Email updates

Keep up to date with the latest news and content from BMC Nephrology and BioMed Central.

Open Access Research article

Would artificial neural networks implemented in clinical wards help nephrologists in predicting epoetin responsiveness?

Luca Gabutti1*, Nathalie Lötscher2, Josephine Bianda1, Claudio Marone3, Giorgio Mombelli4 and Michel Burnier5

Author Affiliations

1 Division of Nephrology, Ospedale la Carità, Via Ospedale, 6600 Locarno, Switzerland

2 Roche Pharma Switzerland Ltd, Reinach, Switzerland

3 Department of Internal Medicine, Ospedale San Giovanni, Bellinzona, Switzerland

4 Department of Internal Medicine, Ospedale la Carità, Locarno, Switzerland

5 Division of Nephrology, University Hospital of Lausanne, Lausanne, Switzerland

For all author emails, please log on.

BMC Nephrology 2006, 7:13  doi:10.1186/1471-2369-7-13

Published: 18 September 2006

Abstract

Background

Due to its strong intra- and inter-individual variability, predicting the ideal erythropoietin dose is a difficult task. The aim of this study was to re-evaluate the impact of the main parameters known to influence the responsiveness to epoetin beta and to test the performance of artificial neural networks (ANNs) in predicting the dose required to reach the haemoglobin target and the monthly dose adjustments.

Methods

We did a secondary analysis of the survey on Anaemia Management in dialysis patients in Switzerland; a prospective, non-randomized observational study, enrolling 340 patients of 26 centres and in order to have additional information about erythropoietin responsiveness, we included a further 92 patients from the Renal Services of the Ente Ospedaliero Cantonale, Bellinzona, Switzerland. The performance of ANNs in predicting the epoetin dose was compared with that of linear regressions and of nephrologists in charge of the patients.

Results

For a specificity of 50%, the sensitivity of ANNs compared with linear regressions in predicting the erythropoietin dose to reach the haemoglobin target was 78 vs. 44% (P < 0.001). The ANN built to predict the monthly adaptations in erythropoietin dose, compared with the nephrologists' opinion, allowed to detect 48 vs. 25% (P < 0.05) of the patients treated with an insufficient dose with a specificity of 92 vs. 83% (P < 0.05).

Conclusion

In predicting the erythropoietin dose required for an individual patient and the monthly dose adjustments ANNs are superior to nephrologists' opinion. Thus, ANN may be a useful and promising tool that could be implemented in clinical wards to help nephrologists in prescribing erythropoietin.