ArticlePediatric transplantation2026
Index to Predict Waiting Times for Pediatric Liver Transplantation.
Article in Pediatric transplantation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Authors and funding
8 authors.
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Abstract
backgroundHundreds of pediatric candidates are added to the liver transplant waitlist annually, yet wait times remain uncertain. This study identifies the variables associated with changes in wait time and creates a predictive points score to predict the likelihood of receiving a deceased-donor pediatric liver transplant within 1 year of listing.
methodsA retrospective analysis of 5246 pediatric liver transplant candidates (2014-2024) was conducted using de-identified OPTN/UNOS data. Variables including demographics, lab values, and medical history were analyzed using univariate and multivariate logistic regression. Significant predictors from the multivariate model were used to develop a weighted points system, with model performance assessed via ROC analysis.
results19 variables were statistically significant. The 3 significant variables most positively associated with receiving a transplant are: high volume transplant center (Odds Ratio [OR]: 3.79, p-value [p] < 0.001), Pacific Islander race (OR: 2.96, p = 0.049), and medium volume transplant center (OR: 2.53, p < 0.003). The 3 significant variables most negatively associated with receiving a transplant are: serum sodium > 150 mEq/L (OR: 0.37, p = 0.002), acute hepatic necrosis (OR: 0.53, p < 0.001), and bilirubin 1-2 mg/dL (OR: 0.68, p < 0.001). The model demonstrated moderate predictive accuracy (c-statistic = 0.66).
conclusionThis study identifies key predictors of pediatric liver transplant wait time and introduces a predictive scoring system to improve clinical decision-making. This tool provides a more accurate framework for identifying vulnerable candidates and can assist clinicians in pursuing technical variant or living donor options for those at highest risk of prolonged waitlist times.
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