A Novel Computerized Clinical Decision Support System for Treating Thrombolysis in Patients with Acute Ischemic Stroke.
10.5853/jos.2015.17.2.199
- Author:
Ji Sung LEE
1
;
Chi Kyung KIM
;
Jihoon KANG
;
Jong Moo PARK
;
Tai Hwan PARK
;
Kyung Bok LEE
;
Soo Joo LEE
;
Yong Jin CHO
;
Jaehee KO
;
Jinwook SEO
;
Hee Joon BAE
;
Juneyoung LEE
Author Information
1. Clinical Research Center, Asan Medical Center, Seoul, Korea.
- Publication Type:Original Article
- Keywords:
Acute ischemic stroke;
Clinical decision support system;
Prediction model;
Thrombolysis
- MeSH:
Antihypertensive Agents;
Blood Pressure;
Calibration;
Glucose;
Hospitals, University;
Humans;
Hydroxymethylglutaryl-CoA Reductase Inhibitors;
Korea;
Logistic Models;
National Institutes of Health (U.S.);
Stroke*
- From:Journal of Stroke
2015;17(2):199-209
- CountryRepublic of Korea
- Language:English
-
Abstract:
BACKGROUND AND PURPOSE: 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.
