HBV相关慢加急性肝衰竭并发细菌感染的预测模型的构建

肝脏 ›› 2022, Vol. 27 ›› Issue (9): 966-972.

HBV相关慢加急性肝衰竭并发细菌感染的预测模型的构建

郑惠芳, 林升龙, 郑嵩, 黄雨欣, 肖珊颖, 叶子杰, 林明华, 高海兵

350004 福州福建医科大学(郑惠芳,郑嵩,黄雨欣,肖珊颖,叶子杰);福建医科大学孟超肝胆医院重症肝病科(林升龙,林明华,高海兵)

收稿日期:2021-11-30 出版日期:2022-09-30 发布日期:2022-10-27
通讯作者: 高海兵,Email: gaohb605@163.com
基金资助:
福建医科大学大学生创新创业训练计划资助项目(C20082)

Establishment of a prediction model for bacterial infection in patients with HBV related acute-on-chronic liver failure

ZHENG Hui-fang¹, LIN Sheng-long², ZHENG Song¹, HUANG Yu-xin¹, XIAO Shan-ying¹, YE Zi-jie¹, LIN Ming-hua², GAO Hai-bing²

1. Fujian Medical University, Fuzhou 350004, China;
2. Department of Severe Liver Disease, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025,China

Received:2021-11-30 Online:2022-09-30 Published:2022-10-27
Contact: GAO Hai-bing,Email: gaohb605@163.com

摘要/Abstract

摘要： 目的探讨HBV相关慢加急性肝衰竭(HBV-ACLF)患者发生细菌感染的危险因素并构建预测模型。方法回顾性分析2015年1月至2018年12月福建医科大学孟超肝胆医院收治的255例HBV-ACLF患者的临床资料,包括患者确诊肝衰竭2 d内(基线)的临床资料及并发症,将住院期间发生细菌感染做为临床结局事件。采用R语言进行数据分析和列线图制作,用lasso回归和logistic回归筛选和分析危险因素,并构建预测模型;采用受试者工作特征曲线下面积(AUC)和校正曲线分析模型的预测效能并通过Bootstrap法对模型进行内部验证。结果 255例HBV-ACLF患者中,男198例,住院期间发生细菌感染203例(79.60%)。lasso回归分析以lambda=lambda.1 se(0.049)为标准,共筛选出年龄、基线直接胆红素(DBil)、胆碱酯酶(CHE)、凝血酶原时间(PT)、活化部分凝血活酶时间(APTT)、C-反应蛋白(CRP)和肝性脑病(HE)等7个变量为并发细菌感染的相关危险因素,并进一步构建logistic回归模型,回归方程为logistic(p)＝-7.1733 + 0.0495×AGE + 0.4107×ln(DBil)-0.0002×CHE + 0.0350×PT + 0.0610×APTT + 0.5212×ln(CRP) + 1.3582×(HE= 1 or 0),其中年龄(OR=1.05, 95% CI:1.02~1.09, P=0.0025)、DBil(OR=1.51, 95%CI:1.06~2.17, P=0.0230)、CRP(OR=1.68, 95%CI:1.11~2.62, P=0.0161)和HE(OR=3.88, 95% CI:1.37~14.10, P=0.0193)为独立危险因素。模型的特异度为84.62%,灵敏度为69.49%。新建预测模型的诊断效能(AUC=0.851)优于CRP(AUC=0.719)和降钙素原(AUC=0.655)。结论 HBV-ACLF患者的年龄越大、基线高DBil血症、低CHE水平、PT延长、APTT延长、CRP升高和发生HE,发生细菌感染风险高;根据这7项危险因素构建的模型对HBV-ACLF患者住院后并发细菌感染事件具有良好的预测效能。

关键词: 乙型肝炎病毒, 肝功能衰竭, 细菌感染, 预测模型

Abstract: Objective To investigate the risk factors of bacterial infection in patients with hepatitis B virus related acute-on-chronic liver failure (HBV-ACLF), and to construct a prediction model. Methods A total of 255 patients with HBV-ACLF admitted to our hospital from January 2015 to December 2018 were enrolled, and the clinical data during 2 days after diagnosis were retrospectively analyzed. Bacterial infection occurred during hospitalization was selected as the clinical outcome. The R programming language was used to analyze the data and construct the nomogram. Lasso regression and logistic regression were used to filter variables, analyze risk factors and construct the prediction model. Efficiency of the constructed model was evaluated by receiver operating characteristic (ROC) curve and calibration plot. The model was internally verified by Bootstrap method. Results Among 255 patients with HBV-ACLF, the proportion of male was 78%, and the incidence rate of bacterial infection during hospitalization was 79.60%. Lasso regression analysis Taking lambda = lambda. 1 se (0.049) as the standard, 7 variables including age, direct bilirubin (DBIL), cholinesterase (CHE), prothrombin time (PT), activated partial thromboplastin time (APTT), C-reactive protein (CRP) and hepatic encephalopathy (HE) were selected as the risk factors through Lasso regression analysis. The logistic regression model was logistic(p)＝-7.1733 + 0.0495 × AGE + 0.4107 × ln(DBIL)-0.0002 × CHE + 0.0350 × PT + 0.0610 × APTT + 0.5212 × ln(CRP) + 1.3582 × (HE=1 or 0). Among the 7 variables, AGE (OR=1.05, 95%CI 1.02-1.09), DBIL (OR=1.51, 95%CI 1.06-2.17), CRP (OR=1.68, 95%CI 1.11-2.62) and HE (OR=3.88, 95%CI 1.37-14.10) were independent risk factors. The specificity of the model was 84.62%, the sensitivity was 69.49%. ROC curve showed the new prediction model (AUC = 85.1%) was superior to CRP(AUC = 71.9%) and PCT (AUC = 65.5%). Conclusion In HBV-ACLF patients, older age, baseline hyper DBIL, low CHE level, prolonged PT, prolonged APTT, elevated CRP, and HE are positively correlated with the occurrence of bacterial infection. The model based on these 7 risk factors performs good in predicting the occurrence of bacterial infection during the hospitalization in patients with HBV-ACLF.

Key words: Hepatitis B virus, Liver failure, Bacterial infection, Prediction model

郑惠芳, 林升龙, 郑嵩, 黄雨欣, 肖珊颖, 叶子杰, 林明华, 高海兵. HBV相关慢加急性肝衰竭并发细菌感染的预测模型的构建[J]. 肝脏, 2022, 27(9): 966-972.

ZHENG Hui-fang, LIN Sheng-long, ZHENG Song, HUANG Yu-xin, XIAO Shan-ying, YE Zi-jie, LIN Ming-hua, GAO Hai-bing. Establishment of a prediction model for bacterial infection in patients with HBV related acute-on-chronic liver failure[J]. Chinese Hepatolgy, 2022, 27(9): 966-972.

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