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A stroke prediction score in the elderly: validation and Web-based application.

Thomas Lumley1, Richard A Kronmal, Mary Cushman

  • 1University of Washington, CHS Coordinating Center, Plaza 600 Building, Suite 700, 600 Stewart St., Seattle, WA 98101, USA. tlumley@u.washington.edu

Journal of Clinical Epidemiology
|January 26, 2002
PubMed
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Researchers developed a stroke prediction model for older adults using factors like age and blood pressure. This model effectively identifies high-risk individuals, aiding in early intervention for stroke prevention.

Area of Science:

  • Gerontology
  • Epidemiology
  • Cardiovascular Medicine

Background:

  • Stroke is a leading cause of disability and death, particularly in the elderly population.
  • Accurate prediction of stroke risk is crucial for implementing timely preventive strategies.

Purpose of the Study:

  • To develop and validate a stroke prediction model for elderly U.S. individuals.
  • To assess the performance of existing stroke prediction models in this demographic.

Main Methods:

  • Utilized data from the Cardiovascular Health Study, including 5,711 participants aged 65+ followed for 6.3 years.
  • Constructed sex-specific prediction equations using variables such as age, systolic blood pressure, diabetes, atrial fibrillation, cardiovascular disease history, gait speed, and serum creatinine.
  • Implemented the prediction rule as a risk score and a web-based interactive Java applet.

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Main Results:

  • The developed model predicted 5-year stroke risks from less than 1% to 59%.
  • The highest risk group (20% of subjects) had a 15% 5-year stroke incidence, versus 1% for the lowest risk group.
  • Previously published risk scores also demonstrated good performance in this cohort.

Conclusions:

  • A novel stroke prediction model effectively discriminates between low and high stroke risk in the elderly using readily available data.
  • The model and risk scores show promise for identifying elderly individuals at risk of stroke.
  • Further research is needed to evaluate the generalizability and clinical utility of this prediction model.