肝细胞癌
肝病学
医学
内科学
肝移植
肝病
辍学(神经网络)
危险系数
比例危险模型
移植
肝癌
肿瘤科
胃肠病学
置信区间
计算机科学
机器学习
作者
Christian Toso,Elise Dupuis‐Lozeron,Pietro Majno,Thierry Berney,Norman M. Kneteman,Thomas Perneger,Philippe Morel,Gilles Mentha,Christophe Combescure
出处
期刊:Hepatology
[Lippincott Williams & Wilkins]
日期:2012-01-23
卷期号:56 (1): 149-156
被引量:105
摘要
In many countries, the allocation of liver grafts is based on the Model of End-stage Liver Disease (MELD) score and the use of exception points for patients with hepatocellular carcinoma (HCC). With this strategy, HCC patients have easier access to transplantation than non-HCC ones. In addition, this system does not allow for a dynamic assessment, which would be required to picture the current use of local tumor treatment. This study was based on the Scientific Registry of Transplant Recipients and included 5,498 adult candidates of a liver transplantation for HCC and 43,528 for non-HCC diagnoses. A proportional hazard competitive risk model was used. The risk of dropout of HCC patients was independently predicted by MELD score, HCC size, HCC number, and alpha-fetoprotein. When combined in a model with age and diagnosis, these factors allowed for the extrapolation of the risk of dropout. Because this model and MELD did not share compatible scales, a correlation between both models was computed according to the predicted risk of dropout, and drop-out equivalent MELD (deMELD) points were calculated.The proposed model, with the allocation of deMELD, has the potential to allow for a dynamic and combined comparison of opportunities to receive a graft for HCC and non-HCC patients on a common waiting list.
科研通智能强力驱动
Strongly Powered by AbleSci AI