Development and Validation of Novel Diagnostic Models for Biliary Atresia in a Large Cohort of Chinese Patients

Background & aims: The overlapping features of biliary atresia (BA) and the other forms of neonatal cholestasis (NC) with different causes (non-BA) has posed challenges for the diagnosis of BA. This study aimed at developing new and better diagnostic models for BA. Methods: We retrospectively analyzed data from 1728 newborn infants with neonatal obstructive jaundice (NOJ). New prediction models, including decision tree (DT), random forest (RF), and multivariate logistic regression-based nomogram for BA were created and externally validated in an independent set of 508 infant patients. Results: Fiver predictors, including gender, weight, direct bilirubin (DB), alkaline phosphatase (ALP), and gamma-glutamyl transpeptidase (GGT) were significantly different between the BA and non-BA groups (P<.05), from which DT, RF, and nomogram models were developed. The area under the receiver operating characteristic (ROC) curve (AUC) value for the nomogram was 0.898, which was greater than that of a single biomarker in the prediction of BA. Performance comparison of the three diagnostic models showed that the nomogram displayed better discriminative ability (sensitivity, 85.7%; specificity, 80.3%; PPV, 0.969) at the optimal cut-off value compared with DT and RF, which had relatively similar high sensitivity and PPV (0.941 and 0.947, respectively), but low specificity in the modeling group. In sub-analysis of the discriminative capacity between the nomogram and GGT (<300 or >= 300), we found that the nomogram was superior to the GGT alone in the preoperative diagnosis of BA. Conclusions: The nomogram has demonstrated better performance for the prediction of BA, holding promise for future clinical application. (c) 2018 The Authors. Published by Elsevier B.V.