title: A Novel Computerized Clinical Decision Support System for Treating Thrombolysis in Patients with Acute Ischemic Stroke.
abstract: OBJECTIVE
  Thrombolysis is underused in acute ischemic stroke, mainly due to the reluctance of physicians to treat thrombolysis patients. However, a computerized clinical decision support system can help physicians to develop individualized stroke treatments.
  
  
  METHODS
  A consecutive series of 958 patients, hospitalized within 12 hours of ischemic stroke onset from a representative clinical center in Korea, was used to establish a prognostic model. Multivariable logistic regression was used to develop the model for global and safety outcomes. An external validation of developed model was performed using 954 patients data obtained from 5 university hospitals or regional stroke centers.
  
  
  RESULTS
  Final global outcome predictors were age; previous modified Rankin scale score; initial National Institutes of Health Stroke Scale (NIHSS) score; previous stroke; diabetes; prior use of antiplatelet treatment, antihypertensive drugs, and statins; lacunae; thrombolysis; onset to treatment time; and systolic blood pressure. Final safety outcome predictors were age, initial NIHSS score, thrombolysis, onset to treatment time, systolic blood pressure, and glucose level. The discriminative ability of the prognostic model showed a C-statistic of 0.89 and 0.84 for the global and safety outcomes, respectively. Internal and external validation showed similar C-statistic results. After updating the model, calibration slopes were corrected from 0.68 to 1.0 and from 0.96 to 1.0 for the global and safety outcome models, respectively.
  
  
  CONCLUSIONS
  A novel computerized outcome prediction model for thrombolysis after ischemic stroke was developed using large amounts of clinical information. After external validation and updating, the model's performance was deemed clinically satisfactory.
authors.affiliation: Clinical Research Center, Asan Medical Center, Seoul, Korea.
authors.forename: Ji Sung
authors.initials: JS
authors.lastname: Lee
authors.affiliation: Department of Neurology, Seoul National University Hospital, Seoul, Korea.
authors.forename: Chi Kyung
authors.initials: CK
authors.lastname: Kim
authors.affiliation: Department of Neurology, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Korea.
authors.forename: Jihoon
authors.initials: J
authors.lastname: Kang
authors.affiliation: Department of Neurology, Eulji General Hospital, Eulji University, Seoul, Korea.
authors.forename: Jong-Moo
authors.initials: JM
authors.lastname: Park
authors.affiliation: Department of Neurology, Seoul Medical Center, Seoul, Korea.
authors.forename: Tai Hwan
authors.initials: TH
authors.lastname: Park
authors.affiliation: Department of Neurology, Soonchunhyang University Hospital Seoul, Seoul, Korea.
authors.forename: Kyung Bok
authors.initials: KB
authors.lastname: Lee
authors.affiliation: Department of Neurology, Eulji University Hospital, Daejeon, Korea.
authors.forename: Soo Joo
authors.initials: SJ
authors.lastname: Lee
authors.affiliation: Department of Neurology, Ilsan Paik Hospital, Inje University, Goyang, Korea.
authors.forename: Yong-Jin
authors.initials: YJ
authors.lastname: Cho
authors.affiliation: Human-Computer Interaction Lab, Department of Computer Science and Engineering, Seoul National University, Seoul, Korea.
authors.forename: Jaehee
authors.initials: J
authors.lastname: Ko
authors.affiliation: Human-Computer Interaction Lab, Department of Computer Science and Engineering, Seoul National University, Seoul, Korea.
authors.forename: Jinwook
authors.initials: J
authors.lastname: Seo
authors.affiliation: Department of Neurology, Seoul National University Bundang Hospital, Seongnam, Korea.
authors.forename: Hee-Joon
authors.initials: HJ
authors.lastname: Bae
authors.affiliation: Department of Biostatistics, Korea University College of Medicine, Seoul, Korea.
authors.forename: Juneyoung
authors.initials: J
authors.lastname: Lee
keywords: Acute ischemic stroke; Clinical decision support system; Prediction model; Thrombolysis