Journal Article

Lack of a centre effect in UK renal units: application of an artificial neural network model

Navdeep Tangri, David Ansell and David Naimark

in Nephrology Dialysis Transplantation

Published on behalf of European Renal Association - European Dialysis and Transplant Assoc

Volume 21, issue 3, pages 743-748
Published in print March 2006 | ISSN: 0931-0509
Published online November 2005 | e-ISSN: 1460-2385 | DOI:
Lack of a centre effect in UK renal units: application of an artificial neural network model

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Background. Dialysis centre effect has been suggested to influence survival in end-stage renal disease (ESRD) patients. Few studies over the past decade have commented on the existence of the centre effect using logistic regression models.

Methods. We used high quality prospectively collected data from the UK Renal Registry (UKRR) and created an artificial neural network model to predict mortality within 1 year in this cohort. We used a multitude of demographic variables including co-morbodities as well as relevant laboratory data to create a prognostic model.

Results. A highly efficient model for predicting 1 year mortality was created after restricting the model to use demographic and case-enriched data [area under the receiver operating characteristic curve (AUROC) = 0.974]. The addition of the dialysis centre code and centre size as input variables did not add to the efficiency of the model (AUROC = 0.962). Moreover, dialysis centre code or size alone was not predictive of mortality when applied to an artificial neuronal network architecture (AUROC = 0.649 and 0.628).

Conclusion. Residual effects in previous studies may have been due to the non-linear nature of the data and complex intervariable relationships. Centre size and other centre-related factors have no impact on survival on ESRD.

Keywords: artificial neuronal network; centre effect; centre size; dialysis centres; patient survival; renal failure

Journal Article.  3249 words.  Illustrated.

Subjects: Nephrology

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