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Self-reported transient ischemic attack and stroke symptoms: methods and baseline prevalence. The ARIC Study,
J F Toole1, D S Lefkowitz, L E Chambless
1Stroke Research Center, Bowman Gray School of Medicine, Wake Forest University, Winston-Salem, NC 27157-1068, USA.
American Journal of Epidemiology
|November 1, 1996
Summary
A computerized algorithm identified few TIA/stroke symptoms among ARIC study participants reporting neurological events. Speech dysfunction was most often classified as TIA/stroke, with higher prevalence in females and older adults.
Area of Science:
- Cardiovascular Epidemiology
- Neurology
- Biostatistics
Background:
- Atherosclerosis Risk in Communities (ARIC) Study investigates atherosclerosis.
- Standardized tools are needed to assess transient ischemic attack (TIA) and stroke symptoms.
- Accurate identification of cerebrovascular events is crucial for public health.
Purpose of the Study:
- To develop and test a diagnostic algorithm for identifying TIA and stroke symptoms.
- To evaluate the prevalence of TIA/stroke symptoms in a large cohort.
- To examine demographic and clinical factors associated with TIA/stroke symptoms.
Main Methods:
- Developed a computerized diagnostic algorithm based on neurologic trigger symptoms.
- Administered a standardized questionnaire to 12,205 ARIC participants.
- Classified reported symptoms using the algorithm to identify TIA or stroke events.
Main Results:
- Nearly half (47%) of participants reporting cerebrovascular symptoms experienced sudden onset.
- Only 12.9% of those reporting symptoms were classified by the algorithm as TIA/stroke.
- Speech dysfunction symptoms were most frequently classified as TIA/stroke (77%).
- TIA/stroke symptoms were more frequent in females (7%) than males (5%) and increased with age.
- African Americans in Forsyth County reported more TIA/stroke symptoms than Caucasians.
Conclusions:
- A significant gap exists between self-reported neurological symptoms and algorithmically defined TIA/stroke events.
- The developed algorithm provides a standardized method for TIA/stroke symptom assessment.
- Further research is needed to understand the association between algorithmically defined TIA/stroke symptoms and risk factors.