细菌性肝脓肿病原菌分布及预后预测模型构建

doi:10.19871/j.cnki.xfcrbzz.2026.01.010

摘要/Abstract

摘要： 目的本研究旨在明确细菌性肝脓肿（pyogenic liver abscesses,PLA）的病原菌分布特征,并构建预测患者预后的风险模型,以期指导临床精准诊疗。方法选取2018年1月至2024年6月承德医学院附属医院收治的229例PLA患者作为训练集,2024年7月至2025年3月医院收治的115例PLA患者作为检验集,采集患者肝脓肿脓液样本进行病原菌鉴定。随访3个月统计训练集、检验集患者预后,根据训练集预后情况分为不良组55例、良好组157例。比较训练集、检验集临床资料;比较训练集中不良组、良好组临床资料及主要病原菌分布;以多因素Logistic回归模型分析PLA预后不良的影响因素,构建风险预测Nomogram模型;采用受试者操作特征曲线（receiver operating characteristic curve,ROC曲线）、校准曲线评价模型的效能。结果训练集PLA病原菌中革兰氏阴性菌、革兰氏阳性菌构成比分别为77.36%、22.64%;训练集、检验集预后不良率分别为25.94%、25.23%;训练集、检验集临床资料比较,差异无统计学意义（P>0.05）;训练集不良组年龄、脓肿最大直径大于良好组,合并糖尿病、低白蛋白血症的构成比、肺炎克雷伯杆菌的构成比及白细胞（white blood cell,WBC）计数、降钙素原（procalcitonin,PCT）水平、C反应蛋白（C-reactive protein,CRP）水平高于良好组,差异均有统计学意义（P<0.05）;多因素Logistic回归分析显示,年龄（OR=1.451,95%CI：1.013~2.080）、合并糖尿病（OR=2.276,95%CI：1.512~3.425）、合并低白蛋白血症（OR=1.971,95%CI：1.196~3.247）、PCT水平（OR=2.295,95%CI：1.413~3.728）、脓肿最大直径（OR=2.882,95%CI：1.507~5.512）、肺炎克雷伯杆菌感染（OR=1.770,95%CI：1.155~2.713）是PLA预后不良的独立危险因素（P<0.05）;ROC曲线显示,预后预测模型预测训练集PLA患者预后不良的曲线下面积（area under the curve,AUC）为0.927,敏感度、特异度分别为81.82%、91.08%,预测检验集PLA患者预后不良的AUC为0.913,敏感度、特异度分别为85.19%、93.75%;Hosmer-Lemeshow检验显示,该模型预测训练集、检验集预后不良的预测概率与实际概率的校准曲线比较,差异无统计学意义（χ²=0.174,P=0.676;χ²=0.205,P=0.603）。结论 PLA患者病原菌以革兰氏阴性菌为主,最常见为肺炎克雷伯杆菌、大肠埃希菌,该病预后不良的影响因素包括年龄、合并糖尿病、合并低白蛋白血症、PCT水平、脓肿最大直径、肺炎克雷伯杆菌感染,据此构建的风险预测Nomogram模型具有良好的临床效能。

关键词: 细菌性肝脓肿, 病原菌, 预后, 影响因素, 预测模型

Abstract: Objective To clarify the distribution characteristics of pathogenic bacteria in patients with pyogenic liver abscesses (PLA) and to construct a risk model for predicting patient prognosis, in order to guide precise clinical diagnosis and treatment. Method A total of 229 PLA patients admitted to the Affiliated Hospital of Chengde Medical University from January 2018 to June 2024 were selected as the training set, and 115 PLA patients admitted from July 2024 to March 2025 were selected as the validation set. Pus samples from liver abscesses were collected for pathogen identification. Patients in both sets were followed for 3 months to assess prognosis. Based on the prognosis in the training set, patients were divided into a poor prognosis group(55 cases) and a good prognosis group(157 cases). General characteristics were compared between the training and validation sets. General characteristics and the distribution of main pathogens were compared between the poor and good prognosis groups within the training set. Multivariate logistic regression analysis was used to identify influencing factors for poor prognosis in PLA and to construct a risk prediction nomogram model. The performance of the model was evaluated using the receiver operating characteristic (ROC) curve and calibration curves. Result In the training set, the composition ratios of Gram-negative bacteria and Gram-positive bacteria among PLA pathogens were 77.36% and 22.64%, respectively. The rates of poor prognosis in the training set and validation set were 25.94% and 25.23%, respectively. There was no statistically significant difference in general characteristics between the training and validation sets (P>0.05). In the training set, the poor prognosis group had older age, larger maximum abscess diameter, higher proportions of patients with comorbid diabetes, comorbid hypoalbuminemia, and Klebsiella pneumoniae infection, as well as higher white blood cell (WBC) count, procalcitonin (PCT) level, and C reactive protein (CRP) level compared to the good prognosis group. All these differences were statistically significant (P<0.05). Multivariate logistic regression analysis showed that increased age (OR=1.451, 95%CI: 1.013-2.080), comorbid diabetes (OR=2.276, 95%CI: 1.512-3.425), comorbid hypoalbuminemia (OR=1.971, 95%CI: 1.196-3.247), high PCT level (OR=2.295, 95%CI: 1.413-3.728), large maximum abscess diameter (OR=2.882, 95%CI: 1.507-5.512), and Klebsiella pneumoniae infection (OR=1.770, 95%CI: 1.155-2.713) were independent risk factors for poor prognosis in PLA (P<0.05). The ROC curve showed that the area under the curve (AUC) for predicting poor prognosis in the training set using the prognostic prediction model was 0.927, with a sensitivity of 81.82% and a specificity of 91.08%. For the validation set, the AUC was 0.913, with a sensitivity of 85.19% and a specificity of 93.75%. The Hosmer-Lemeshow test indicated no statistically significant difference between the predicted probability and the actual probability of poor prognosis for both the training set (χ²=0.174, P=0.676) and the validation set (χ²=0.205, P=0.603) in the calibration curves. Conclusion The main pathogens in PLA patients are Gram-negative bacteria, with Klebsiella pneumoniae and Escherichia coli being the most common. Factors influencing poor prognosis include increased age, comorbid diabetes, comorbid hypoalbuminemia, high PCT level, large maximum abscess diameter, and Klebsiella pneumoniae infection. The risk prediction nomogram model constructed based on these factors demonstrates good clinical performance.

Key words: Pyogenic liver abscess, Pathogen distribution, Prognosis, Risk factors, Prediction model

中图分类号:

R575.4

张竹青, 杜云玲, 宗春光, 闫妹姝, 张薇雯, 陈凯. 细菌性肝脓肿病原菌分布及预后预测模型构建[J]. 新发传染病电子杂志, 2026, 11(1): 60-66.

Zhang Zhuqing, Du Yunling, Zong Chunguang, Yan Meishu, Zhang Weiwen, Chen Kai. Distribution of pathogenic bacteria in pyogenic liver abscess and construction of a prognostic prediction model[J]. Electronic Journal of Emerging Infectious Diseases, 2026, 11(1): 60-66.

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