title: External validation of outcome prediction model for ureteral/renal calculi.
abstract: OBJECTIVE
  We externally validated a previously designed neural network model to predict outcome and duration of passage for ureteral/renal calculi. The model was also evaluated using a 6 mm largest stone dimension cutoff in predicting stone outcome.
  
  
  METHODS
  The model was previously designed on 301 patients at Albany Medical Center (free shareware from www.uroengineering.com). The model had a prediction accuracy of 86% for passage outcome and 87% for passage duration. In this study we tested the model on a separate 384 patients from 6 different external institutions to assess the prediction accuracy. All patients had a single renal/ureteral calculus by evaluation in an emergency room setting or by primary physicians and were then referred for further treatment. Model accuracy was also compared to using a 6 mm largest stone dimension cutoff in predicting the need for intervention.
  
  
  RESULTS
  Testing on the 384 patients from all 6 external institutions revealed an outcome prediction accuracy of 88%. The area under the ROC curve was 0.9. Using a 6 mm stone size cutoff provided 79% (ROC 0.8) accuracy. The model duration of passage prediction accuracy was 80% (133 patients passed the stone, area under ROC of 0.8).
  
  
  CONCLUSIONS
  The model provided high stone outcome prediction accuracy (ROC of 0.9 and 0.8) at the 6 external institutions, comparable to that of the design institution. The model provided higher accuracy than using only the largest stone dimension as a cutoff. Increasing experience will further assess the model's accuracy.
authors.affiliation: Cleveland Clinic Foundation, Cleveland, Ohio, USA. sijo_p@hotmail.com
authors.forename: Sijo J
authors.initials: SJ
authors.lastname: Parekattil
authors.forename: Udaya
authors.initials: U
authors.lastname: Kumar
authors.forename: Nicholas J
authors.initials: NJ
authors.lastname: Hegarty
authors.forename: Clay
authors.initials: C
authors.lastname: Williams
authors.forename: Tara
authors.initials: T
authors.lastname: Allen
authors.forename: Patrick
authors.initials: P
authors.lastname: Teloken
authors.forename: Victor A
authors.initials: VA
authors.lastname: Leitão
authors.forename: Nelson R
authors.initials: NR
authors.lastname: Netto
authors.forename: Georges-Pascal
authors.initials: GP
authors.lastname: Haber
authors.forename: Charles
authors.initials: C
authors.lastname: Ballereau
authors.forename: Arnauld
authors.initials: A
authors.lastname: Villers
authors.forename: Stevan B
authors.initials: SB
authors.lastname: Streem
authors.forename: Mark D
authors.initials: MD
authors.lastname: White
authors.forename: Michael E
authors.initials: ME
authors.lastname: Moran
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D005260:Female; D006801:Humans; D007669:Kidney Calculi / Q000628:therapy; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012075:Remission, Spontaneous; D015203:Reproducibility of Results; D014514:Ureteral Calculi / Q000628:therapy