Identifying the Prognosis Factors in Death after Liver Transplantation via Adaptive LASSO in Iran.
Identifying the Prognosis Factors in Death after Liver Transplantation via Adaptive LASSO in Iran.
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DOI：
10.1155/2016/7620157
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发表时间：
2016
影响因子：
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通讯作者：
Ayatollahi SM
中科院分区：
文献类型：
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作者：
Raeisi Shahraki H;Pourahmad S;Ayatollahi SM
尽管肝移植作为肝病的常规治疗手段已得到广泛应用，但影响其疗效的因素仍存在争议。本研究试图找出对肝移植后死亡影响最大的因素。为此，使用了改进的最小绝对收缩和选择算子(LASSO)，称为自适应LASSO。这种方法的最大优点之一是考虑了大量的因素。因此，在2008年至2013年的历史队列研究中，考虑了680名接受肝移植手术的患者的临床结果。然后采用岭回归和自适应套索回归方法确定对死亡影响最大的因素。为了比较这两种模型的性能，使用了接收器工作特性(ROC)曲线。结果表明，岭回归中有12个因素显著，自适应套索回归中有9个因素显著。自适应套索的ROC曲线下面积为89%(95%CI：86%~91%)，明显大于岭回归(%，95%CI：61%~68%)(p<；0.001)。总而言之，显著因素和绩效标准显示了自适应套索方法作为惩罚模型相对于传统回归模型的优越性。
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%) (p < 0.001). 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.