Dataset Information

Accuracy of the Pediatric End-stage Liver Disease Score in Estimating Pretransplant Mortality Among Pediatric Liver Transplant Candidates.

ABSTRACT:

Importance

Fair allocation of livers between pediatric and adult recipients is critically dependent on the accuracy of mortality estimates afforded by the Pediatric End-stage Liver Disease (PELD) and Model for End-stage Liver Disease, respectively. Widespread reliance on exceptions for pediatric recipients suggests that the 2 systems may not be comparable.

Objective

To evaluate the accuracy of the PELD score in estimating 90-day pretransplant mortality among pediatric patients on the United Network for Organ Sharing (UNOS) waiting list.

Design, setting, and participants

Patients who were listed from February 27, 2002, to March 31, 2014, for primary liver transplant were included in this retrospective analysis and were followed up for at least 2 years through June 17, 2016. The study analyzed 2 cohorts using the UNOS Standard Transplant Analysis and Research data files. The full cohort comprised 4298 patients (<18 years of age) who had chronic liver disease (excluding cancer). The reduced cohort (n = 2421) excluded patients receiving living donor transplantation or PELD exception points.

Main outcomes and measures

Observed and expected 90-day pretransplant mortality rates evaluated at 10-point interval PELD levels.

Results

Among the 4298 patients in the full cohort (mean [SD] age, 2.5 [4.2] years; 2251 [52.4%] female; 2201 [51.2%] white), PELD scores and mortality were concordant (C statistic, 0.8387 [95% CI, 0.8191-0.8584] for the full cohort and 0.8123 [95% CI, 0.7919-0.8327] for the reduced cohort). However, the estimated 90-day mortality using the PELD score underestimated the actual probability of death by as much as 17%.

Conclusions and relevance

With use of the PELD score, the ranking of risk among children was preserved, but direct comparisons between adult and pediatric candidates were not accurate. Children with chronic liver disease who are in need of transplant may be at a disadvantage compared with adults in a similar situation.

SUBMITTER: Chang CH

PROVIDER: S-EPMC6248160 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundThe scarcity of grafts available necessitates a system that considers expected posttransplant survival, in addition to pretransplant mortality as estimated by the MELD. So far, however, conventional linear techniques have failed to achieve sufficient accuracy in posttransplant outcome prediction. In this study, we aim to develop a pretransplant predictive model for liver recipients' survival with benign end-stage liver diseases (BESLD) by a nonlinear method based on pretransplant characteristics, and compare its performance with a BESLD-specific prognostic model (MELD) and a general-illness severity model (the sequential organ failure assessment score, or SOFA score).Methodology/principal findingsWith retrospectively collected data on 360 recipients receiving deceased-donor transplantation for BESLD between February 1999 and August 2009 in the west China hospital of Sichuan university, we developed a multi-layer perceptron (MLP) network to predict one-year and two-year survival probability after transplantation. The performances of the MLP, SOFA, and MELD were assessed by measuring both calibration ability and discriminative power, with Hosmer-Lemeshow test and receiver operating characteristic analysis, respectively. By the forward stepwise selection, donor age and BMI; serum concentration of HB, Crea, ALB, TB, ALT, INR, Na(+); presence of pretransplant diabetes; dialysis prior to transplantation, and microbiologically proven sepsis were identified to be the optimal input features. The MLP, employing 18 input neurons and 12 hidden neurons, yielded high predictive accuracy, with c-statistic of 0.91 (P<0.001) in one-year and 0.88 (P<0.001) in two-year prediction. The performances of SOFA and MELD were fairly poor in prognostic assessment, with c-statistics of 0.70 and 0.66, respectively, in one-year prediction, and 0.67 and 0.65 in two-year prediction.Conclusions/significanceThe posttransplant prognosis is a multidimensional nonlinear problem, and the MLP can achieve significantly high accuracy than SOFA and MELD scores in posttransplant survival prediction. The pattern recognition methodologies like MLP hold promise for solving posttransplant outcome prediction.