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NDT Advance Access published online on April 25, 2008
Nephrology Dialysis Transplantation, doi:10.1093/ndt/gfn187
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© The Author [2008].
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org


Predicting technique survival in peritoneal dialysis patients: comparing artificial neural networks and logistic regression

Navdeep Tangri1, David Ansell2 and David Naimark3

1 Department of Internal Medicine, McGill University, Montreal, QC, Canada 2 United Kingdom Renal Registry, Bristol, UK 3 Division of Nephrology, Sunnybrook Health Sciences Centre and the Departments of Medicine and of Health Policy Management and Evaluation, University of Toronto, Toronto, ON, Canada

Correspondence and offprint requests to: Navdeep Tangri, Department of Internal Medicine, McGill University, Montreal, QC, Canada. E-mail: ntangri{at}yahoo.com



  Abstract

Background. 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.

Methods. 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.

Results. 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)

Conclusions. 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.

Keywords: artificial neural networks; early technique failure; logistic regression; peritoneal dialysis; technique survival

Received for publication: 27. 8.07
Accepted in revised form: 11. 3.08


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