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


CONCLUSIONS
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.
Tangri
Navdeep
N
Department of Internal Medicine, McGill University, Montreal QC, Canada. ntangri@yahoo.com
Ansell
David
D
Naimark
David
D
D000368:Aged; D005260:Female; D017052:Hospital Mortality / Q000639:trends; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D007676:Kidney Failure, Chronic / Q000401:mortality / Q000628:therapy*; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011379:Prognosis; D011446:Prospective Studies; D006435:Renal Dialysis / Q000401:mortality*; D006113:United Kingdom / Q000453:epidemiology