title: Predicting intradialytic hypotension from experience, statistical models and artificial neural networks.
abstract: BACKGROUND
  Symptomatic intradialytic hypotension (IDH) associated with increased mortality in hemodialysis patients is difficult to predict and hence prevent. Artificial Neural Networks (ANNs) are promising tools to solve multidimensional non-linear problems. The aim of the study was to verify in which way mathematical models, statistics or knowledge of patients influence the ability of the nephrologists to predict IDH.
  
  
  METHODS
  The performance of ANNs was compared with that of independent nephrologists supported by a logistic regression giving odds ratio for each studied variable (NEPHiS) or of nephrologists in charge of the patients without (NEPHc) or with statistical support as for NEPHiS (NEPHcS). Data from 98 hemodialysis patients were analysed in order to select patients with frequent IDH (>10% of the dialysis sessions). Complete data on 1979 dialysis sessions from 7 patients were retrieved. The ability to predict the occurrence of hypotension episodes was compared (ROC curves) between ANNs, NEPHc/S (N=7) in Switzerland and NEPHiS from independent dialysis centers in Western Australia (N=10).
  
  
  RESULTS
  ANN gave the most accurate correlation between estimated and observed IHD episodes compared to NEPHc (p<0.001), but a similar performance was attained by NEPHcS (p<0.001). NEPHiS were superior to NEPHc (P<0.05), but inferior to ANN (P<0.01). For a sensitivity of 80%, specificity was 44% for ANNs, 33% for NEPHcS and 20% for NEPHc.
  
  
  CONCLUSIONS
  ANNs are superior to nephrologists in predicting IDH episodes; however when supported by a statistical analysis, nephrologists reach ANNs in their prediction ability. IDH still remains difficult to predict even with mathematical models.
authors.affiliation: Renal Services, Ente Ospedaliero Cantonale, Bellinzona - Switzerland. lugabutti@swissonline.ch
authors.forename: L
authors.initials: L
authors.lastname: Gabutti
authors.forename: M
authors.initials: M
authors.lastname: Machacek
authors.forename: C
authors.initials: C
authors.lastname: Marone
authors.forename: P
authors.initials: P
authors.lastname: Ferrari
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D007022:Hypotension / Q000175:diagnosis* / Q000453:epidemiology* / Q000209:etiology; D015994:Incidence; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D016571:Neural Networks, Computer; D016017:Odds Ratio; D011237:Predictive Value of Tests; D006435:Renal Dialysis / Q000009:adverse effects / Q000706:statistics & numerical data; D012189:Retrospective Studies; D013557:Switzerland / Q000453:epidemiology