■本研究的目的是确定严重白质高强度(WMH)伴肥胖(SWO)的预测因子,并建立未经核磁共振成像(MRI)检查筛查重度WMH肥胖人群的预测模型。
■从2020年9月至2021年10月,连续招募了650名WMH患者。受试者分为两组,SWO组和非SWO组。应用单因素和Logistic回归分析探讨SWO的潜在预测因子。在建立SWO的预测模型时,采用Youden指数法确定最佳临界值。每个参数有两个选项,低和高。构建基于logistic回归的预测模型和列线图。在650个科目中,487名受试者(75%)被随机分配到训练组,163名受试者(25%)被分配到验证组。通过重新采样受试者的操作特性和校准曲线的曲线下面积(AUC)1000次,诺模图性能得到了验证。使用决策曲线分析(DCA)来评估列线图的临床有用性。通过重新采样受试者的操作特性和校准曲线的曲线下面积(AUC)1000次,诺模图性能得到了验证。使用决策曲线分析(DCA)来评估列线图的临床有用性。
■Logistic回归表明高血压,尿酸(UA),补体3(C3)和白细胞介素8(IL-8)是SWO的独立危险因素。高血压,UA,C3,IL-8,叶酸(FA),在预测模型中,空腹C肽(FCP)和嗜酸性粒细胞可用于预测SWO的发生,具有良好的诊断性能,总分的曲线下面积(AUC)为0.823(95%CI:0.760-0.885,p<0.001),灵敏度为60.0%,特异性91.4%。在开发小组中,列线图的AUC(C统计量)为0.829(95%CI:0.760-0.899),在验证组中,它是0.835(95%CI:0.696,0.975)。在开发和验证组中,1,000个引导程序后的校准曲线显示,观察到的概率与预测的概率之间具有令人满意的拟合。DCA显示列线图具有很大的临床实用性。
■高血压,UA,C3,IL-8,FA,FCP和嗜酸性粒细胞模型有可能预测SWO的发生率。当模型总分超过9分时,SWO的风险将大大增加,和列线图可以可视化患者的WMH风险。我们的模型的应用前景主要在于无需MRI检查即可方便地筛查SWO,以检测SWO并及早控制WMH危害。
UNASSIGNED: The purpose of the present study was to identify predictors of severe white matter hyperintensity (WMH) with obesity (SWO), and to build a prediction model for screening obese people with severe WMH without Nuclear Magnetic Resonance Imaging (MRI) examination.
UNASSIGNED: From September 2020 to October 2021, 650 patients with WMH were recruited consecutively. The subjects were divided into two groups, SWO group and non-SWO group. Univariate and Logistic regression analysis were was applied to explore the potential predictors of SWO. The Youden index method was adopted to determine the best cut-off value in the establishment of the prediction model of SWO. Each parameter had two options, low and high. The score table of the prediction model and nomogram based on the logistic regression were constructed. Of the 650 subjects, 487 subjects (75%) were randomly assigned to the training group and 163 subjects (25%) to the validation group. By resampling the area under the curve (AUC) of the subject\'s operating characteristics and calibration curves 1,000 times, nomogram performance was verified. A decision curve analysis (DCA) was used to evaluate the nomogram\'s clinical usefulness. By resampling the area under the curve (AUC) of the subject\'s operating characteristics and calibration curves 1,000 times, nomogram performance was verified. A decision curve analysis (DCA) was used to evaluate the nomogram\'s clinical usefulness.
UNASSIGNED: Logistic regression demonstrated that hypertension, uric acid (UA), complement 3 (C3) and Interleukin 8 (IL-8) were independent risk factors for SWO. Hypertension, UA, C3, IL-8, folic acid (FA), fasting C-peptide (FCP) and eosinophil could be used to predict the occurrence of SWO in the prediction models, with a good diagnostic performance, Areas Under Curves (AUC) of Total score was 0.823 (95% CI: 0.760-0.885, p < 0.001), sensitivity of 60.0%, specificity of 91.4%. In the development group, the nomogram\'s AUC (C statistic) was 0.829 (95% CI: 0.760-0.899), while in the validation group, it was 0.835 (95% CI: 0.696, 0.975). In both the development and validation groups, the calibration curves following 1,000 bootstraps showed a satisfactory fit between the observed and predicted probabilities. DCA showed that the nomogram had great clinical utility.
UNASSIGNED: Hypertension, UA, C3, IL-8, FA, FCP and eosinophil models had the potential to predict the incidence of SWO. When the total score of the model exceeded 9 points, the risk of SWO would increase significantly, and the nomogram enabled visualization of the patient\'s WMH risk. The application prospect of our models mainly lied in the convenient screening of SWO without MRI examination in order to detect SWO and control the WMH hazards early.