Abstract
Background Biliary atresia(BA)is a rare fatal liver disease in children,and the aim of this study was to develop a method to diagnose BA early.Methods We determined serum levels of matrix metalloproteinase-7(MMP-7),the results of 13 liver tests,and the levels of 20 bile acids,and integrated computational models were constructed to diagnose BA.Results Our findings demonstrated that MMP-7 expression levels,as well as the results of four liver tests and levels of ten bile acids,were significantly different between 86 BA and 59 non-BA patients(P＜0.05).The computational prediction model revealed that MMP-7 levels alone had a higher predictive accuracy[area under the receiver operating characteristic curve(AUC)=0.966,95％confidence interval(CI):0.942,0.989]than liver test results and bile acid levels.The AUC was 0.890(95％CI 0.837,0.943)for liver test results and 0.825(95％CI 0.758,0.892)for bile acid levels.Furthermore,bile levels had a higher contribution to enhancing the predictive accuracy of MMP-7 levels(AUC=0.976,95％CI 0.953,1.000)than liver test results.The AUC was 0.983(95％CI 0.962,1.000)for MMP-7 levels combined with liver test results and bile acid levels.In addition,we found that MMP-7 levels were highly correlated with gamma-glutamyl transferase levels and the liver fibrosis score.Conclusion The innovative integrated models based on a large number of indicators provide a noninvasive and cost-effective approach for accurately diagnosing BA in children.
基金项目
Key Program of the Independent Design Project of National Clinical Research Center for Child Health(S20C0004)
Shanghai Pujiang Program(21PJ1423100)
Ministry of Industry and Information Technology Artificial Intelligence Medical Devices Innovation Program()
NETL
NSTL
万方数据