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Measuring the Carotid to Femoral Pulse Wave Velocity Cf-PWV to Evaluate Arterial Stiffness
Published on: May 3, 2018
Does Arterial Stiffness Predict Cardiovascular Disease in Older Adults With an Intellectual Disability?
Insights
Arterial stiffness, measured by Mobil-O-Graph, is linked to cardiovascular disease (CVD) risk in individuals with intellectual disabilities. This noninvasive method can predict CVD risk, with machine learning improving accuracy.
Area of Science:
- Cardiology
- Public Health
- Intellectual Disability Research
Background:
- Arterial stiffness is a known risk factor for cardiovascular disease (CVD) in the general population.
- Individuals with intellectual disabilities often face higher risks for chronic health conditions, including CVD.
- Assessing CVD risk factors like arterial stiffness is crucial for this underserved population.
Purpose of the Study:
- To investigate the association between arterial stiffness (measured by Mobil-O-Graph) and CVD risk in individuals with intellectual disabilities.
- To determine if arterial stiffness can predict CVD risk in this population.
- To compare the predictive accuracy of traditional statistical models versus machine learning models for CVD risk.
Main Methods:
- Cross-sectional study of 58 adults with intellectual disabilities from the Irish Longitudinal Study on Aging.
- Arterial stiffness assessed using the noninvasive Mobil-O-Graph device (measuring pulse wave velocity).
- Statistical (proportional odds logistic regression) and machine learning (k-nearest neighbor, random forest) models used for risk prediction.
Main Results:
- Significant associations found between higher arterial stiffness, diabetes, and higher CVD risk scores (SCORE2).
- The Mobil-O-Graph demonstrated predictive capability for CVD risk, with logistic regression achieving ~60% accuracy.
- Machine learning models (k-NN, random forest) significantly improved CVD risk prediction accuracy to ~76-78%.
Conclusions:
- Noninvasive measurement of arterial stiffness using the Mobil-O-Graph is a viable tool for assessing CVD risk in individuals with intellectual disabilities.
- Machine learning approaches enhance the accuracy of CVD risk prediction in this population.
- Early identification and management of arterial stiffness may help mitigate CVD risk in individuals with intellectual disabilities.
Background:
Arterial stiffness has been associated with an increased risk of cardiovascular disease (CVD) in some patient populations.
Objectives:
The aims of this study were to investigate (1) whether there is an association between arterial stiffness, as measured by the Mobil-O-Graph, and risk for CVD in a population of individuals with intellectual disability and (2) whether arterial stiffness can predict the risk for CVD.
Methods:
This cross-sectional study included 58 individuals who participated in wave 4 of the Intellectual Disability Supplement to the Irish Longitudinal Study on Aging (2019-2020). Statistical models were used to address the first aim, whereas machine learning models were used to improve the accuracy of risk predictions in the second aim.
Results:
Sample characteristics were mean (SD) age of 60.69 (10.48) years, women (62.1%), mild/moderate level of intellectual disability (91.4%), living in community group homes (53.4%), overweight/obese (84.5%), high cholesterol (46.6%), alcohol consumption (48.3%), hypertension (25.9%), diabetes (17.24%), and smokers (3.4%). Mean (SD) pulse wave velocity (arterial stiffness measured by Mobil-O-Graph) was 8.776 (1.6) m/s. Cardiovascular disease risk categories, calculated using SCORE2, were low-to-moderate risk (44.8%), high risk (46.6%), and very high risk (8.6%). Using proportional odds logistic regression, significant associations were found between arterial stiffness, diabetes diagnosis, and CVD risk SCORE2 ( P < .001). We also found the Mobil-O-Graph can predict risk of CVD, with prediction accuracy of the proportional odds logistic regression model approximately 60.12% (SE, 3.2%). Machine learning models, k -nearest neighbor, and random forest improved model predictions over and above proportional odds logistic regression at 75.85% and 77.7%, respectively.
Conclusions:
Arterial stiffness, as measured by the noninvasive Mobil-O-Graph, can be used to predict risk of CVD in individuals with intellectual disabilities.
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