title: Predicting technique survival in peritoneal dialysis patients: comparing artificial neural networks and logistic regression.
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.
authors.affiliation: Department of Internal Medicine, McGill University, Montreal, QC, Canada. ntangri@yahoo.com
authors.forename: Navdeep
authors.initials: N
authors.lastname: Tangri
authors.forename: David
authors.initials: D
authors.lastname: Ansell
authors.forename: David
authors.initials: D
authors.lastname: Naimark
mesh_terms: D000465:Algorithms; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D010530:Peritoneal Dialysis; D012372:ROC Curve; D012042:Registries; D017211:Treatment Failure; D006113:United Kingdom