Application of a decision tree to establish factors associated with a nomogram of aortic stiffness. Issue 10 (3rd September 2019)

• Record Type: Journal Article
• Authors: Vallée, Alexandre; Safar, Michel E.; Blacher, Jacques
• Is Part Of: Journal of clinical hypertension. Volume 21:Issue 10(2019)
• Page Start: 1484
• Page End: 1492
• Publication Date: 2019-09-03
• Subjects: aortic stiffness -- data mining -- decision tree -- diabetes -- hypertension -- pulse wave velocity; Hypertension -- Periodicals; 616.132
• DOI: 10.1111/jch.13662
• Languages: English
• ISSNs: 1524-6175

Abstract

Abstract: Aortic stiffness is a marker of vascular aging and may reflect occurrence of cardiovascular (CV) diseases. Aortic pulse wave velocity (PWV), a marker of aortic stiffness, can be measured by applanation tonometry. A nomogram of aortic stiffness was evaluated by the calculation of PWV index. Theoretical PWV can be calculated according to age, gender, mean blood pressure, and heart rate, allowing to form an individual PWV index [(measured PWV – theoretical PWV)/theoretical PWV]. The purpose of the present cross‐sectional study was to investigate the determinants of the PWV index, by applying a decision tree. A cross‐sectional study was conducted from 2012 to 2017, and 597 individuals were included. A training decision tree was constructed based on seventy percent of these subjects (N = 428). The remaining 30% (N = 169) were used as the testing dataset to evaluate the performance of the decision trees. The input variables for the models were clinical and biochemical parameters. The different input variables remained in the model were diabetes, tobacco status, carotid plaque, albuminuria, C‐reactive protein, total cholesterol, BMI, and previous CV diseases. For the validation decision model, the sensitivity, specificity, and accuracy values for identifying the related risk factors of PWV index were 70%, 78%, and 0.73. Since determinants of PWV index were all well‐accepted CV risk factors, a nomogram of aortic stiffness could be considered as an integrator of CV risk factors on their duration of exposure and could be utilized to develop future programs for CV risk assessment and reduction strategies.