Tangri Navdeep, Ansell David, Naimark David
Department of Internal Medicine, McGill University, Montreal, QC, Canada.
Nephrol Dial Transplant. 2008 Sep;23(9):2972-81. doi: 10.1093/ndt/gfn187. Epub 2008 Apr 25.
Early technique failure has been a major limitation on the wider adoption of peritoneal dialysis (PD). The objectives of this study were to use data from a large, multi-centre, prospective database, the United Kingdom Renal Registry (UKRR), in order to determine the ability of an artificial neural network (ANN) model to predict early PD technique failure and to compare its performance with a logistic regression (LR)-based approach.
The analysis included all incident PD patients enrolled in the UKRR from 1999 to 2004. The event of interest was technique failure. For both the ANN and LR analyses a bootstrap approach was used: the data were divided into 20 random training (75%) and validation (25%) sets. Models were derived on the latter and then used to make predictions on the former. Predictive accuracy was assessed by area under the ROC curve (AUROC). The 20 AUROC values and their standard errors were then averaged.
There were 3269 patients included in the analysis with a mean age of 59.9 years and a mean observation time of 430 days. Of the patients, 38.3% were female and 90.8% were Caucasian. 1458 patients (44.6%) suffered technique failure. The AUROC for the ANN model was 0.760 +/- 0.0167 and the LR model was 0.709 and 0.0208. (P = 0.0164)
Using UKRR data, both ANN and LR models predicted early PD technique failure with moderate accuracy. In this study, an ANN outperformed an LR-based approach. As the scope and the completeness of the UKRR increases, the question of whether more sophisticated ANN models will perform even better remains for further study.
早期技术失败一直是腹膜透析（PD）广泛应用的主要限制因素。本研究的目的是利用来自大型多中心前瞻性数据库——英国肾脏注册中心（UKRR）的数据，以确定人工神经网络（ANN）模型预测早期PD技术失败的能力，并将其性能与基于逻辑回归（LR）的方法进行比较。
分析纳入了1999年至2004年在UKRR登记的所有新发PD患者。感兴趣的事件是技术失败。对于ANN和LR分析，均采用自助法：将数据随机分为20个训练集（75%）和验证集（25%）。在验证集上推导模型，然后用于对训练集进行预测。通过ROC曲线下面积（AUROC）评估预测准确性。然后对20个AUROC值及其标准误差进行平均。
分析纳入3269例患者，平均年龄59.9岁，平均观察时间430天。患者中，38.3%为女性，90.8%为白种人。1458例患者（44.6%）出现技术失败。ANN模型的AUROC为0.760±0.0167，LR模型的AUROC为0.709±0.0208。（P = 0.0164）
利用UKRR数据，ANN和LR模型对早期PD技术失败均有中等程度的准确预测。在本研究中，ANN的表现优于基于LR的方法。随着UKRR范围和完整性的增加，更复杂的ANN模型是否会表现得更好仍有待进一步研究。
