Simplified criteria for the diagnosis of autoimmune hepatitis

Hepatology. 2008 Jul;48(1):169-76. doi: 10.1002/hep.22322.

Abstract

Diagnosis of autoimmune hepatitis (AIH) may be challenging. However, early diagnosis is important because immunosuppression is life-saving. Diagnostic criteria of the International Autoimmune Hepatitis Group (IAIHG) were complex and purely meant for scientific purposes. This study of the IAIHG aims to define simplified diagnostic criteria for routine clinical practice. Candidate criteria included sex, age, autoantibodies, immunoglobulins, absence of viral hepatitis, and histology. The training set included 250 AIH patients and 193 controls from 11 centers worldwide. Scores were built from variables showing predictive ability in univariate analysis. Diagnostic value of each score was assessed by the area under the receiver operating characteristic (ROC) curve. The best score was validated using data of an additional 109 AIH patients and 284 controls. This score included autoantibodies, immunoglobulin G, histology, and exclusion of viral hepatitis. The area under the curve for prediction of AIH was 0.946 in the training set and 0.91 in the validation set. Based on the ROC curves, two cutoff points were chosen. The score was found to have 88% sensitivity and 97% specificity (cutoff > or =6) and 81% sensitivity and 99% specificity (cutoff > or =7) in the validation set.

Conclusion: A reliable diagnosis of AIH can be made using a very simple diagnostic score. We propose the diagnosis of probable AIH at a cutoff point greater than 6 points and definite AIH 7 points or higher.

Publication types

  • Multicenter Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Area Under Curve
  • Cohort Studies
  • Diagnostic Techniques, Digestive System
  • Gastroenterology / methods
  • Hepatitis, Autoimmune / diagnosis*
  • Humans
  • International Cooperation
  • Middle Aged
  • Practice Guidelines as Topic
  • ROC Curve
  • Reproducibility of Results
  • Retrospective Studies
  • Sensitivity and Specificity