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Liver simulated allocation model does not effectively predict organ offer decisions for pediatric liver transplant candidates
Author(s) -
Wood Nicholas L.,
Mogul Douglas B.,
Perito Emily R.,
VanDerwerken Douglas,
Mazariegos George V.,
Hsu Evelyn K.,
Segev Dorry L.,
Gentry Sommer E.
Publication year - 2021
Publication title -
american journal of transplantation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.89
H-Index - 188
eISSN - 1600-6143
pISSN - 1600-6135
DOI - 10.1111/ajt.16621
Subject(s) - medicine , receiver operating characteristic , liver transplantation , area under the curve , intensive care medicine , pediatrics , transplantation
The SRTR maintains the liver‐simulated allocation model (LSAM), a tool for estimating the impact of changes to liver allocation policy. Integral to LSAM is a model that predicts the decision to accept or decline a liver for transplant. LSAM implicitly assumes these decisions are made identically for adult and pediatric liver transplant (LT) candidates, which has not been previously validated. We applied LSAM's decision‐making models to SRTR offer data from 2013 to 2016 to determine its efficacy for adult (≥18) and pediatric (<18) LT candidates, and pediatric subpopulations—teenagers (≥12 to <18), children (≥2 to <12), and infants (<2)—using the area under the receiver operating characteristic (ROC) curve (AUC). For nonstatus 1A candidates, all pediatric subgroups had higher rates of offer acceptance than adults. For non‐1A candidates, LSAM's model performed substantially worse for pediatric candidates than adults (AUC 0.815 vs. 0.922); model performance decreased with age (AUC 0.898, 0.806, 0.783 for teenagers, children, and infants, respectively). For status 1A candidates, LSAM also performed worse for pediatric than adult candidates (AUC 0.711 vs. 0.779), especially for infants (AUC 0.618). To ensure pediatric candidates are not unpredictably or negatively impacted by allocation policy changes, we must explicitly account for pediatric‐specific decision making in LSAM.