Trial reportPloS one2017
Initial assessment of the infant with neonatal cholestasis-Is this biliary atresia?
Trial report in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00061828 (Childhood Liver Disease Research Network), which is not on this map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Childhood Liver Disease Research Network (ChiLDReN): A Prospective Database of Infants With Cholestasis
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.
- The diagnostic performance of ultrasound features for biliary atresia: a systematic review and updated meta-analysis.Pediatric surgery international · 2025Pooled it
- Article
- Simple biliary atresia score-a validated diagnostic aid for infantile cholestasis.Pediatric surgery international · 2024Article
- The magnetic resonance imaging and age-adjusted matrix metalloproteinase-7 assist the diagnosis of biliary atresia.Journal of gastroenterology · 2024Article
- Development and Validation of a Machine Learning-Based Prediction Model for Detection of Biliary Atresia.Gastro hep advances · 2023Article
- Protocols of Investigation of Neonatal Cholestasis-A Critical Appraisal.Healthcare (Basel, Switzerland) · 2022Review
- Serum matrix metalloproteinase-7 levels in infants with cholestasis and biliary atresia.BMC pediatrics · 2022Article
- Low gamma-glutamyl transpeptidase levels at presentation are associated with severity of liver illness and poor outcome in biliary atresia.Frontiers in pediatrics · 2022Article
- Neonatal cholestasis: development of a diagnostic decision algorithm from multivariate predictive models.European journal of pediatrics · 2021Article
- Improving Poor Outcomes of Children With Biliary Atresia in South Africa by Early Referral to Centralized Units.JPGN reports · 2021Article
- Ensembled deep learning model outperforms human experts in diagnosing biliary atresia from sonographic gallbladder images.Nature communications · 2021Article
- Combination of gamma-glutamyl transferase and liver stiffness measurement for biliary atresia screening at different ages: a retrospective analysis of 282 infants.BMC pediatrics · 2020Article
- Tc-99m PMT scintigraphy in the diagnosis of pediatric biliary atresia.Japanese journal of radiology · 2019Article
- Diagnostic Accuracy of Serum Matrix Metalloproteinase-7 for Biliary Atresia.Hepatology (Baltimore, Md.) · 2018Observational
- Regulation of epithelial injury and bile duct obstruction by NLRP3, IL-1R1 in experimental biliary atresia.Journal of hepatology · 2018Article
Corrections and comments
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Authors and funding
21 authors at 17 institutions in 3 countries.
Funding
Abstract
introductionOptimizing 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.
methodsInfants 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.
resultsIn 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; <0.2 (n = 120) yielded an 11% false negative rate.
conclusionDespite the relatively good accuracy of our optimized prediction models, the high precision required for differentiating BA from Non-BA was not achieved. Accurate identification of BA in infants with neonatal cholestasis requires further evaluation, and BA should not be excluded based only on presenting clinical features.
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