Hoot Nathan, Aronsky Dominik
Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
AMIA Annu Symp Proc. 2005;2005:345-9.
The relative scarcity of grafts available for liver transplantation highlights the need to identify patients likely to have good outcomes after treatment. We used transplant information from the United Network for Organ Sharing database to construct a Bayesian network model to predict 90-day graft survival. The final model incorporated a set of 29 pre-transplant variables, and it achieved performance, as measured by area under the receiver operating characteristic curve, of 0.674 by cross-validation and 0.681 on an independent validation set. The results showed a positive predictive value of 91%, while the negative predictive value was lower at 30%. With additional refinement and validation, our model may be useful as an adjunct to clinical experience in identifying patients most likely to have good outcomes following liver transplantation.
可用于肝移植的移植物相对稀缺，这凸显了识别治疗后可能有良好预后患者的必要性。我们利用器官共享联合网络数据库中的移植信息构建了一个贝叶斯网络模型，以预测90天移植物存活率。最终模型纳入了一组29个移植前变量，通过交叉验证，其受试者操作特征曲线下面积衡量的性能为0.674，在独立验证集上为0.681。结果显示阳性预测值为91%，而阴性预测值较低，为30%。经过进一步完善和验证，我们的模型可能有助于临床经验，用于识别肝移植后最有可能有良好预后的患者。
