乙型肝炎肝硬化失代偿患者发生细菌感染的风险预测模型建立

Establishment of a risk prediction model for bacterial infections in decompensated patients with hepatitis B cirrhosis

目的分析乙型肝炎肝硬化失代偿患者发生细菌感染的影响因素并构建风险预测模型。方法回顾性分析2014年1月至2020年4月在浙江省人民医院收治的198例乙型肝炎肝硬化失代偿患者资料。所有患者根据是否发生细菌感染分为细菌感染组(n=86)和非细菌感染组(n=112)。采用多因素Logistic回归模型分析乙型肝炎肝硬化失代偿患者发生细菌感染的危险因素,以逻辑回归模型为基础,通过R软件绘制列线图,构建风险预测模型。通过受试者工作曲线(ROC)对该风险预测模型进行评估。结果多因素Logistic回归分析结果显示,既往吸烟史、凝血酶原时间、中性粒细胞计数、超敏C-反应蛋白为乙型肝炎肝硬化失代偿患者发生细菌感染的独立危险因素(P<0.05或<0.01),而规律抗病毒治疗和高密度脂蛋白为独立保护因素(P<0.05或<0.01)。ROC曲线分析显示,风险预测模型的曲线下面积(AUC)为0.872(95%CI 0.820~0.924,P<0.01),MELD评分的AUC为0.670(95%CI 0.599~0.735,P<0.01)。风险预测模型预测乙型肝炎肝硬化失代偿发生细菌感染的价值高于MELD评分(Z=4.89,P<0.01)。结论该风险预测模型对于乙型肝炎肝硬化失代偿患者发生细菌感染具有较好的预测价值。

Objective To explore the risk factors of bacterial infections in patients with decompensated hepatitis B cirrhosis and to construct a risk prediction model.Methods The clinical data of 198 patients with decompensated hepatitis B cirrhosis admitted in Zhejiang Provincial People’s Hospital from January 2014 to April 2020 were retrospectively analyzed.There were 86 patients with bacterial infection(infection group)and 112 patients without bacterial infections(non-infection group).The risk factors of bacterial infections were analyzed by multivariate Logistic regression.R language was used to establish a nomogram model to predict the risk of bacterial infection in patients with decompensated hepatitis B cirrhosis.Receiver operating characteristic(ROC)curve was used to explore the prediction efficiency of the nomogram model for bacterial infection.Results Multivariate logistic regression analysis showed that previous smoking history,prothrombin time,neutrophil count and hypersensitive C protein were independent risk factors for bacterial infection in patients with decompensated hepatitis B cirrhosis(P<0.05 or<0.01),while regular antiviral treatment and high-density lipoprotein were protective factors(P<0.05 or<0.01).ROC curve showed that the area under the curve(AUC)of risk prediction model for bacterial infections was 0.872(95%CI 0.820-0.924,P<0.01),and AUC of MELD score for predicting bacterial infections was 0.670(95%CI 0.599-0.735,P<0.01);the risk prediction model was superior to MELD score in prediction(Z=4.89,P<0.01).Conclusions The established risk prediction model in this study can more accurately predict the occurrence of bacterial infections in patients with decompensated hepatitis B cirrhosis.