BMC pediatrics · 2023
Development and validation of a model for early diagnosis of biliary atresia.
Наблюдательное исследованиеСтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Gong Zongrong, Lin Lin, Lu Gen, Wan Chaomin
Аннотация
BACKGROUND AND AIMS: Early diagnosis of biliary atresia (BA), particularly distinguishing it from other causes of neonatal cholestasis (NC), is challenging. This study aimed to design and validate a predictive model for BA by using the data available at the initial presentation.
METHODS: Infants presenting with NC were retrospectively identified from tertiary referral hospitals and constituted the model design cohort (n = 148); others were enrolled in a prospective observational study and constituted the validation cohort (n = 21). Clinical, laboratory, and abdominal ultrasonographic features associated with BA were assessed. A prediction model was developed using logistic regression and decision tree (DT) analyses.
RESULTS: Three predictors, namely, gamma glutamyl transpeptidase (γGT) level, triangular cord sign (TC sign), and gallbladder abnormalities, were identified as factors for diagnosing BA in multivariate logistic regression, which was used to develop the DT model. The area under the receiver operating characteristic (ROC) curve (AUC) value for the model was 0.905, which was greater than those for γGT level, TC sign, or gallbladder abnormalities alone in the prediction of BA.
CONCLUSION: A simple prediction model combining liver function and abdominal ultrasonography findings can provide a moderate and early estimate of the risk of BA in patients with NC.
METHODS: Infants presenting with NC were retrospectively identified from tertiary referral hospitals and constituted the model design cohort (n = 148); others were enrolled in a prospective observational study and constituted the validation cohort (n = 21). Clinical, laboratory, and abdominal ultrasonographic features associated with BA were assessed. A prediction model was developed using logistic regression and decision tree (DT) analyses.
RESULTS: Three predictors, namely, gamma glutamyl transpeptidase (γGT) level, triangular cord sign (TC sign), and gallbladder abnormalities, were identified as factors for diagnosing BA in multivariate logistic regression, which was used to develop the DT model. The area under the receiver operating characteristic (ROC) curve (AUC) value for the model was 0.905, which was greater than those for γGT level, TC sign, or gallbladder abnormalities alone in the prediction of BA.
CONCLUSION: A simple prediction model combining liver function and abdominal ultrasonography findings can provide a moderate and early estimate of the risk of BA in patients with NC.