Risk factors for pyogenic liver abscess comorbid with sepsis and construction of a nomogram prediction model

Jiayi GUO; Haiquan KANG; Mengjiao WANG; Deyang XI; Xuebing YAN; Chunyang LI

doi:10.12449/JCH250621

Volume 41 Issue 6

Jun. 2025

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Article Navigation > Journal of Clinical Hepatology > 2025 > 41(6): 1143-1149

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Risk factors for pyogenic liver abscess comorbid with sepsis and construction of a nomogram prediction model

DOI: 10.12449/JCH250621

a.
Department of Infectious Diseases, The Affiliated Hospital of Xuzhou Medical University，Xuzhou 221000，Jiangsu，China
b.
Department of Clinical Laboratory, The Affiliated Hospital of Xuzhou Medical University，Xuzhou 221000，Jiangsu，China

Research funding:

National Science and Technology Key Project (2018ZX10302206-003-010);

Scientific Research Program of Jiangsu Provincial Healthcare Commission (Z2021009);

Scientific Research Program of Jiangsu Provincial Healthcare Commission (Ym2023110);

Jiangsu Postgraduate Research and Practice Innovation Program (KYCX24_3067)

More Information

Corresponding author: LI Chunyang， 13775989791@126.com （ORCID： 0009-0006-9200-6208）
Received Date: 2024-11-11
Accepted Date: 2025-01-09
Published Date: 2025-06-25

Abstract

Abstract

Objective To investigate the risk factors for pyogenic liver abscess （PLA） comorbid with sepsis by analyzing clinical features， and to construct a predictive model. Methods A retrospective analysis was performed for 489 patients who were hospitalized and diagnosed with PLA in The Affiliated Hospital of Xuzhou Medical University from January 2019 to December 2023， and according to the presence or absence of sepsis， they were divided into sepsis group with 306 patients and non-sepsis group with 183 patients. Related data were collected， including general information， laboratory markers， and outcome measures. The patients were further divided into a training set of 342 patients and a validation set of 147 patients at a ratio of 7∶3， and the training set was used for screening of variables and construction of a predictive model， while the validation set was used to test the performance of the model. An LASSO regression analysis was used for the screening of variables， and a multivariate Logistic regression analysis was used to construct the predictive model and plot a nomogram. The calibration curve， the receiver operating characteristic （ROC） curve， and the decision curve analysis were used for the validation of the model， and internal validation was performed for assessment. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test was used for comparison of categorical variables between groups. Results There were significant differences between the sepsis group and the non-sepsis group in pulse rate， mean arterial pressure， duration pf symptoms， comorbidity of liver cirrhosis or malignant tumor， leukocyte count， neutrophil count， lymphocyte count， platelet count （PLT）， activated partial thromboplastin time， fibrinogen， C-reactive protein， aspartate aminotransferase， alanine aminotransferase， albumin， total bilirubin （TBil）， creatinine， potassium， and prognostic nutritional index （PNI）（all P<0.05）. In the training set， the LASSO regression analysis identified four predictive factors of pulse rate， PLT， TBil and PNI， and the multivariate Logistic regression analysis showed that pulse rate （odds ratio ［OR］=1.033， 95% confidence interval ［CI］： 1.006‍ ‍—‍ ‍1.061， P=0.018）， PLT （OR=0.981， 95%CI： 0.975‍ ‍—‍ ‍0.987， P<0.001）， TBil （OR=1.086， 95%CI： 1.053‍ ‍—‍ ‍1.125， P<0.001）， and PNI （OR=0.935， 95%CI： 0.882‍ ‍—‍ ‍0.988， P=0.019） were independent influencing factors for the risk of sepsis in patients with PLA. The model constructed based on these factors showed a good predictive ability， with an area under the ROC curve of 0.948 （95%CI： 0.923‍ ‍—‍ ‍0.973） in the training set and 0.912 （95%CI： 0.848‍ ‍—‍ ‍0.976） in the validation set. The decision curve analysis showed that the model has a good net benefit within the range of 0.3‍ ‍—‍ ‍0.9 for threshold probability. Conclusion The nomogram prediction model constructed based on pulse rate， PLT， TBil， and PNI has a certain clinical value and can well predict the risk of sepsis in patients with PLA.
- Liver Abscess,
- Sepsis,
- Risk Factors,
- Nomograms

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References(27)

References

[1]	KHIM G, EM S, MO S, et al. Liver abscess: Diagnostic and management issues found in the low resource setting[J]. Br Med Bull, 2019, 132( 1): 45- 52. DOI: 10.1093/bmb/ldz032.
[2]	Emergency Medicine Branch of the Chinese Medical Association. Emergency expert consensus on diagnosis and treatment of bacterial liver abscess[J]. Chin J Emerg Med, 2022, 31( 3): 273- 280. DOI: 10.3760/cma.j.issn.1671-0282.2022.03.003. 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. DOI: 10.3760/cma.j.issn.1671-0282.2022.03.003.
[3]	ZHANG J, GAO Y, DU ZQ, et al. Clinical features and prognosis of gas-forming and non-gas-forming pyogenic liver abscess: A comparative study[J]. Surg Infect(Larchmt), 2021, 22( 4): 427- 433. DOI: 10.1089/sur.2020.245.
[4]	SEYMOUR CW, LIU VX, IWASHYNA TJ, et al. Assessment of clinical criteria for sepsis: For the third international consensus definitions for sepsis and septic shock(sepsis-3)[J]. JAMA, 2016, 315( 8): 762- 774. DOI: 10.1001/jama.2016.0288.
[5]	EVANS L, RHODES A, ALHAZZANI W, et al. Surviving sepsis campaign: International guidelines for management of sepsis and septic shock 2021[J]. Crit Care Med, 2021, 49( 11): e1063- e1143. DOI: 10.1097/ccm.0000000000005337.
[6]	VILLANUEVA DM, TAUNK P, KRISHNAN P, et al. Emerging multiorgan Klebsiella pneumoniae invasive syndrome leading to septic shock: A case report and review of the literature[J]. Cureus, 2022, 14( 7): e26647. DOI: 10.7759/cureus.26647.
[7]	HELMS J, CATOIRE P, ABENSUR VUILLAUME L, et al. Oxygen therapy in acute hypoxemic respiratory failure: Guidelines from the SRLF-SFMU consensus conference[J]. Ann Intensive Care, 2024, 14( 1): 140. DOI: 10.1186/s13613-024-01367-2.
[8]	Liver Failure and Artificial Liver Group, Chinese Society of Infectious Diseases, Chinese Medical Association; Severe Liver Disease and Artificial Liver Group, Chinese Society of Hepatology, Chinese Medical Association. Guideline for diagnosis and treatment of liver failure(2018)[J]. J Clin Hepatol, 2019, 35( 1): 38- 44. DOI: 10.3969/j.issn.1001-5256.2019.01.007. 中华医学会感染病学分会肝衰竭与人工肝学组, 中华医学会肝病学分会重型肝病与人工肝学组. 肝衰竭诊治指南(2018年版)[J]. 临床肝胆病杂志, 2019, 35( 1): 38- 44. DOI: 10.3969/j.issn.1001-5256.2019.01.007.
[9]	KELLUM JA, ROMAGNANI P, ASHUNTANTANG G, et al. Acute kidney injury[J]. Nat Rev Dis Primers, 2021, 7( 1): 52. DOI: 10.1038/s41572-021-00284-z.
[10]	CHO H, LEE ES, LEE YS, et al. Predictors of septic shock in initially stable patients with pyogenic liver abscess[J]. Scand J Gastroenterol, 2017, 52( 5): 589- 594. DOI: 10.1080/00365521.2017.1288757.
[11]	HE MY, WANG CM, ZHAI WL, et al. Study the influence factors of treatment prognosis in septic shock patients[J]. Clin J Med Offic, 2023, 51( 6): 570- 573, 578. DOI: 10.16680/j.1671-3826.2023.06.06. 贺明轶, 王春梅, 翟文亮, 等. 脓毒症休克患者预后影响因素研究[J]. 临床军医杂志, 2023, 51( 6): 570- 573, 578. DOI: 10.16680/j.1671-3826.2023.06.06.
[12]	HU XD, BA XT, ZHU P. Risk factors and prognosis analysis of gastrointestinal bleeding secondary to sepsis[J]. J Clin Exp Med, 2023, 22( 19): 2032- 2035. DOI: 10.3969/j.issn.1671-4695.2023.19.005. 胡向党, 巴晓彤, 朱萍. 脓毒症继发消化道出血危险因素及预后分析[J]. 临床和实验医学杂志, 2023, 22( 19): 2032- 2035. DOI: 10.3969/j.issn.1671-4695.2023.19.005.
[13]	LI ZB, LI QC, WU QP, et al. Analysis of risk factors for sepsis caused by liver abscess and establishment and verification of prediction model based on PNI and SII[J]. J Hainan Med Univ, 2024, 30( 12): 930- 938. DOI: 10.13210/j.cnki.jhmu.20240307.002. 李泽标, 李秋呈, 吴秋萍, 等. 肝脓肿致脓毒症的危险因素分析及基于PNI、SII预测模型的建立与验证[J]. 海南医学院学报, 2024, 30( 12): 930- 938. DOI: 10.13210/j.cnki.jhmu.20240307.002.
[14]	LI J, WANG Y, LUO JH, et al. Development and validation of a nomogram for predicting sepsis in patients with pyogenic liver abscess[J]. Sci Rep, 2023, 13( 1): 10849. DOI: 10.1038/s41598-023-37907-2.
[15]	LIU F, YAO J, LIU CY, et al. Construction and validation of machine learning models for sepsis prediction in patients with acute pancreatitis[J]. BMC Surg, 2023, 23( 1): 267. DOI: 10.1186/s12893-023-02151-y.
[16]	COX D. Sepsis-it is all about the platelets[J]. Front Immunol, 2023, 14: 1210219. DOI: 10.3389/fimmu.2023.1210219.
[17]	LI Y, ZOU ZP, ZHANG YL, et al. Dynamics in perioperative neutrophil-to-lymphocyte*platelet ratio as a predictor of early acute kidney injury following cardiovascular surgery[J]. Ren Fail, 2021, 43( 1): 1012- 1019. DOI: 10.1080/0886022X.2021.1937220.
[18]	MEYER J, LEJMI E, FONTANA P, et al. A focus on the role of platelets in liver regeneration: Do platelet-endothelial cell interactions initiate the regenerative process?[J]. J Hepatol, 2015, 63( 5): 1263- 1271. DOI: 10.1016/j.jhep.2015.07.002.
[19]	GRECO E, LUPIA E, BOSCO O, et al. Platelets and multi-organ failure in sepsis[J]. Int J Mol Sci, 2017, 18( 10): 2200. DOI: 10.3390/ijms18102200.
[20]	CZERWONKO ME, HUESPE P, BERTONE S, et al. Pyogenic liver abscess: Current status and predictive factors for recurrence and mortality of first episodes[J]. HPB(Oxford), 2016, 18( 12): 1023- 1030. DOI: 10.1016/j.hpb.2016.09.001.
[21]	ZHANG SY, CHEN YX. Progress in diagnosis and treatment of pyogenic liver abscess[J]. J Clin Hepatol, 2018, 34( 7): 1577- 1580. DOI: 10.3969/j.issn.1001-5256.2018.07.046. 章顺轶, 陈岳祥. 细菌性肝脓肿诊治进展[J]. 临床肝胆病杂志, 2018, 34( 7): 1577- 1580. DOI: 10.3969/j.issn.1001-5256.2018.07.046.
[22]	XIE TH, XIN Q, CHEN R, et al. Clinical value of prognostic nutritional index and neutrophil-to-lymphocyte ratio in prediction of the development of sepsis-induced kidney injury[J]. Dis Markers, 2022, 2022: 1449758. DOI: 10.1155/2022/1449758.
[23]	CHEN L, BAI P, KONG XY, et al. Prognostic nutritional index(PNI) in patients with breast cancer treated with neoadjuvant chemotherapy as a useful prognostic indicator[J]. Front Cell Dev Biol, 2021, 9: 656741. DOI: 10.3389/fcell.2021.656741.
[24]	ZHANG JL, CHEN Y, ZOU L, et al. Prognostic nutritional index as a risk factor for diabetic kidney disease and mortality in patients with type 2 diabetes mellitus[J]. Acta Diabetol, 2023, 60( 2): 235- 245. DOI: 10.1007/s00592-022-01985-x.
[25]	GIRARDOT T, RIMMELÉ T, VENET F, et al. Apoptosis-induced lymphopenia in sepsis and other severe injuries[J]. Apoptosis, 2017, 22( 2): 295- 305. DOI: 10.1007/s10495-016-1325-3.
[26]	XU J, ZHOU XH, ZHENG C. The geriatric nutritional risk index independently predicts adverse outcomes in patients with pyogenic liver abscess[J]. BMC Geriatr, 2019, 19( 1): 14. DOI: 10.1186/s12877-019-1030-5.
[27]	DING R, XIE W, LIU LG, et al. Clinical features of Klebsiella pneumoniae liver abscess and influencing factors for prognosis[J]. J Clin Hepatol, 2022, 38( 7): 1584- 1589. DOI: 10.3969/j.issn.1001-5256.2022.07.023. 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. DOI: 10.3969/j.issn.1001-5256.2022.07.023.

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Risk factors for pyogenic liver abscess comorbid with sepsis and construction of a nomogram prediction model

DOI: 10.12449/JCH250621

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