Risk factors for concurrent hepatic hydrothorax before intervention in primary liver cancer and construction of a nomogram prediction model
- VernacularTitle:原发性肝癌干预前并发肝性胸腔积液的危险因素及列线图预测模型构建
- Author:
Yuanzhen WANG
1
;
Renhai TIAN
1
;
Yingyuan ZHANG
1
;
Danqing XU
1
;
Lixian CHANG
1
;
Chunyun LIU
1
;
Li LIU
1
Author Information
- Publication Type:Journal Article
- Keywords: Liver Neoplasms; Pleural Effusion; Nomograms
- From: Journal of Clinical Hepatology 2025;41(1):75-83
- CountryChina
- Language:Chinese
- Abstract: ObjectiveTo investigate the influencing factors for hepatic hydrothorax （HH） before intervention for primary hepatic carcinoma （PHC）， and to construct and assess the nomogram risk prediction model. MethodsA retrospective analysis was performed for the clinical data of 353 hospitalized patients who attended the Third People’s Hospital of Kunming for the first time from October 2012 to October 2021 and there diagnosed with PHC， and according to the presence or absence of HH， they were divided into HH group with 153 patients and non-HH group with 200 patients. General data and the data of initial clinical testing after admission were collected from all PHC patients. 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 or the Fisher’s exact test was used for comparison of categorical data between groups. After the multicollinearity test was performed for the variables with statistical significance determined by the univariate analysis， the multivariate Logistic regression analysis was used to identify independent influencing factors. The “rms” software package was used to construct a nomogram risk prediction model， and the Hosmer-Lemeshow test and the receiver operating characteristic （ROC） curve were used to assess the risk prediction model； the “Calibration Curves” software package was used to plot the calibration curve， and the “rmda” software package was used to plot the clinical decision curve and the clinical impact curve. ResultsAmong the 353 patients with PHC， there were 153 patients with HH， with a prevalence rate of 43.34%. Child-Pugh class B （odds ratio ［OR］=2.652， 95% confidence interval ［CI］： 1.050 — 6.698， P=0.039）， Child-Pugh class C （OR=7.963， 95%CI： 1.046 — 60.632， P=0.045）， total protein （OR=0.947， 95%CI： 0.914 — 0.981， P=0.003）， high-sensitivity C-reactive protein （OR=1.007， 95%CI： 1.001 — 1.014， P=0.025）， and interleukin-2 （OR=0.801， 95%CI： 0.653 — 0.981， P=0.032） were independent influencing factors for HH before PHC intervention， and a nomogram risk prediction model was established based on these factors. The Hosmer-Lemeshow test showed that the model had a good degree of fitting （χ2=5.006， P=0.757）， with an area under the ROC curve of 0.752 （95%CI： 0.701 — 0.803）， a sensitivity of 78.40%， and a specificity of 63.50%. The calibration curve showed that the model had good consistency in predicting HH before PHC intervention， and the clinical decision curve and the clinical impact curve showed that the model had good clinical practicability within a certain threshold range. ConclusionChild-Pugh class， total protein， interleukin-2， and high-sensitivity C-reactive protein are independent influencing factors for developing HH before PHC intervention， and the nomogram model established based on these factors can effectively predict the risk of developing HH.
