Development and validation of a noninvasive prediction model of autoimmune hepatitis in patients with liver diseases.
Scand J Gastroenterol
; 59(1): 62-69, 2024.
Article
in En
| MEDLINE
| ID: mdl-37649307
ABSTRACT
BACKGROUND AND AIMS:
There is no golden standard for the diagnosis of autoimmune hepatitis which still dependent on liver biopsy currently. So, we developed a noninvasive prediction model to help optimize the diagnosis of autoimmune hepatitis.METHODS:
From January 2017 to December 2019, 1739 patients who had undergone liver biopsy were seen in the second hospital of Nanjing, of which 128 were here for consultation. Clinical, laboratory, and histologic data were obtained retrospectively. Multivariable logistic regression analysis was employed to create a nomogram model that predicting the risk of autoimmune hepatitis. Internal and external validation was both performed to evaluate the model.RESULTS:
A total of 1288 patients with liver biopsy were enrolled (1184 from the second hospital of Nanjing, the remaining 104 from other centers). After the univariate and multivariate logistic regression analysis, nine variables including ALT, IgG, ALP/AST, ALB, ANA, AMA, HBsAg, age, and gender were selected to establish the noninvasive prediction model. The nomogram model exhibits good prediction in diagnosing autoimmune hepatitis with AUROC of 0.967 (95% CI 0.776-0.891) in internal validation and 0.835 (95% CI 0.752-0.919) in external validation.CONCLUSIONS:
ALT, IgG, ALP/AST, ALB, ANA, AMA, HBsAg, age, and gender are predictive factors for the diagnosis of autoimmune hepatitis in patients with unexplained liver diseases. The predictive nomogram model built by the nine predictors achieved good prediction for diagnosing autoimmune hepatitis.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Hepatitis, Autoimmune
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Scand J Gastroenterol
Year:
2024
Document type:
Article
Affiliation country: