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Published on: May 29, 2020
Develop and validate a novel online AIHI-nomogram to predict severe liver inflammation in patients with autoimmune
Zhiyi Zhang1, Jian Wang2, Huali Wang3
1Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China.
Introduction And Objectives:
Assessment of liver inflammation plays a vital role in the management of patients with autoimmune hepatitis (AIH). We aimed to establish and validate a nomogram to predict severe liver inflammation in AIH patients.
Patients And Methods:
AIH patients who underwent liver biopsy were included and randomly divided into a training set and a validation set. Independent predictors of severe liver inflammation were selected by the least absolute shrinkage and selection operator regression from the training set and used to conduct a nomogram. Receiver characteristic curves (ROC), calibration curves, and decision curve analysis (DCA) were adopted to evaluate the performance of nomogram.
Results:
Of the 213 patients, female patients accounted for 83.1% and the median age was 53.0 years. The albumin, gamma-glutamyl transpeptidase, total bilirubin, red cell distribution width, prothrombin time, and platelets were independent predictors of severe inflammation. An online AIHI-nomogram was established and was available at https://ndth-zzy.shinyapps.io/AIHI-nomogram/. The calibration curve revealed that the AIHI-nomogram had a good agreement with actual observation in the training and validation sets. The area under the ROCs of AIHI-nomogram were 0.795 in the training set and 0.759 in the validation set, showing significantly better performance than alanine aminotransferase and immunoglobulin G in the training and validation sets, as well in AIH patients with normal ALT in the training set. DCA indicated that the AIHI-nomogram was clinically useful.
Conclusions:
This novel AIHI-nomogram provided an excellent prediction of severe liver inflammation in AIH patients and could be used for the better management of AIH.
