Analysis of prognostic factors and construction of a logistic regression model for patients with drug-induced liver failure.
10.11817/j.issn.1672-7347.2018.12.009
- Author:
Jiebin ZHOU
1
,
2
;
Qian LI
1
,
2
;
Guozhong GONG
3
;
Huanyu GONG
4
;
Zhouhua HOU
2
,
5
Author Information
1. Department of Infectious Diseases, Xiangya Hospital, Central South University
2. Hunan Province Key Laboratory of Viral Hepatitis, Changsha 410008, China.
3. Department of Infectious Diseases, Second Xiangya Hospital, Central South University, Changsha 410011, China.
4. Department of Infectious Diseases, Th ird Xiangya Hospital, Central South University, Changsha 410013, China.
5. DDepartment of Infectious Diseases, Xiangya Hospital, Central South University
- Publication Type:Journal Article
- MeSH:
China;
Humans;
Liver Failure;
chemically induced;
diagnosis;
Logistic Models;
Predictive Value of Tests;
Prognosis;
ROC Curve;
Retrospective Studies;
Severity of Illness Index
- From:
Journal of Central South University(Medical Sciences)
2018;43(12):1337-1344
- CountryChina
- Language:Chinese
-
Abstract:
To explore the prognostic factors for patients with drug-induced liver failure (DILF) and construct a logistic regression model (LRM).
Methods: A retrospective analysis of clinical data was performed in 183 hospitalized patients, who were diagnosed with DILF in Xiangya Hospital, the Second Xiangya Hospital and the Third Xiangya Hospital, Central South University from January 2009 to January 2018. The patients were divided into an improved group (n=67) and an ineffective group (n=116) according to their prognosis. Univariate analysis was performed to screen for possible prognostic factors such as age, Tbil, SCr, PT and complications. According to the results of univariate analysis, the multivariate analysis was performed to determine the independent prognostic factors and construct a LRM. The LRM was compared with the model for end-stage liver disease (MELD), the predictive value of LRM and MELD was evaluated by receiver operating characteristic curve (ROC), the parameters such as area under the ROC (AUC) and total accuracy were compared between the 2 models and verified by another independent sample.
Results: According to univariate analysis, there was significant differences in age, clinical type, hepatic encephalopathy, hepatorenal syndrome, WBC count, the ratio of aspartic acid transaminase (AST) to glutamine transaminase (ALT) (AST/ALT), Tbil, SCr, PT and alpha-fetoprotein (AFP) between the 2 groups (all P<0.05). Multivariate analysis revealed that: AFP, PT, AST/ALT, hepatic encephalopathy and hepatorenal syndrome were independent prognostic factors for DILF, which could be applied to constructing a LRM. The AUC of LRM and MELD was 0.917 (95% CI 0.876 to 0.959) and 0.709 (95% CI 0.633 to 0.786) respectively, the total accuracy rate of prediction for the LRM and the MELD was 86.7% and 68.3% respectively, there was significant difference in AUC and total accuracy rate between the LRM and the MELD (P<0.05). LRM was superior to MELD.
Conclusion: AFP, PT, AST/ALT, hepatic encephalopathy and hepatorenal syndrome were independent prognostic factors for DILF; the LRM can well predict the prognosis in the DILF patients, which is superior to the MELD.
