A Novel Computerized Clinical Decision Support System for Treating Thrombolysis in Patients with Acute Ischemic Stroke
0
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
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.
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.
참고문헌 (28)
- Adeoye O, "Recombinant tissue-type plasminogen activator use for ischemic stroke in the United States: a doubling of treatment rates over the course of 5 years", Stroke, 42, pp.1952~1955 (2011)
- Rudd AG, "Stroke thrombolysis in England, Wales and Northern Ireland: how much do we do and how much do we need?", J Neurol Neurosurg Psychiatry, 82, pp.14~19 (2011)
- Health Insurance Review Agency in Korea, "Quality Assessment for Stroke Treatment in Korea", ~ (2010)
- Eissa A, "Barriers to the utilization of thrombolysis for acute ischaemic stroke", J Clin Pharm Ther, 37, pp.399~409 (2012)
- Brown DL, "Survey of emergency physicians about recombinant tissue plasminogen activator for acute ischemic stroke", Ann Emerg Med, 46, pp.56~60 (2005)
- Steyerberg EW, "Prognosis research strategy (PROGRESS) 3: prognostic model research", PLoS Med, 10, pp.e1001381~ (2013)
- Garg AX, "Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review", JAMA, 293, pp.1223~1238 (2005)
- Streiff MB, "Lessons from the Johns Hopkins Multi-disciplinary Venous Thromboembolism (VTE) Prevention Collaborative", BMJ, 344, pp.e3935~ (2012)
- Kent DM, "The stroke-thrombolytic predictive instrument: a predictive instrument for intravenous thrombolysis in acute ischemic stroke", Stroke, 37, pp.2957~2962 (2006)
- Strbian D, "Predicting outcome of IV thrombolysis-treated ischemic stroke patients: the dragon score", Neurology, 78, pp.427~432 (2012)
- Turc G, "Magnetic resonance imaging-dragon score: 3-month outcome prediction after intravenous thrombolysis for anterior circulation stroke", Stroke, 44, pp.1323~1328 (2013)
- Strbian D, "Symptomatic intracranial hemorrhage after stroke thrombolysis: the sedan score", Ann Neurol, 71, pp.634~641 (2012)
- Menon BK, "Risk score for intracranial hemorrhage in patients with acute ischemic stroke treated with intravenous tissue-type plasminogen activator", Stroke, 43, pp.2293~2299 (2012)
- Lee BC, "Korean Stroke Registry. International experience in stroke registries: Korean Stroke Registry", Am J Prev Med, 31, pp.243~245 (2006)
- Park JH, "Is asymptomatic hemorrhagic transformation really innocuous?", Neurology, 78, pp.421~426 (2012)
- Hong KS, "Impact of neurological and medical complications on 3-month outcomes in acute ischaemic stroke", Eur J Neurol, 15, pp.1324~1331 (2008)
- Weimar C, "Neurologic worsening during the acute phase of ischemic stroke", Arch Neurol, 62, pp.393~397 (2005)
- Kim BJ, "Current status of acute stroke management in Korea: a report on a multicenter, comprehensive acute stroke registry.", Int J Stroke, 9, pp.514~518 (2014)
- Tibshirani R, "Regression and shrinkage via the Lasso", J R Stat Ser B, 58, pp.267~288 (1996)
- Tibshirani R, "The Lasso method for variable selection in the Cox model", Stat Med, 16, pp.385~395 (1997)
- Harrell FE, "Multivariable prognostic models: Issues in developing models, evaluating assumptions and accuracy, and measuring and reducing errors", Stat Med, 15, pp.361~387 (1996)
- Kumar S, "Antiplatelets, ace inhibitors, and statins combination reduces stroke severity and tissue at risk", Neurology, 66, pp.1153~1158 (2006)
- Chitravas N, "Is prestroke use of angiotensin-converting enzyme inhibitors associated with better outcome?", Neurology, 68, pp.1687~1693 (2007)
- Yu AY, "Pre-stroke use of antihypertensives, antiplatelets, or statins and early ischemic stroke outcomes", Cerebrovasc Dis, 27, pp.398~402 (2009)
- del Zoppo GJ, "Recombinant tissue plasminogen activator in acute thrombotic and embolic stroke", Ann Neurol, 32, pp.78~86 (1992)
- Steyerberg EW, "Clinical prediction models. A practical approach to development, validation, and updating" (2009), Springer
- Mirnezami R, "Preparing for precision medicine", N Engl J Med, 366, pp.489~491 (2012)
- "Moving toward precision medicine. Editorial", Lancet, 378, pp.1678~ (2011)