【摘要】 背景西安市2021—2022年跨年期间暴发的新型冠状病毒感染是发生在一个超大城市且病例数众多、规模较大的本土疫情，有必要对该次疫情的相关内容进行分析和总结。目的 分析新型冠状病毒感染患者的疾病特征，探索重症病例的危险因素以及预测指标。方法 回顾性收集2021年12月至2022年1月收治于西安市第四人民医院的新型冠状病毒感染确诊患者的一般资料和实验室检查指标，计算总IgG与淋巴细胞百分比的比值(IgG∶L%)、总IgM与淋巴细胞百分比的比值(IgM∶L%)、总IgG与淋巴细胞计数的比值(IgG∶L#)、总IgM与淋巴细胞计数的比值(IgM∶L#)，将患者分为轻型及普通型组、重型及危重型组。采用多因素Logistics回归分析探究发生重型及危重型新型冠状病毒感染的危险因素；绘制受试者工作特征曲线(ROC曲线)分析各指标对重型及危重型新型冠状病毒感染的预测价值，计算ROC曲线下面积(AUC)，并采用Delong检验比较各指标的AUC。结果 最终纳入699例新型冠状病毒感染确诊患者，分为轻型及普通型组(n=678)和重型及危重型组(n=21)，轻型及普通型组年龄、基础疾病、D-二聚体、IgM∶L%、IgM∶L#低于重型及危重型组，淋巴细胞百分比、淋巴细胞计数高于重型及危重型组(P<0.05)。多因素Logistics回归分析结果显示，年龄[OR=1.068,95%CI(1.031,1.105),P<0.001]、D-二聚体[OR=1.612,95%CI(1.026,2.533),P=0.038]以及IgM∶L#[OR=1.034,95%CI(1.006,1.063),P=0.018]是重型及危重型新型冠状病毒感染发生的危险因素，淋巴细胞百分比[OR=0.918,95%CI(0.844,0.997),P=0.043]是重型及危重型新型冠状病毒感染发生的保护因素。建立重型及危重型新型冠状病毒感染的联合预测模型，P=-5.031+0.065×年龄-0.086×淋巴细胞百分比+0.738×淋巴细胞计数+0.477×D-二聚体+0.034×IgM∶L#，联合检测预测重型及危重型新型冠状病毒感染的AUC为0.912[95%CI(0.858,0.965)]，截断值为0.04，灵敏度为90.00%，特异度为83.18%，预测价值优于年龄(Z=5.314,P<0.001)、淋巴细胞百分比(Z=-1.987,P=0.047)、D-二聚体(Z=2.273,P=0.023)和IgM∶L#(Z=0.161,P<0.001)。结论 在新型冠状病毒感染急性期，存在炎症反应与细胞免疫功能失衡，该失衡与年龄、D-二聚体均是重症新型冠状病毒感染发生的危险因素。包括年龄、D-二聚体、淋巴细胞百分比、IgM∶L#在内的联合指标可有效预测重型和危重型新型冠状病毒感染。更多还原

【Abstract】 Background The outbreak of COVID-19 in Xi’an between 2021 and 2022 was a large-scale local epidemic in a large city with a huge number of cases. It is necessary to analyze and summarize the contents of this outbreak.Objective To analyze the disease characteristics of patients with COVID-19,and to explore the risk factors as well as predictors of serious cases. Methods General data and laboratory parameters were retrospectively collected from patients diagnosed with a new coronavirus pneumonia who were admitted to the Fourth People’s Hospital of Xi’an between December 2021 and January 2022. Based on the the ratios of total IgG to lymphocyte percentage(IgG∶L%),total Ig M to lymphocyte percentage(IgM∶L%),total IgG to lymphocyte count ratio(Ig G∶L#),and total Ig M to lymphocyte count ratio(IgM∶L#),patients were divided into three groups: mild and common,severe and critical. Multivariate Logistic regression analysis was used to explore the risk factors of developing severe and critically new coronavirus;then the ROC curve was drawn to analyze the predictive indexes and predictive value of severe and critical COVID-19,the area under the ROC curve(AUC) was calculated,and the AUC of each index was compared using the Delong test. Results A total of 699 patients with identified COVID-19 were finally included,and divided into two groups: the mild and common(n=678) and the severe and critical(n=21) forms,with the mild and common forms having younger age,and less underlying disease,D-dimer,IgM ∶ L%,IgM ∶ L#,and higher lymphocyte percentage and lymphocyte count than the severe and critical forms(P<0.05). Multivariate Logistic regression analysis showed that age[OR=1.068,95%CI(1.031,1.105),P<0.001],D-dimer [OR=1.612,95%CI(1.026,2.533),P=0.038]as well as IgM ∶ L#[OR=1.034,95%CI(1.006,1.063),P=0.018] were risk factors for the development of severe and dangerous new coronavirus,and lymphocyte percentage [OR=0.918,95%CI(0.844,0.997),P=0.043]was a protective factor for the development of severe and critical new coronavirus. To establish a joint prediction model for severe and critical novel coronavirus infection,P=-5.031+0.065×age-0.086× lymphocyte percentage +0.738× lymphocyte count +0.477× D-dimer +0.034×IgM∶L#,and the cutoff value for combined detection to predict severe and critical COVID-19 was 0.04,with a sensitivity of 90.00%,a specificity of 83.18%,and its AUC of 0.912[95%CI(0.858,0.965)],which was greater than that for age(Z=5.314,P<0.001),lymphocyte percentage(Z=-1.987,P=0.047),D-dimer(Z=2.273,P=0.023),and IgM∶L#(Z=0.161,P<0.001),with statistically significant differences. Conclusion In the acute phase of COVID-19,there is an imbalance between inflammatory response and cellular immune function,and this imbalance,along with age and D-dimer,are all risk factors for severe COVID-19. Combined indicators including age,D-dimer,lymphocyte percentage and IgM∶L# can effectively predict severe and critical COVID-19.更多还原