肺炎克雷伯肝脓肿侵袭综合征的危险因素及预测模型构建
Risk Factors and Predictive Model Construction of Klebsiella pneumoniae Liver Abscess Invasion Syndrome
DOI: 10.12677/acm.2025.1541328, PDF,   
作者: 吕小菲, 马承泰*:青岛大学附属医院急诊科,山东 青岛;毛冰格:黑龙江省森工总医院重症医学科,黑龙江 哈尔滨
关键词: 肝脓肿肺炎克雷伯杆菌侵袭综合征预测模型列线图Liver Abscess Klebsiella pneumoniae Invasion Syndrome Prediction Model Nomogram
摘要: 目的:回顾性分析肺炎克雷伯菌肝脓肿综合征患者发生侵袭综合征的危险因素,构建和验证列线图预测模型,并进行相关验证。方法:检索收集青岛大学附属医院2017.12~2023.12期间第一诊断为肝脓肿且引流液培养为单一肺炎克雷伯杆菌的389例患者的病例资料,应用多因素logistic回归筛选肝外侵袭的独立预测因素构建列线图,绘制受试者工作特征(ROC)曲线对模型的预测效能进行评价,校准曲线对模型的校准度进行评价,临床决策曲线(DCA)评价模型的临床效益,Bootstrap法进行内部验证评估模型的稳定性和泛化能力。结果:本项研究总共纳入389例患者作为研究对象,根据肝外表现分为未侵袭组和侵袭组两组,其中未侵袭组332例,侵袭组57例,两组在年龄、性别之间均没有统计学差异。最终筛选出sofa评分(OR = 1.306, 95% CI: 1.136~1.507, P < 0.001)、中性粒细胞百分率(OR = 1.089, 95% CI: 1.029~1.152, P = 0.004)、空腹血糖(OR = 1.194, 95% CI: 1.086~1.311, P < 0.001)、白蛋白(OR = 0.901, 95% CI: 0.840~0.967, P = 0.004)为肝外侵袭的独立预测因素。纳入以上危险因素制作列线图(nomogram)并进行模型验证,ROC曲线示AUC = 0.840 (95% CI: 0.784~0.897),Hosmer-Lemeshow检验P = 0.66 > 0.05,校准曲线图示校准曲线与理想曲线贴合良好,临床决策曲线(DCA)图显示当阈值概率在0.01~0.99时模型均能产生更好的临床效益。结论:sofa评分、中性粒细胞百分率、空腹血糖、白蛋白是肺炎克雷伯菌肝脓肿患者发生侵袭综合征的独立危险因素,根据此指标可构建有效的肺炎克雷伯肝脓肿侵袭综合征的预测模型,具有一定的临床应用价值。
Abstract: Objective: To retrospectively analyze the risk factors of invasive syndrome in patients with Klebsiella pneumoniae liver abscess syndrome, construct and verify the nomogram prediction model, and carry out relevant verification. Method: The case data of 389 patients who were first diagnosed with liver abscess and whose drainage fluid was cultured as a single KP in the Affiliated Hospital of Qingdao University from 2017.12 to 2023.12 were collected. Multivariate logistic regression was used to screen the independent predictors of extrahepatic invasion to construct a nomogram. The receiver operating characteristic (ROC) curve was drawn to evaluate the predictive efficacy of the model, the calibration curve was used to evaluate the calibration of the model, the clinical decision curve (DCA) was used to evaluate the clinical benefits of the model, and the Bootstrap method was used to evaluate the stability and generalization ability of the model. Results: A total of 389 patients were included in this study. According to extrahepatic manifestations, they were divided into non-invasive group and invasive group, including 332 cases in non-invasive group and 57 cases in invasive group. There was no significant difference in age and sex between the two groups. Finally, sofa score (OR = 1.306, 95% CI: 1.136~1.507, P < 0.001), neutrophil percentage (OR = 1.089, 95% CI: 1.029~1.152, P = 0.004), fasting blood glucose (OR = 1.194, 95% CI: 1.086~1.311, P < 0.001), albumin (OR = 0.901, 95% CI: 0.840~0.967, P = 0.004) were independent predictors of extrahepatic invasion. The ROC curve showed AUC = 0.840 (95% CI: 0.784~0.897), Hosmer-Lemeshow test P = 0.66 > 0.05, the calibration curve diagram shows that the calibration curve fits well with the ideal curve, the visual calibration curve diagram shows that the predicted risk fits well with the actual risk represented by the standard line. The clinical decision curve (DCA) diagram shows that the model can produce better clinical benefits when the threshold probability is 0.01~0.99. Conclusion: Sofa score, neutrophil percentage, fasting blood glucose and albumin are independent risk factors for invasive syndrome in patients with Klebsiella pneumoniae liver abscess. According to this index, an effective predictive model for invasive syndrome of Klebsiella pneumoniae liver abscess can be constructed, which has certain clinical application value.
文章引用:吕小菲, 马承泰, 毛冰格. 肺炎克雷伯肝脓肿侵袭综合征的危险因素及预测模型构建[J]. 临床医学进展, 2025, 15(4): 3548-3562. https://doi.org/10.12677/acm.2025.1541328

参考文献

[1] Lardière-Deguelte, S., Ragot, E., Amroun, K., Piardi, T., Dokmak, S., Bruno, O., et al. (2015) Hepatic Abscess: Diagnosis and Management. Journal of Visceral Surgery, 152, 231-243. [Google Scholar] [CrossRef] [PubMed]
[2] Mukthinuthalapati, V.V.P.K., Attar, B.M., Parra-Rodriguez, L., Cabrera, N.L., Araujo, T. and Gandhi, S. (2019) Risk Factors, Management, and Outcomes of Pyogenic Liver Abscess in a US Safety Net Hospital. Digestive Diseases and Sciences, 65, 1529-1538. [Google Scholar] [CrossRef] [PubMed]
[3] Zhang, S., Zhang, X., Wu, Q., et al. (2019) Clinical, Microbiological, and Molecular Epidemiological Characteristics of Klebsiella pneumoniae-Induced Pyogenic Liver Abscess in Southeastern China. Antimicrobial Resistance & Infection Control, 29, Article No. 166. [Google Scholar] [CrossRef] [PubMed]
[4] Luo, M., Yang, X.-X., Tan, B., Zhou, X.-P., Xia, H.-M., Xue, J., et al. (2016) Distribution of Common Pathogens in Patients with Pyogenic Liver Abscess in China: A Meta-Analysis. European Journal of Clinical Microbiology & Infectious Diseases, 35, 1557-1565. [Google Scholar] [CrossRef] [PubMed]
[5] Siu, L.K., Yeh, K., Lin, J., Fung, C. and Chang, F. (2012) Klebsiella pneumoniae Liver Abscess: A New Invasive Syndrome. The Lancet Infectious Diseases, 12, 881-887. [Google Scholar] [CrossRef] [PubMed]
[6] 张自然, 孟凡征, 尹大龙, 等. 肺炎克雷伯菌性肝脓肿伴内源性眼内炎的诊断及治疗[J]. 中华肝脏外科手术学电子杂志, 2017, 6(6): 433-436.
[7] Lin, Y., Lu, M., Tang, H., Liu, H., Chen, C., Liu, K., et al. (2011) Assessment of Hypermucoviscosity as a Virulence Factor for Experimental Klebsiella pneumoniae Infections: Comparative Virulence Analysis with Hypermucoviscosity-Negative Strain. BMC Microbiology, 11, Article No. 50. [Google Scholar] [CrossRef] [PubMed]
[8] Baron, S.A., Pascale, L., Million, M., Briantais, A., Durand, J., Hadjadj, L., et al. (2020) Whole Genome Sequencing to Decipher the Virulence Phenotype of Hypervirulent Klebsiella pneumoniae Responsible for Liver Abscess, Marseille, France. European Journal of Clinical Microbiology & Infectious Diseases, 40, 1073-1077. [Google Scholar] [CrossRef] [PubMed]
[9] 中华医学会急诊医学分会. 细菌性肝脓肿诊治急诊专家共识[J]. 中华急诊医学杂志, 2022, 31(3): 273-280.
[10] 中华医学会糖尿病学分会. 中国2型糖尿病防治指南(2013年版) [J]. 中华糖尿病杂志, 2014, 6(7): 447-498.
[11] Singer, M., Deutschman, C.S., Seymour, C.W., Shankar-Hari, M., Annane, D., Bauer, M., et al. (2016) The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA, 315, 801-810. [Google Scholar] [CrossRef] [PubMed]
[12] Moreno, R., Vincent, J.-L., Matos, R., Mendonça, A., Cantraine, F., Thijs, L., et al. (1999) The Use of Maximum SOFA Score to Quantify Organ Dysfunction/Failure in Intensive Care. Results of a Prospective, Multicentre Study. Intensive Care Medicine, 25, 686-696. [Google Scholar] [CrossRef] [PubMed]
[13] Ferreira, F.L. (2001) Serial Evaluation of the SOFA Score to Predict Outcome in Critically Ill Patients. JAMA, 286, 1754-1758. [Google Scholar] [CrossRef] [PubMed]
[14] 刘江福, 何秀华, 陈泰裕, 等. 肺炎克雷伯菌肝脓肿致脓毒症的临床特征及危险因素分析[J]. 中国卫生标准管理, 2022, 13(17): 134-138.
[15] Li, S., Yu, S., Peng, M., et al. (2021) Clinical Features and Development of Sepsis in Klebsiella pneumoniae Infected Liver Abscess Patients: A Retrospective Analysis of 135 Cases. BMC Infectious Diseases, 21, Article No. 597. [Google Scholar] [CrossRef] [PubMed]
[16] 张碧莹, 路明, 林菲, 等. 细菌性肝脓肿并发脓毒症的临床特征[J]. 中国临床药理学杂志, 2023, 39(3): 307-311.
[17] Lin, J., Chang, F., Fung, C., Yeh, K., Chen, C., Tsai, Y., et al. (2010) Do Neutrophils Play a Role in Establishing Liver Abscesses and Distant Metastases Caused by Klebsiella pneumoniae? PLOS ONE, 5, e15005. [Google Scholar] [CrossRef] [PubMed]
[18] Park, K.S., Lee, S.H., Yun, S.J., Ryu, S. and Kim, K. (2018) Neutrophil-to-Lymphocyte Ratio as a Feasible Prognostic Marker for Pyogenic Liver Abscess in the Emergency Department. European Journal of Trauma and Emergency Surgery, 45, 343-351. [Google Scholar] [CrossRef] [PubMed]
[19] 蒲琴琴. 中性粒细胞/淋巴细胞比值与侵袭性肺炎克雷伯菌肝脓肿综合征的相关性分析[D]: [硕士学位论文]. 南京: 南京医科大学, 2023.
[20] Li, W., Chen, H., Wu, S. and Peng, J. (2018) A Comparison of Pyogenic Liver Abscess in Patients with or without Diabetes: A Retrospective Study of 246 Cases. BMC Gastroenterology, 18, Article No. 144. [Google Scholar] [CrossRef] [PubMed]
[21] Lin, Y., Wang, F., Wu, P. and Fung, C. (2013) Klebsiella pneumoniae Liver Abscess in Diabetic Patients: Association of Glycemic Control with the Clinical Characteristics. BMC Infectious Diseases, 13, Article No. 56. [Google Scholar] [CrossRef] [PubMed]
[22] Sheu, S., Kung, Y., Wu, T., Chang, F. and Horng, Y. (2011) Risk Factors for Endogenous Endophthalmitis Secondary to Klebsiella pneumoniae Liver Abscess. Retina, 31, 2026-2031. [Google Scholar] [CrossRef] [PubMed]
[23] Wang, H., Tsai, S., Yu, C., Hsu, H., Liu, C., Lin, J., et al. (2014) The Association of Haemoglobin A1C Levels with the Clinical and CT Characteristics of Klebsiella pneumoniae Liver Abscesses in Patients with Diabetes Mellitus. European Radiology, 24, 980-989. [Google Scholar] [CrossRef] [PubMed]
[24] Allison, K.R., Brynildsen, M.P. and Collins, J.J. (2011) Metabolite-Enabled Eradication of Bacterial Persisters by Aminoglycosides. Nature, 473, 216-220. [Google Scholar] [CrossRef] [PubMed]
[25] 丁蕊, 谢雯, 刘丽改, 等. 肺炎克雷伯菌肝脓肿的临床特征及预后影响因素分析[J]. 临床肝胆病杂志, 2022, 38(7): 1584-1589.
[26] Feng, C.Y., Zhang, L.W., Liu, T., et al. (2024) Establishment and Verification of Invasion Syndrome Prediction Model in Patients with Diabetes Complicated with Klebsiella pneumoniae Liver Abscess. National Medical Journal of China, 104, 956-962. [Google Scholar] [CrossRef] [PubMed]
[27] Feng, C., Di, J., Jiang, S., Li, X. and Hua, F. (2023) Machine Learning Models for Prediction of Invasion Klebsiella pneumoniae Liver Abscess Syndrome in Diabetes Mellitus: A Singled Centered Retrospective Study. BMC Infectious Diseases, 23, Article No. 284. [Google Scholar] [CrossRef] [PubMed]