Influencing factors for the prognosis of patients with drug-induced liver injury and establishment of a nomogram model
- VernacularTitle:药物性肝损伤患者预后影响因素分析及列线图模型的建立
- Author:
Shimei WANG
1
;
Shuai JIN
2
;
Junru LI
1
;
Na WANG
3
;
Yan CHEN
1
;
Ying CUI
1
;
Mingming MA
3
;
Xiaoli HU
1
Author Information
- Publication Type:Journal Article
- Keywords: Chemical and Drug Induced Liver Injury; Prognosis; Nomograms
- From: Journal of Clinical Hepatology 2024;40(3):562-567
- CountryChina
- Language:Chinese
- Abstract: ObjectiveTo investigate the influencing factors for the clinical outcome of patients with drug-induced liver injury （DILI）， and to establish a nomogram prediction model for validation. MethodsA retrospective analysis was performed for the general information and laboratory data of 188 patients with DILI who were admitted to Heilongjiang Provincial Hospital Affiliated to Harbin Institute of Technology from January 2017 to December 2022， and according to their clinical outcome， they were divided into good outcome group with 146 patients and poor outcome group with 42 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 was used for comparison of categorical data between two groups. Univariate and multivariate Logistic regression analyses were used to investigate the independent influencing factors for the clinical outcome of DILI patients. R Studio 4.1.2 software was used to establish a nomogram model， and calibration curve， receiver operating characteristic （ROC） curve， and decision curve analysis （DCA） were used to perform internal validation. ResultsThe univariate Logistic regression analysis showed that liver biopsy for the diagnosis of DILI， platelet count， cholinesterase， albumin， prothrombin time activity， IgM， and IgG were associated with adverse outcomes in patients with DILI. The multivariate Logistic regression analysis showed that liver biopsy for the diagnosis of DILI （odds ratio ［OR］=0.072， 95% confidence interval ［CI］： 0.022 — 0.213， P<0.001）， clinical classification （OR=0.463， 95%CI： 0.213 — 0.926， P=0.039）， alanine aminotransferase （OR=0.999， 95%CI： 0.998 — 1.000， P=0.025）， prothrombin time activity （OR=0.973， 95%CI： 0.952 — 0.993， P=0.011）， and IgM （OR=1.456， 95%CI： 1.082 — 2.021， P=0.015） were independent influencing factors for clinical outcome in patients with DILI. The nomogram prediction model was established， and after validation， the calibration curve was close to the reference curve. The area under the ROC curve was 0.829， and the DCA curve showed that the model had good net clinical benefit. ConclusionThe nomogram prediction model established in this study has good clinical calibration， discriminative ability， and application value in evaluating the clinical outcome of patients with DILI.
