title: Use of a handheld, computerized device as a decision support tool for stroke classification.
abstract: BACKGROUND
  The Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification has been widely used to determine etiology of ischemic stroke. However, interrater reliability is known to be modest. The complexity of abstraction and the interpretation of various clinical and laboratory data might limit the accuracy of the TOAST classification. In this study, we developed a computerized clinical decision support system for stroke classification that can be used in a handheld device and tested whether this system can improve diagnostic accuracy and reliability.
  
  
  METHODS
  Based on the TOAST classification, a logical algorithm was developed and implemented on a handheld device, named iTOAST. After answering six questions using the touch interface, the stroke subtype result is displayed on the screen. Four neurology residents were randomly assigned to classify stroke subtypes using iTOAST or the conventional method (cTOAST). Using a crossover design, they classified the stroke subtypes of 70 patients. The standard subtypes were determined by three stroke experts. Correlated kappa coefficients using iTOAST compared with cTOAST were determined.
  
  
  RESULTS
  The kappa (SE) value of iTOAST [0.790 (0.041), 95% CI: 0.707-0.870] was higher than that of cTOAST [0.692 (0.046), 95% CI: 0.600-0.782] (P<0.001). Neither sequence (P=0.857) nor period effect (P=0.999) was observed.
  
  
  CONCLUSIONS
  The stroke classification tool using a handheld, computerized device was easy, accurate, and reliable over the conventional method. It may have additional benefit because a handheld, computerized device is accessible anytime and anywhere.
authors.affiliation: Department of Neurology, Yonsei University College of Medicine, Seoul, Korea.
authors.forename: H S
authors.initials: HS
authors.lastname: Nam
authors.forename: M-J
authors.initials: MJ
authors.lastname: Cha
authors.forename: Y D
authors.initials: YD
authors.lastname: Kim
authors.forename: E H
authors.initials: EH
authors.lastname: Kim
authors.forename: E
authors.initials: E
authors.lastname: Park
authors.forename: H S
authors.initials: HS
authors.lastname: Lee
authors.forename: C M
authors.initials: CM
authors.lastname: Nam
authors.forename: J H
authors.initials: JH
authors.lastname: Heo
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D012984:Software; D020521:Stroke / Q000145:classification*