A nomogram for analyzing risk factors of poor treatment response in patients with autoimmune hepatitis

Abstract

Objective The objective of this study was to identify biochemical and clinical predictors of poor response (including incomplete response and non-response) to standard treatment in autoimmune hepatitis (AIH) patients. Methods This study retrospectively collected clinical data from 297 patients who were first diagnosed with AIH in Beijing Ditan Hospital from 2010 to 2019. Finally, 149 patients were screened out. Risk factors were screened by univariate and multifactorial logistic regression. Then they were used to establish the nomogram. The ROC curve, calibration curve, decision curves analysis (DCA) and clinical impact curves (CIC) were used to evaluate the nomogram. Results 149 patients were divided into two groups: the response group (n = 120, 80%) and the poor response group (n = 29, 20%). Multivariate logistic regression analysis found that IgG > 26.5 g/L (OR: 22.016; 95% CI: 4.677–103.640) in AIH patients increased the risk. In contrast, treatment response status was better in women (OR: 0.085; 95% CI: 0.015–0.497) aged >60 years (OR: 0.159; 95% CI: 0.045–0.564) with AST > 4.49 × ULN (OR: 0.066; 95% CI: 0.009–0.494). The C index (0.853) and the calibration curve show that the nomogram is well differentiated and calibrated; the DCA and CIC indicate that the model has good clinical benefits and implications. Conclusion The study found that male patients aged ≤ 60 years with IgG > 26.5 g/L and elevated AST ≤ 4.49 × ULN were more likely to have a non-response/incomplete response to standard treatment.