Initial assessment of the infant with neonatal cholestasis—Is this biliary atresia?

Benjamin L Shneider, Jeff Moore, Nanda Kerkar, John C Magee, Wen Ye, Saul J Karpen, Binita M Kamath, Jean P Molleston, Jorge A Bezerra, Karen F Murray, Kathleen M Loomes, Peter F Whitington, Philip Rosenthal, Robert H Squires, Stephen L Guthery, Ronen Arnon, Kathleen B Schwarz, Yumirle P Turmelle, Averell H Sherker, Ronald J Sokol and for the Childhood Liver Disease Research Network

PLOS ONE, 2017, vol. 12, issue 5, 1-15

Abstract: Introduction: Optimizing outcome in biliary atresia (BA) requires timely diagnosis. Cholestasis is a presenting feature of BA, as well as other diagnoses (Non-BA). Identification of clinical features of neonatal cholestasis that would expedite decisions to pursue subsequent invasive testing to correctly diagnose or exclude BA would enhance outcomes. The analytical goal was to develop a predictive model for BA using data available at initial presentation. Methods: Infants at presentation with neonatal cholestasis (direct/conjugated bilirubin >2 mg/dl [34.2 μM]) were enrolled prior to surgical exploration in a prospective observational multi-centered study (PROBE–NCT00061828). Clinical features (physical findings, laboratory results, gallbladder sonography) at enrollment were analyzed. Initially, 19 features were selected as candidate predictors. Two approaches were used to build models for diagnosis prediction: a hierarchical classification and regression decision tree (CART) and a logistic regression model using a stepwise selection strategy. Results: In PROBE April 2004-February 2014, 401 infants met criteria for BA and 259 for Non-BA. Univariate analysis identified 13 features that were significantly different between BA and Non-BA. Using a CART predictive model of BA versus Non-BA (significant factors: gamma-glutamyl transpeptidase, acholic stools, weight), the receiver operating characteristic area under the curve (ROC AUC) was 0.83. Twelve percent of BA infants were misclassified as Non-BA; 17% of Non-BA infants were misclassified as BA. Stepwise logistic regression identified seven factors in a predictive model (ROC AUC 0.89). Using this model, a predicted probability of >0.8 (n = 357) yielded an 81% true positive rate for BA;

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0176275

DOI: 10.1371/journal.pone.0176275

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