Accurate prediction of biliary atresia with an integrated model using MMP-7 levels and bile acids

Area of Science:

• Pediatric Hepatology
• Biomarker Discovery
• Computational Diagnostics

Background:

• Biliary atresia (BA) is a rare, fatal liver disease affecting infants.
• Early diagnosis is critical for effective treatment and improved outcomes.
• Current diagnostic methods can be invasive or lack sufficient accuracy.

Purpose of the Study:

• To develop a novel, noninvasive diagnostic method for biliary atresia (BA).
• To evaluate the diagnostic performance of serum matrix metalloproteinase-7 (MMP-7) levels.
• To integrate MMP-7, liver tests, and bile acid profiles using computational models for enhanced BA diagnosis.

Main Methods:

• Serum MMP-7 levels, 13 liver tests, and 20 bile acids were measured in 86 BA and 59 non-BA patients.
• Computational models were constructed to predict BA based on these biomarkers.
• Diagnostic accuracy was assessed using the area under the receiver operating characteristic curve (AUC).

Main Results:

• MMP-7 levels, four liver tests, and ten bile acids differed significantly between BA and non-BA groups (P < 0.05).
• MMP-7 alone showed high predictive accuracy (AUC = 0.966).
• Integrated models combining MMP-7, liver tests, and bile acids achieved the highest accuracy (AUC = 0.983), outperforming individual components.

Conclusions:

• Integrated computational models offer a noninvasive and cost-effective approach for accurate BA diagnosis in children.
• MMP-7 is a promising biomarker for BA detection, significantly enhanced by combining it with liver tests and bile acids.
• This approach holds potential for early intervention and improved patient outcomes in biliary atresia.