A multivariate analysis of acute severe cholangitis and the establishment and evaluation of a risk prediction scoring model

Hongyu XIANG; Zheng DANG; Shulin XU; Gang NIU; Yuesheng LI; Baiwen MIAO; Yaoping PANG; Ruifang FAN; Jianwei QIN

doi:10.3969/j.issn.1001-5256.2022.08.024

Volume 38 Issue 8

Aug. 2022

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Article Navigation > Journal of Clinical Hepatology > 2022 > 38(8): 1847-1853

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A multivariate analysis of acute severe cholangitis and the establishment and evaluation of a risk prediction scoring model

DOI: 10.3969/j.issn.1001-5256.2022.08.024

1.
Department of Hepatobiliary Surgery, The 940th Hospital of Joint Logistics Support Force of Chinese People's Liberation Army, Lanzhou 730000, China
2.
Medical College of Northwest Minzu University, Lanzhou 730000, China

Research funding:

Applied Basic Research Prcject of Logistics Support Department of the Military Commission (20BJZ23)

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Corresponding author: QIN Jianwei, qinjianwei@163.com(ORCID: 0000-0001-7663-1894)
Received Date: 2021-12-05
Accepted Date: 2022-01-06
Published Date: 2022-08-20

Abstract

Abstract

Objective To investigate the independent risk factors for acute severe cholangitis and related protective factors, and to construct a risk prediction scoring model for acute severe cholangitis. Methods A retrospective analysis was performed for the clinical data of 381 patients with acute cholangitis who were admitted to Department of Hepatobiliary Surgery, The 940th Hospital of Joint Logistics Support Force of Chinese People's Liberation Army, from January 2016 to July 2021, among whom there were 273 patients with non-severe cholangitis and 108 patients with severe cholangitis. Univariate and multivariate logistic regression analyses were used to screen out the independent risk factors for acute severe cholangitis and related protective factors, and then a logistic regression model was established. The receiver operating characteristic (ROC) curve was used to evaluate the discriminatory ability of the model, the calibration curve was used to evaluate the prediction accuracy of the model, and decision curve analysis (DCA) was used to evaluate the clinical value of the model. Moreover, the enhanced Bootstrap method was used to perform internal validation of the model and evaluate the performance of the model in internal validation. The model was visualized by the construction of Web calculator, nomogram, and scoring system. The two-independent-samples t test was used for comparison of normally distributed continuous data between groups, and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups; the chi-square test was used for comparison of categorical data between groups. Results The univariate and multivariate logistic regression analyses showed that total bilirubin (TBil) (odds ratio [OR]=1.014, 95% confidence interval [CI]: 1.009-1.020, P < 0.001), percentage of neutrophils (OR=1.128, 95%CI: 1.088-1.175, P < 0.001), and age (OR=1.053, 95%CI: 1.027-1.082, P < 0.001) were independent risk factors, and albumin (Alb) (OR=0.871, 95%CI: 0.817-0.924, P < 0.001) was a protective factor. The above independent risk factors and protective factor were included in the logistic regression analysis for model fitting, and the predictive model obtained had an area under the ROC curve (AUC) of 0.925 (95%CI: 0.897-0.952), with a specificity of 0.817 and a sensitivity of 0.935 at the optimal cut-off value of 0.245. The calibration curve showed that the predicted probability of the model was approximately equal to the actual probability, with a Brier value of 0.098, and the decision curve analysis showed that the model had a higher net income within the threshold probability interval of 0.1-0.9. Internal validation showed an AUC_{internal validation} of 0.915 and a Brier value_{internal verification} of 0.106. Conclusion TBil, percentage of neutrophils, and age are independent risk factors for acute severe cholangitis, while Alb is a protective factor. The established risk prediction scoring model has good discriminatory ability, calibration, and clinical value and can identify patients with acute severe cholangitis at an early stage, which provides a reference for subsequent diagnosis and treatment.
- Cholangitis,
- Logistic Models,
- Risk Factors

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References(23)

References

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A multivariate analysis of acute severe cholangitis and the establishment and evaluation of a risk prediction scoring model

DOI: 10.3969/j.issn.1001-5256.2022.08.024