Prediction of biochemical nonresolution in patients with chronic drug-induced liver injury: A large multicenter study
- PMID: 34919746
- DOI: 10.1002/hep.32283
Prediction of biochemical nonresolution in patients with chronic drug-induced liver injury: A large multicenter study
Abstract
Background and aims: To clarify high-risk factors and develop a nomogram model to predict biochemical resolution or biochemical nonresolution (BNR) in patients with chronic DILI.
Approach and results: Retrospectively, 3655 of 5326 patients with chronic DILI were enrolled from nine participating hospitals, of whom 2866 underwent liver biopsy. All of these patients were followed up for over 1 year and their clinical characteristics were retrieved from electronic medical records. The endpoint was BNR, defined as alanine aminotransferase or aspartate aminotransferase >1.5× upper limit of normal or alkaline phosphatase >1.1× ULN, at 12 months from chronic DILI diagnosis. The noninvasive high-risk factors for BNR identified by multivariable logistic regression were used to establish a nomogram, which was validated in an independent external cohort. Finally, 19.3% (707 of 3655) patients presented with BNR. Histologically, with the increase in liver inflammation grades and fibrosis stages, the proportion of BNR significantly increased. The risk of BNR was increased by 21.3-fold in patients with significant inflammation compared to none or mild inflammation (p < 0.001). Biochemically, aspartate aminotransferase and total bilirubin, platelets, prothrombin time, sex, and age were associated with BNR and incorporated to construct a nomogram model (BNR-6) with a concordance index of 0.824 (95% CI, 0.798-0.849), which was highly consistent with liver histology. These results were successfully validated both in the internal cohort and external cohort.
Conclusions: Significant liver inflammation is a robust predictor associated with biochemical nonresolution. The established BNR-6 model provides an easy-to-use approach to assess the outcome of chronic DILI.
© 2021 American Association for the Study of Liver Diseases.
Comment in
-
Letter to the editor: Selection of appropriate statistical methods for prediction model.Hepatology. 2022 May;75(5):1348-1349. doi: 10.1002/hep.32371. Epub 2022 Feb 18. Hepatology. 2022. PMID: 35092078 No abstract available.
-
Reply.Hepatology. 2022 May;75(5):1349-1351. doi: 10.1002/hep.32372. Epub 2022 Feb 20. Hepatology. 2022. PMID: 35098558 No abstract available.
-
Letter to the editor: Prediction of biochemical nonresolution in patients with chronic drug-induced liver injury: A large multicenter study.Hepatology. 2022 Jul;76(1):E13-E14. doi: 10.1002/hep.32389. Epub 2022 Mar 6. Hepatology. 2022. PMID: 35112375 No abstract available.
References
-
- Weaver RJ, Blomme EA, Chadwick AE, Copple IM, Gerets HHJ, Goldring CE, et al. Managing the challenge of drug‐induced liver injury: a roadmap for the development and deployment of preclinical predictive models. Nat Rev Drug Discov. 2020;19:131–48.
-
- Wang Q, Huang A, Wang JB, Zou Z. Chronic drug‐induced liver injury: updates and future challenges. Front Pharmacol. 2021;12:627133.
-
- Kullak‐Ublick GA, Andrade RJ, Merz M, End P, Benesic A, Gerbes AL, et al. Drug‐induced liver injury: recent advances in diagnosis and risk assessment. Gut. 2017;66:1154–64.
-
- Li Z‐B, Chen D‐D, He Q‐J, Li LE, Zhou G, Fu Y‐M, et al. The LAC score indicates significant fibrosis in patients with chronic drug‐induced liver injury: a large biopsy‐based study. Front Pharmacol. 2021;12:734090.
-
- Shen T, Liu Y, Shang J, Xie Q, Li J, Yan M, et al. Incidence and etiology of drug‐induced liver injury in mainland China. Gastroenterology. 2019;156:2230–41.
Publication types
MeSH terms
Substances
LinkOut - more resources
Full Text Sources
Medical