化脓性肝脓肿患者肺炎克雷伯菌感染的影响因素及预测模型

Influencing factors and prediction model of Klebsiella pneumoniae infection in patients with pyogenic liver abscess

摘要

目的 分析化脓性肝脓肿(pyogenic liver abscess, PLA)患者肺炎克雷伯菌(Klebsiella pneumoniae, KP)感染的影响因素,构建并验证KP感染的早期预测工具。 方法 回顾性分析2016年12月至2025年10月在山东大学齐鲁医院(青岛)收治的287例PLA患者的临床、实验室及影像学资料,采用MICE多重插补处理缺失数据,Lasso回归筛选特征变量后构建logistic回归模型,通过ROC曲线、校准曲线、Hosmer-Lemeshow检验及5折交叉验证评估模型性能,并进行敏感性分析。 结果 logistic回归分析显示,糖尿病史及较高CRP水平是KP感染的正相关因子,年龄较大、高NEU水平、腹部手术史为负相关因子,模型AUC为0.837,敏感度约为0.807,特异度约为0.767,关键预测变量在不同敏感性分析中效应方向稳定。 结论 糖尿病史、CRP水平、年龄、NEU水平、腹部手术史是PLA患者KP感染的相关影响因素,基于此构建的预测模型具备一定鉴别与校准能力,可为经验性抗菌治疗提供参考,但未来仍需多中心、大样本及多模态数据进一步验证与优化。

Abstract

Objective To analyze the factors associated with Klebsiella pneumoniae(KP)infection in patients with pyogenic liver abscess(PLA)and to develop and validate an early prediction tool for KP infection. Methods The clinical, laboratory, and imaging data of 287 patients with PLA who were admitted to Qilu Hospital of Shandong University(Qingdao)from December 2016 to October 2025 were retrospectively analyzed. Missing data were handled using multiple imputation by chained equations(MICE). Candidate variables were screened using Lasso regression, and a logistic regression model was subsequently constructed. Model performance was evaluated using receiver operating characteristic(ROC)curves, calibration curves, the Hosmer-Lemeshow test, and 5-fold cross-validation. Sensitivity analyses were also performed. Results Logistic regression analysis showed that a history of diabetes and higher CRP levels were positively associated with KP infection, whereas older age, higher neutrophil(NEU)levels, and a history of abdominal surgery were negatively associated with KP infection. The model achieved an area under the curve(AUC)of 0.837, with a sensitivity of 0.807 and a specificity of 0.767. The directions of association for the key predictors remained stable across different sensitivity analyses. Conclusion History of diabetes, CRP level, age, NEU level, and history of abdominal surgery are associated factors for KP infection in patients with PLA. The predictive model constructed based on these variables shows acceptable discrimination and calibration abilities, and may provide a reference for empirical antimicrobial therapy. However, further validation and optimization using multicenter, larger-sample, and multimodal data are still needed.

关键词

肝脓肿 / 肺炎克雷伯菌 / 糖尿病 / 预测模型 / 影响因素

Key words

Liver abscess / Klebsiella pneumoniae / Diabetes mellitus / Prediction model / Influencing factors

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