Determining optimal ALT cut-off values for predicting significant hepatic histological changes in patients with normal ALT in the grey zone of chronic hepatitis B virus infection.
Journal
Alimentary pharmacology & therapeutics
ISSN: 1365-2036
Titre abrégé: Aliment Pharmacol Ther
Pays: England
ID NLM: 8707234
Informations de publication
Date de publication:
04 Jan 2024
04 Jan 2024
Historique:
revised:
04
10
2023
received:
09
08
2023
accepted:
20
12
2023
medline:
5
1
2024
pubmed:
5
1
2024
entrez:
5
1
2024
Statut:
aheadofprint
Résumé
We aimed to define gender-specific, optimal alanine aminotransferase (ALT) cut-off values for the prediction of significant liver histological changes (SLHC) in Chinese patients with grey zone (GZ) chronic hepatitis B (CHB) and normal ALT. In a retrospective study, we included 1101 consecutive patients with GZ CHB and normal ALT assigned to training or internal validation cohorts. We included an independent cohort of 842 patients for external validation. We performed receiver operating characteristic (ROC) curve, smoothed curve fitting, and threshold effect analyses to determine optimal ALT cut-off values. Area under the curve (AUC) values were calculated to assess their predictive performance. A proportion of 79.3% of patients with GZ CHB and normal ALT (≤40 U/L) had SLHC. ROC curve analysis initially identified optimal ALT cut-off values of 29 U/L (male) and 22 U/L (female). After smoothed curve fitting and threshold effect analyses, new optimal cut-off values were 27 U/L for males and 24 U/L for females. AUCs for these values were 0.836 (male) and 0.833 (female) in the internal validation cohort, and 0.849 (male) and 0.844 (female) in the external validation cohort. The accuracy and discriminative ability of the newly defined ALT cut-off values were greater than those of the current recommendations. This study established novel optimal ALT cut-off values for more precise prediction of SLHC among Chinese patients with GZ CHB and normal ALT levels. This may help identify individuals who will benefit from timely antiviral therapy.
Sections du résumé
BACKGROUND AND AIMS
OBJECTIVE
We aimed to define gender-specific, optimal alanine aminotransferase (ALT) cut-off values for the prediction of significant liver histological changes (SLHC) in Chinese patients with grey zone (GZ) chronic hepatitis B (CHB) and normal ALT.
METHODS
METHODS
In a retrospective study, we included 1101 consecutive patients with GZ CHB and normal ALT assigned to training or internal validation cohorts. We included an independent cohort of 842 patients for external validation. We performed receiver operating characteristic (ROC) curve, smoothed curve fitting, and threshold effect analyses to determine optimal ALT cut-off values. Area under the curve (AUC) values were calculated to assess their predictive performance.
RESULTS
RESULTS
A proportion of 79.3% of patients with GZ CHB and normal ALT (≤40 U/L) had SLHC. ROC curve analysis initially identified optimal ALT cut-off values of 29 U/L (male) and 22 U/L (female). After smoothed curve fitting and threshold effect analyses, new optimal cut-off values were 27 U/L for males and 24 U/L for females. AUCs for these values were 0.836 (male) and 0.833 (female) in the internal validation cohort, and 0.849 (male) and 0.844 (female) in the external validation cohort. The accuracy and discriminative ability of the newly defined ALT cut-off values were greater than those of the current recommendations.
CONCLUSION
CONCLUSIONS
This study established novel optimal ALT cut-off values for more precise prediction of SLHC among Chinese patients with GZ CHB and normal ALT levels. This may help identify individuals who will benefit from timely antiviral therapy.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Natural Science Fundation of Fujian Province
ID : 2023J01555
Organisme : Zhejiang Provincial Basic and Public Welfare Foundation under Grant
ID : LGF22H030002
Organisme : Clinical Research Center for Liver and Intestinal Diseases of Fujian Province
ID : 2021Y2006
Informations de copyright
© 2024 John Wiley & Sons Ltd.
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