ArticleBMC nephrology2006
Would artificial neural networks implemented in clinical wards help nephrologists in predicting epoetin responsiveness?
Article in BMC nephrology, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Machine Learning-Based Prediction of Hemoglobin Variability in Patients with Chronic Kidney Disease Receiving Erythropoiesis-Stimulating Agents: A Real-World Retrospective Study.International journal of nephrology and renovascular disease · 2026Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Article
- Mechanisms of hemoglobin cycling in anemia patients treated with erythropoiesis-stimulating agents.PLoS computational biology · 2023Article
- Machine Learning Analysis of Time-Dependent Features for Predicting Adverse Events During Hemodialysis Therapy: Model Development and Validation Study.Journal of medical Internet research · 2021Article
- The Role of Feedback Control Design in Developing Anemia Management Protocols.Annals of biomedical engineering · 2021Article
- Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review.BioMed research international · 2020Review
- Article
- Development of an erythropoietin prescription simulator to improve abilities for the prescription of erythropoietin stimulating agents: is it feasible?BMC nephrology · 2011Article
- Predicting technique survival in peritoneal dialysis patients: comparing artificial neural networks and logistic regression.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2008Article
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6 authors.
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Abstract
backgroundDue 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.
methodsWe 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.
resultsFor 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).
conclusionIn 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.
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