Risk factors for pyogenic liver abscess comorbid with sepsis and construction of a nomogram prediction model
- VernacularTitle:细菌性肝脓肿并发脓毒症的危险因素及列线图构建
- Author:
Jiayi GUO
1
;
Haiquan KANG
2
;
Mengjiao WANG
1
;
Deyang XI
1
;
Xuebing YAN
1
;
Chunyang LI
1
Author Information
- Publication Type:Journal Article
- Keywords: Liver Abscess; Sepsis; Risk Factors; Nomograms
- From: Journal of Clinical Hepatology 2025;41(6):1143-1149
- CountryChina
- Language:Chinese
- Abstract: ObjectiveTo investigate the risk factors for pyogenic liver abscess （PLA） comorbid with sepsis by analyzing clinical features， and to construct a predictive model. MethodsA 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. ResultsThere 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. ConclusionThe 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.
