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

Détails

Ressource 1Télécharger: BIB_7BB0E536211D.P001.pdf (345.05 [Ko])
Etat: Serval
Version: de l'auteur
ID Serval
serval:BIB_7BB0E536211D
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Would artificial neural networks implemented in clinical wards help nephrologists in predicting epoetin responsiveness?
Périodique
BMC Nephrology
Auteur(s)
Gabutti L., Lötscher N., Bianda J., Marone C., Mombelli G., Burnier M.
ISSN
1471-2369
Statut éditorial
Publié
Date de publication
2006
Peer-reviewed
Oui
Volume
7
Pages
13
Langue
anglais
Notes
Publication types: Journal Article ; Multicenter Study ; Research Support, Non-U.S. Gov't
Résumé
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.
Mots-clé
Adolescent, Adult, Aged, Aged, 80 and over, Anemia, Computer Simulation, Decision Support Systems, Clinical, Drug Therapy, Computer-Assisted, Erythropoietin, Expert Systems, Female, Hemoglobins, Humans, Kidney Diseases, Male, Middle Aged, Models, Biological, Nephrology, Neural Networks (Computer), Point-of-Care Systems, Renal Dialysis, Reproducibility of Results, Sensitivity and Specificity, Switzerland, Treatment Outcome
Pubmed
Open Access
Oui
Création de la notice
25/01/2008 13:56
Dernière modification de la notice
08/05/2019 20:50
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