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Journal of Environmental and Public Health
Volume 2016, Article ID 7620157, 6 pages
http://dx.doi.org/10.1155/2016/7620157
Research Article

Identifying the Prognosis Factors in Death after Liver Transplantation via Adaptive LASSO in Iran

Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran

Received 28 April 2016; Accepted 7 August 2016

Academic Editor: Pam R. Factor-Litvak

Copyright © 2016 Hadi Raeisi Shahraki et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Despite the widespread use of liver transplantation as a routine therapy in liver diseases, the effective factors on its outcomes are still controversial. This study attempted to identify the most effective factors on death after liver transplantation. For this purpose, modified least absolute shrinkage and selection operator (LASSO), called Adaptive LASSO, was utilized. One of the best advantages of this method is considering high number of factors. Therefore, in a historical cohort study from 2008 to 2013, the clinical findings of 680 patients undergoing liver transplant surgery were considered. Ridge and Adaptive LASSO regression methods were then implemented to identify the most effective factors on death. To compare the performance of these two models, receiver operating characteristic (ROC) curve was used. According to the results, 12 factors in Ridge regression and 9 ones in Adaptive LASSO regression were significant. The area under the ROC curve (AUC) of Adaptive LASSO was equal to 89% (95% CI: 86%–91%), which was significantly greater than Ridge regression (64%, 95% CI: 61%–68%) (). As a conclusion, the significant factors and the performance criteria revealed the superiority of Adaptive LASSO method as a penalized model versus traditional regression model in the present study.