Format

Send to

Choose Destination
See comment in PubMed Commons below
Nephrol Dial Transplant. 2008 Sep;23(9):2972-81. doi: 10.1093/ndt/gfn187. Epub 2008 Apr 25.

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

Author information

  • 1Department of Internal Medicine, McGill University, Montreal, QC, Canada. ntangri@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.

PMID:
18441002
PMCID:
PMC2517147
DOI:
10.1093/ndt/gfn187
[PubMed - indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for HighWire Icon for PubMed Central
    Loading ...
    Support Center