PDF(Pubmed)
Abstract：
UNASSIGNED: To explore the risk factors of proteinuria in Omicron variant patients and to construct and verify the risk predictive model.
UNASSIGNED: 1091 Omicron patients who were hospitalized from August 2022 to November 2022 at Tianjin First Central Hospital were defined as the derivation cohort. 306 Omicron patients who were hospitalized from January 2022 to March 2022 at the same hospital were defined as the validation cohort. The risk factors of proteinuria in derivation cohort were screened by univariate and multivariate logistic regression analysis, and proteinuria predicting scoring system was constructed and the receiver operating characteristic(ROC)curve was drawn to test the prediction ability. The proteinuria risk model was externally validated in validation cohort.
UNASSIGNED: 7 factors including comorbidities, blood urea nitrogen (BUN), serum sodium (Na), uric acid (UA), C reactive protein (CRP) and vaccine dosages were included to construct a risk predictive model. The score ranged from -5 to 16. The area under the ROC curve(AUC) of the model was 0.8326(95% CI 0.7816 to 0.8835, p < 0.0001). Similarly to that observed in derivation cohort, the AUC is 0.833(95% CI 0.7808 to 0.9002, p < 0.0001), which verified good prediction ability and diagnostic accuracy in validation cohort.
UNASSIGNED: The risk model of proteinuria after Omicron infection had better assessing efficiency which could provide reference for clinical prediction of the risk of proteinuria in Omicron patients.
UNASSIGNED: 1091 Omicron patients who were hospitalized from August 2022 to November 2022 at Tianjin First Central Hospital were defined as the derivation cohort. 306 Omicron patients who were hospitalized from January 2022 to March 2022 at the same hospital were defined as the validation cohort. The risk factors of proteinuria in derivation cohort were screened by univariate and multivariate logistic regression analysis, and proteinuria predicting scoring system was constructed and the receiver operating characteristic(ROC)curve was drawn to test the prediction ability. The proteinuria risk model was externally validated in validation cohort.
UNASSIGNED: 7 factors including comorbidities, blood urea nitrogen (BUN), serum sodium (Na), uric acid (UA), C reactive protein (CRP) and vaccine dosages were included to construct a risk predictive model. The score ranged from -5 to 16. The area under the ROC curve(AUC) of the model was 0.8326(95% CI 0.7816 to 0.8835, p < 0.0001). Similarly to that observed in derivation cohort, the AUC is 0.833(95% CI 0.7808 to 0.9002, p < 0.0001), which verified good prediction ability and diagnostic accuracy in validation cohort.
UNASSIGNED: The risk model of proteinuria after Omicron infection had better assessing efficiency which could provide reference for clinical prediction of the risk of proteinuria in Omicron patients.
摘要：
■探讨Omicron变异型患者蛋白尿的危险因素，构建并验证风险预测模型。
■1091例于2022年8月至2022年11月在天津市第一中心医院住院的Omicron患者被定义为派生队列。从2022年1月至2022年3月在同一家医院住院的306名Omicron患者被定义为验证队列。采用单因素和多因素logistic回归分析筛选衍生队列中蛋白尿的危险因素,构建蛋白尿预测评分系统,绘制受试者工作特征(ROC)曲线,检验其预测能力。在验证队列中对蛋白尿风险模型进行了外部验证。
■7个因素，包括合并症，血尿素氮(BUN),血清钠（Na），尿酸（UA），纳入C反应蛋白（CRP）和疫苗剂量以构建风险预测模型。评分范围从-5到16。模型的ROC曲线下面积（AUC）为0.8326（95％CI为0.7816至0.8835，p<0.0001）。与在派生队列中观察到的类似，AUC为0.833(95%CI0.7808至0.9002，p<0.0001)，在验证队列中验证了良好的预测能力和诊断准确性。
■Omicron感染后蛋白尿风险模型具有较好的评估效率，可为临床预测Omicron患者蛋白尿风险提供参考。
■1091例于2022年8月至2022年11月在天津市第一中心医院住院的Omicron患者被定义为派生队列。从2022年1月至2022年3月在同一家医院住院的306名Omicron患者被定义为验证队列。采用单因素和多因素logistic回归分析筛选衍生队列中蛋白尿的危险因素,构建蛋白尿预测评分系统,绘制受试者工作特征(ROC)曲线,检验其预测能力。在验证队列中对蛋白尿风险模型进行了外部验证。
■7个因素，包括合并症，血尿素氮(BUN),血清钠（Na），尿酸（UA），纳入C反应蛋白（CRP）和疫苗剂量以构建风险预测模型。评分范围从-5到16。模型的ROC曲线下面积（AUC）为0.8326（95％CI为0.7816至0.8835，p<0.0001）。与在派生队列中观察到的类似，AUC为0.833(95%CI0.7808至0.9002，p<0.0001)，在验证队列中验证了良好的预测能力和诊断准确性。
■Omicron感染后蛋白尿风险模型具有较好的评估效率，可为临床预测Omicron患者蛋白尿风险提供参考。
