ASCVD 10-Year Visualization
Graphically analyze the 10-year ACC/AHA atherosclerotic cardiovascular disease (ASCVD) Pooled Cohort Equations
ClinCalc.com » Cardiology » Interactive Visualization Tool for 10-Year ASCVD Risk
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About This Calculator
This graphing tool is intended for advanced clinicians wanting to visualize (and geek out) on the ACC/AHA Pooled Cohort Equations 10-Year ASCVD (atherosclerotic cardiovascular disease) risk calculations.1 If you are looking to calculate the risk for an individual patient, check out our standard 10-Year ASCVD Calculator.
The Black Box Phenomenon
The Pooled Cohort Equations are a collection of complex linear regression models with approximately 13 coefficients to calculate a patient's 10-year ASCVD risk. Unlike other risk models where a clinician can hand calculate, this model is too complex. Because of this complexity, a "black box" phenomenon is introduced -- a patient's risk factors are fed into a "black box" and an ASCVD risk is magically produced. The clinician is left with a poor understanding of how the calculation is produced and the relative weight or importance of each risk factor.
ASCVD visualization can produce interesting patterns in the Pooled Cohort Equations that may have otherwise gone unnoticed.
As an example, black female patients with low HDL cholesterol express particularly high ASCVD risk early in life that actually DECLINES with age. All other models have increasing ASCVD risk as age increases:
TC 245 mg/dL, HDL 20 mg/dL, systolic BP 186 mmHg (treated)
Another interesting phenomenon is a "U"-shaped curve as age increases with non-black females, which is exaggerated with elevated risk factors:
TC 224 mg/dL, HDL 20 mg/dL, systolic BP 154 mmHg (treated), smoker, diabetic
These patterns are interesting because they do not make intuitive sense. Given the complexity of the Pooled Cohort regression models, these effects could be an unintended consequence or a true effect. Regardless, graphical visualization allows clinicians interested in the model to view these patterns and be aware that there may be limitations as patients approach the maximums of the risk factor ranges (such as very low HDL).
References and Additional Reading