AUTHOR=Zhang Yimao , Wang Qi , Pu Siyu , Wang Junxiang , Xiang Bo , Liu Juxian , Jin Shuguang TITLE=A Novel Model for Predicting the Clearance of Jaundice in Patients With Biliary Atresia After Kasai Procedure JOURNAL=Frontiers in Pediatrics VOLUME=10 YEAR=2022 URL=https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2022.837247 DOI=10.3389/fped.2022.837247 ISSN=2296-2360 ABSTRACT=Background

The failed clearance of jaundice (CJ) in patients with biliary atresia (BA) after the Kasai procedure (KP) often leads to a shorter native liver survival (NLS) time and earlier liver transplantation. We aimed to investigate risk factors of failed CJ and establish a novel nomogram model to predict the status of CJ.

Methods

We retrospectively reviewed institutional medical records from January 2015 to April 2020 and enrolled BA patients post-KP, randomly divided into training and testing cohorts at a ratio of 7:3, and further subdivided into cleared and uncleared jaundice groups. Univariate and multiple logistic regression analyses were used to select risk factors to establish the nomogram in the training cohort. The performance of the nomogram was evaluated by calculating the areas under the receiver operating curve (AUC) in both cohorts.

Results

This study included 175 BA patients post-KP. After univariate and multiple logistic regression analyses, Cytomegalovirus IgM +ve associated BA (OR = 3.38; 95% CI 1.01–11.32; P = 0.04), ln γ-glutamyl transpeptidase (GGT) (OR = 0.41; 95% CI 0.22–0.80; P = 0.009), thickness of the fibrous portal plate (OR = 0.45; 95% CI 0.27–0.76; P = 0.003), liver stiffness measurement (LSM) (OR = 1.19; 95% CI 1.06–1.34; P = 0.002), and multiple episodes of cholangitis (OR = 1.65; 95% CI 1.13–2.41; P = 0.01) were identified as independent risk factors of unsuccessful CJ to construct the nomogram. The receiver operating characteristic curve (ROC) analysis suggested good nomogram performance in both the training (AUC = 0.96) and testing cohorts (AUC = 0.91).

Conclusion

Our nomogram model including several risk factors effectively predicts CJ in patients post-KP, which could aid in clinical decision-making.