title: Would artificial neural networks implemented in clinical wards help nephrologists in predicting epoetin responsiveness?
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).
  
  
  CONCLUSIONS
  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.
authors.affiliation: Division of Nephrology, Ospedale la Carità, Via Ospedale, 6600 Locarno, Switzerland. luca.gabutti@eoc.ch
authors.forename: Luca
authors.initials: L
authors.lastname: Gabutti
authors.forename: Nathalie
authors.initials: N
authors.lastname: Lötscher
authors.forename: Josephine
authors.initials: J
authors.lastname: Bianda
authors.forename: Claudio
authors.initials: C
authors.lastname: Marone
authors.forename: Giorgio
authors.initials: G
authors.lastname: Mombelli
authors.forename: Michel
authors.initials: M
authors.lastname: Burnier
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000740:Anemia / Q000097:blood / Q000188:drug therapy* / Q000209:etiology; D003198:Computer Simulation; D020000:Decision Support Systems, Clinical; D004360:Drug Therapy, Computer-Assisted / Q000379:methods*; D004921:Erythropoietin / Q000008:administration & dosage*; D005103:Expert Systems; D005260:Female; D006454:Hemoglobins / Q000032:analysis; D006801:Humans; D007674:Kidney Diseases / Q000097:blood / Q000150:complications / Q000628:therapy*; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D009398:Nephrology / Q000379:methods; D016571:Neural Networks, Computer; D019095:Point-of-Care Systems; D006435:Renal Dialysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013557:Switzerland; D016896:Treatment Outcome
chemical_list: D006454:Hemoglobins; D004921:Erythropoietin