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Lack of a centre effect in UK renal units: application of an artificial neural network model
Navdeep Tangri1, David Ansell, David Naimark
1Department of Internal Medicine, McGill University, Montreal QC, Canada. ntangri@yahoo.com
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.
