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Published on: September 27, 2024
Coronary artery calcification, intima-media thickness, and ankle-brachial index are complementary stroke predictors
Janine Gronewold1, Marcus Bauer1, Nils Lehmann1
1From the Departments of Neurology (J.G., C.W., D.M.H.) and Cardiology (M.B., A.A.M., H.K., R.E.), University Hospital Essen, Essen, Germany; Institute of Medical Informatics, Biometry, and Epidemiology, University of Duisburg-Essen, Essen, Germany (N.L., S.M., K.-H.J.); and Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany (K.B.).
Insights
Coronary artery calcification (CAC) and ankle-brachial index (ABI) together improve stroke prediction beyond traditional risk factors. Combining CAC with ABI offers significant stroke risk assessment enhancement.
Area of Science:
- Cardiovascular Medicine
- Atherosclerosis Research
- Stroke Prevention
Background:
- Coronary artery calcification (CAC) is a known stroke predictor.
- The added value of peripheral atherosclerosis markers, carotid intima-media thickness (CIMT) and ankle-brachial index (ABI), to CAC for stroke prediction was unclear.
Purpose of the Study:
- To investigate if combining CAC with CIMT and ABI improves stroke risk prediction.
- To assess the incremental predictive value of these markers beyond established risk factors.
Main Methods:
- Utilized data from 3289 participants (45-75 years) in the Heinz Nixdorf Recall study.
- Assessed incident stroke over a 9.0±1.9 year follow-up period.
- Employed multivariable Cox regression and Harrell's c-statistics to analyze CAC, CIMT, and ABI as stroke predictors.
Main Results:
- CAC, CIMT, and ABI were independently associated with increased stroke risk.
- Combining CAC with ABI significantly improved the area under the curve (P=0.047) and reclassification metrics.
- ABI demonstrated the highest predictive value, while CIMT had the lowest.
Conclusions:
- CAC, CIMT, and ABI provide complementary information for stroke risk assessment.
- The combination of CAC and ABI offers enhanced stroke risk prediction, particularly in specific subpopulations.
- ABI is a valuable, distinctive marker for stroke risk stratification.
Background And Purpose:
Coronary artery calcification (CAC), a marker of coronary atherosclerosis, predicts stroke in addition to established risk factors. Whether CAC's predictive value can be improved by peripheral atherosclerosis markers, namely carotid intima-media thickness (CIMT) and ankle-brachial index (ABI), was unknown.
Methods:
A total of 3289 participants of the population-based Heinz Nixdorf Recall study (45-75 years; 48.8% men) without previous stroke or coronary heart disease were evaluated for incident stroke for 9.0±1.9 years. CAC, CIMT, and ABI were examined as stroke predictors.
Results:
Eighty-four strokes occurred during follow-up. In multivariable Cox proportional hazard regressions, CAC (hazard ratio, 1.45 [95% confidence interval, 1.11-1.88] per SD increase in ln(CAC+1); SD, 2.40), CIMT (1.34 [1.08-1.66] per SD increase; SD, 0.127 mm), and ABI (1.55 [1.32-1.82] per SD decrease; SD, 0.148) were associated with stroke in addition to established risk factors. When combined with each other, ln(CAC+1)'s hazard ratio remained similar when CIMT (1.41 [1.09-1.83]) was inserted into the multivariable model, but slightly decreased when ABI (1.31 [1.01-1.72]) or CIMT and ABI (1.29 [0.99-1.68]) were included. Although CAC alone did not significantly elevate the area under the curve in Harrell's c-statistics (by 0.009; P=0.379) in addition to established risk factors, the combination of CAC and ABI increased area under the curve (by 0.029; P=0.047), as did ABI (by 0.025; P=0.038) but not CIMT (by 0.002; P=0.795) alone. The combination of CAC and ABI also resulted in significant category-free net reclassification and integrated discrimination improvement.
Conclusions:
CAC, CIMT, and ABI provide complementary information about stroke risk. ABI, which is distinctive in a small subpopulation, had the highest and CIMT, which is distributed across a larger range of values, had the lowest predictive value.
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