Application of a decision tree to establish factors associated with a nomogram of aortic stiffness
Key result
A decision tree model identified determinants of the pulse wave velocity index with 70% sensitivity, 78% specificity, and 0.73 accuracy.
Why the study?
The study was conducted to investigate the determinants of the aortic pulse wave velocity (PWV) index by applying a decision tree.
Population
597 individuals
Design
Cross-sectional study
Authors
Discussion
Loading...
Member takes
Implication
PWV index integrates established CV risks; leaves open its utility as composite marker pending prospective validation.
Study Design
Type
Cross-Sectional (n=597)
Structured PICO
P
Population
597 individuals evaluated between 2012 and 2017 to investigate the determinants of the pulse wave velocity index using a decision tree model.
E
Exposure
Decision tree model using clinical and biochemical parameters to identify determinants of pulse wave velocity (PWV) index
O
Outcome
Determinants of the PWV index and model performance (sensitivity, specificity, accuracy)surrogate
Main Result
Effect estimate: Accuracy 0.73
A decision tree model demonstrated that the PWV index is determined by well-accepted cardiovascular risk factors, suggesting its utility as an integrated marker of cardiovascular risk exposure.
Cite This Study
Vallée et al. (2019) conducted a cross-sectional in Aortic stiffness (n=597). Clinical and biochemical parameters was evaluated on Performance of the decision tree model for identifying related risk factors of PWV index (Accuracy 0.73). A decision tree model identified determinants of the pulse wave velocity index with 70% sensitivity, 78% specificity, and 0.73 accuracy.