Pretransplant prediction of posttransplant survival for liver recipients with benign end-stage liver diseases: a nonlinear model

The 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...

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Bibliographic Details
Published in:PloS one 2012-03, Vol.7 (3), p.e31256-e31256
Main Authors: Zhang, Ming, Yin, Fei, Chen, Bo, Li, You Ping, Yan, Lu Nan, Wen, Tian Fu, Li, Bo
Format: Article
Language:English
Subjects:
Adult
Algorithms
Analysis
Benign
Biology
Biopsy
Blood & organ donations
Body mass
Calibration
Diabetes mellitus
Dialysis
End Stage Liver Disease - mortality
End Stage Liver Disease - therapy
Evidence-based medicine
Female
Graft Survival
Grafts
Head injuries
Health aspects
Hepatitis
Hospitals
Humans
Intensive care
Liver
Liver diseases
Liver transplantation
Liver Transplantation - methods
Liver transplants
Male
Mathematical models
Medical prognosis
Medical schools
Medicine
Middle Aged
Models, Statistical
Mortality
Neurons
Nonlinear Dynamics
Outcome Assessment (Health Care)
Pattern recognition
Pediatrics
Prediction models
Prognosis
Retrospective Studies
ROC Curve
Sepsis
Severity of Illness Index
Short term
Statistical analysis
Survival
Transplantation
Transplants & implants
Treatment Outcome
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Summary:The 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). With 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
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0031256