor prognosis.

Objective To observe the value of whole-brain CT perfusion(CTP)parameters at different time and clinical data for predicting delayed cerebral ischemia(DCI)and 3-month poor prognosis in patients with aneurysmal subarachnoid hemorrhage(aSAH).Methods Totally 127 aSAH patients were retrospectively enrolled.Clinical and CTP data within 24 h of symptom onset and during DCI time window(DCITW)were collected.The patients were divided into DCI group(n=34)and non-DCI group(n=93)based on DCI occurred or not during hospitalization,also into poor outcome group(modified Rankin scale[mRS]≥3,n=36)and good outcome group(mRS≤2)based on 3-month's follow-up.Multivariate logistic regression was performed to select independent predictive factors among variates being significantly different between groups.Then receiver operating characteristic curve was drawn,and the area under the curve(AUC)was calculated to evaluate the predictive performance of logistic regression model.Results Patients'age,modified Fisher score(mFS),subarachnoid hemorrhage early brain edema score(SEBES)and mean flow extraction product(mFEP)within 24 h of onset were all identified as independent predictive factors of DCI,and the AUC of their combination for predicting DCI during hospitalization was 0.817.Patients' age and mFS within 24 h of onset,alternatively,World Federation of Neurosurgical Societies(WFNS)grade and mFEP during DCITW were all independent predictive predictors of 3 months' prognosis,and the combination of the latter two showed better predictive performance(AUC=0.922)tahn the former two(AUC=0.822,P＜0.05).Conclusion Whole-brain CTP parameters combined with clinical data within 24 h of onset of aSAH could be used to predict the occurrence of DCI during hospitalization,whole-brain CTP parameters during DCITW could be used to predict 3 months'poor prognosis.

Objective:To investigate the association between quantified CT (QCT)-measured body composition and metabolic syndrome (MS) components in obese populations before bariatric surgery.Methods:A cross-sectional study. A retrospective analysis was conducted on a cohort of 97 obese patients scheduled for weight-loss surgery at the First Affiliated Hospital of Wannan Medical College from January 2021 to March 2023. The patients′ body mass index (BMI), biochemical parameters and body composition measurements obtained by QCT were recorded. The patients were stratified into groups based on gender, obesity severity and the number of MS components. Differences in body composition among the groups were compared. Additionally, the correlations between each body composition parameter and metabolic indicators were analyzed. The diagnostic efficacy of each body composition parameter for identifying obese individuals with different MS components was assessed using receiver operating characteristic (ROC) curve analysis.Results:There were 75 females (77.3%). Male obese patients had higher total abdominal fat area [(693.23±148.90) vs (574.99±114.89) cm 2, t=-3.958, P<0.001], visceral fat area [(289.65±57.67) vs (195.60±57.37) cm 2, t=-6.753, P<0.001], fat content of pancreatic head [27.45%(21.65%, 45.48%) vs 21.60%(17.6%, 26.9%), Z=-2.675, P=0.007], and skeletal muscle index [73.36(68.74, 81.26) vs 61.52(55.74, 66.41) cm 2/m 2, Z=-5.246, P<0.001]. With the increase of obesity, abdominal fat mainly increases in subcutaneous fat. With the increase of MS components (MS2 group, MS3 group, MS4 group, MS5 group), the abdominal fat area, abdominal fat/subcutaneous fat, liver fat content, pancreatic head fat content, and skeletal muscle index of patients all increased accordingly. In diagnosing the presence of two components of MS, area under the curve of visceral fat area was the largest (AUC=0.706, 95% CI=0.577-0.834). For diagnosing the presence of three, four and five components of MS, area under curve of liver fat content were all the largest (MS3=0.712, 95% CI=0.605-0.818; MS4=0.652, 95% CI=0.537-0.766; MS5=0.706, 95% CI=0.576-0.836). Conclusion:There are differences in QCT body composition among obese patients with different MS components, and there is a correlation between each body composition and MS component. Among them, intra-abdominal fat area and liver fat content are of great value in evaluating obese patients with different MS components.