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Author Spotlight: Assessing Ischemic Stroke Damage Through Middle Cerebral Artery Occlusion Model
Published on: August 11, 2023
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Age Specific Models to Capture the Change in Risk Factor Contribution by Age to Short Term Primary Ischemic Stroke
Elizabeth Hunter1, John D Kelleher1,2
1PRECISE4Q Predictive Modelling in Stroke, Technological University Dublin, Dublin, Ireland.
Frontiers in Neurology
|March 7, 2022
Summary
Age significantly impacts stroke risk, but its inclusion in prediction models presents challenges. This study found age-specific stroke risk factors improve prediction accuracy compared to models including age directly.
Area of Science:
- Cardiovascular Epidemiology
- Biostatistics
- Public Health
Background:
- Age is a primary determinant of stroke risk.
- Current stroke prediction models face challenges incorporating age due to its dominant effect and non-proportional contribution of other risk factors.
Purpose of the Study:
- To investigate the proportional contribution of common stroke risk factors across different age groups.
- To develop and evaluate age-stratified stroke risk prediction models.
Main Methods:
- Utilized Framingham Heart Study data.
- Employed logistic regression to build age-specific stroke risk models.
- Compared the calibration of age-stratified models against a general model.
Main Results:
- Evidence of non-proportionality in stroke risk factor contributions by age was found.
- Age-stratified logistic regression models demonstrated improved calibration for 5-year stroke risk prediction.
- These models outperformed a single model incorporating age as a continuous variable.
Conclusions:
- Standard stroke risk models may be improved by accounting for age-related non-proportionality.
- Developing age-specific prediction models offers a more accurate approach to stroke risk assessment.
- Future research should focus on methods that better integrate age into stroke risk prediction.
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