Noninvasive diagnosis of AIH/PBC overlap syndrome based on prediction models

Kailing Wang; Yong Li; Jianfeng Pan; Huifang He; Ziyi Zhao; Yiming Guo; Xiaomei Zhang

doi:10.1515/med-2022-0526

Noninvasive diagnosis of AIH/PBC overlap syndrome based on prediction models

Open Med (Wars). 2022 Sep 28;17(1):1550-1558. doi: 10.1515/med-2022-0526. eCollection 2022.

Authors

Kailing Wang¹, Yong Li¹, Jianfeng Pan¹, Huifang He¹, Ziyi Zhao¹, Yiming Guo¹, Xiaomei Zhang^{1

2}

Affiliations

¹ Department of Gastroenterology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.
² National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan 410007, China.

Abstract

Autoimmune liver diseases (AILDs) are life-threatening chronic liver diseases, mainly including autoimmune hepatitis (AIH), primary biliary cholangitis (PBC), and AIH-PBC overlap syndrome (OS), which are difficult to distinguish clinically at early stages. This study aimed to establish model to achieve the purpose of the diagnosis of AIH/PBC OS in a noninvasive way. A total of 201 AILDs patients were included in this retrospective study who underwent liver biopsy during January 2011 to December 2020. Serological factors significantly associated with OS were determined by the univariate analysis. Two multivariate models based on these factors were constructed to predict the diagnosis of AIH/PBC OS using logistic regression and random forest analysis. The results showed that immunoglobulins G and M had significant importance in both models. In logistic regression model, anti-Sp100, anti-Ro-52, anti-SSA, or antinuclear antibody positivity were risk factors for OS. In random forest model, activated partial thromboplastin time and ɑ-fetoprotein level were important. To distinguish PBC and OS, the sensitivity and specificity of logistic regression model were 0.889 and 0.727, respectively, and the sensitivity and specificity of random forest model were 0.944 and 0.818, respectively. In conclusion, we established two predictive models for the diagnosis of AIH/PBC OS in a noninvasive method and they showed better performance than Paris criteria for the definition of AIH/PBC OS.

Keywords: immunoglobulin; predictive model; primary biliary cirrhosis.