Pediatr Gastroenterol Hepatol Nutr.  2018 Apr;21(2):118-126. 10.5223/pghn.2018.21.2.118.

Accuracy of the 2008 Simplified Criteria for the Diagnosis of Autoimmune Hepatitis in Children

• Arcos-Machancoses JV
• Molera Busoms C
• Julio Tatis E
• Victoria Bovo M
• Quintero Bernabeu J
• Juampérez Goñi J
• Crujeiras Martínez V
• Martin de Carpi J

• KMID: 2409363
• DOI: http://doi.org/10.5223/pghn.2018.21.2.118

Abstract

PURPOSE
Classical criteria for diagnosis of autoimmune hepatitis (AIH) are intended as research tool and are difficult to apply at patient's bedside. We aimed to study the accuracy of simplified criteria and the concordance with the expert diagnosis based on the original criteria.
METHODS
A cohort of children under study for liver disorder was selected through consecutive sampling to obtain the prevalence of AIH within the group of differential diagnoses. AIH was defined, based on classical criteria, through committee review of medical reports. Validity indicators of the simplified criteria were obtained in an intention to diagnose approach. Optimal cut-off and the area under the receiver operating characteristic (ROC) curve were calculated.
RESULTS
Out of 212 cases reviewed, 47.2% were AIH. For the optimal cut-off (6 points), the simplified criteria showed a sensitivity of 72.0% and a specificity of 96.4%, with a 94.7% positive and a 79.4% negative predictive value. The area under the ROC curve was 94.3%. There was a good agreement in the pre-treatment concordance between the classical and the simplified criteria (kappa index, 0.775).
CONCLUSION
Simplified criteria provide a moderate sensitivity for the diagnosis of AIH, but may help in indicating treatment in cases under suspicion with 6 or more points.

Keyword

Autoimmune hepatitis; Diagnosis; Sensitivity and specificity

MeSH Terms

Child*
Cohort Studies
Diagnosis*
Diagnosis, Differential
Hepatitis, Autoimmune*
Humans
Intention
Liver
Prevalence
ROC Curve
Sensitivity and Specificity

Figure

• 

  Fig. 1 Simplified criteria receiver operating characteristic curve. CI: confidence interval.

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