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Published on: May 3, 2018
Arterial stiffness and biological parameters: A decision tree machine learning application in hypertensive
1Department of Epidemiology and Public Health, Foch hospital, Suresnes, France.
Insights
Arterial stiffness index (ASI) can identify target organ damage in hypertension. Key determinants include HDL cholesterol, smoking, and phosphate levels, aiding cardiovascular risk management.
Area of Science:
- Cardiovascular Medicine
- Biostatistics
- Preventive Cardiology
Background:
- Arterial stiffness is a key indicator of target organ damage in hypertensive individuals.
- No established normal reference values currently exist for the arterial stiffness index (ASI).
- ASI quantifies arterial stiffness by comparing measured to predicted values, with a positive index indicating stiffness.
Purpose of the Study:
- To identify the primary determinants of the stiffness index in hypertensive patients without cardiovascular disease.
- To establish threshold values for discriminating positive and negative stiffness index.
- To elucidate the hierarchical associations between these determinants using a decision tree model.
Main Methods:
- Predicted ASI was determined from 53,363 healthy UK Biobank participants.
- Stiffness index was calculated for 49,452 hypertensive individuals without cardiovascular disease.
- A decision tree model analyzed clinical and biological parameters to rank classifiers by sensitivity and specificity.
Main Results:
- The most sensitive classifiers for positive stiffness index were HDL cholesterol ≤1.425 mmol/L, smoking pack years ≥9.2, and Phosphate ≥1.172 mmol/L.
- Specific classifiers included Cystatin c ≤0.901 mg/L, Triglycerides ≥1.487 mmol/L, and Urate ≥291.9 μmol/L.
- The decision tree model demonstrated superior performance (p<0.001) compared to logistic regression in classifying stiffness index.
Conclusions:
- The stiffness index integrates multiple cardiovascular risk factors, offering potential for enhanced risk evaluation.
- Decision tree models provide accurate and clinically useful classification of arterial stiffness determinants.
- Findings support the use of stiffness index and decision trees in future cardiovascular risk management and preventive strategies.
Abstract:
Arterial stiffness, measured by arterial stiffness index (ASI), could be considered a main denominator in target organ damage among hypertensive subjects. Currently, no reported ASI normal references have been reported. The index of arterial stiffness is evaluated by calculation of a stiffness index. Predicted ASI can be estimated regardless to age, sex, mean blood pressure, and heart rate, to compose an individual stiffness index [(measured ASI-predicted ASI)/predicted ASI]. A stiffness index greater than zero defines arterial stiffness. Thus, the purpose of this study was 1) to determine determinants of stiffness index 2) to perform threshold values to discriminate stiffness index and then 3) to determine hierarchical associations of the determinants by performing a decision tree model among hypertensive participants without CV diseases. A study was conducted from 53,363 healthy participants in the UK Biobank survey to determine predicted ASI. Stiffness index was applied on 49,452 hypertensives without CV diseases to discriminate determinants of positive stiffness index (N = 22,453) from negative index (N = 26,999). The input variables for the models were clinical and biological parameters. The independent classifiers were ranked from the most sensitives: HDL cholesterol≤1.425 mmol/L, smoking pack years≥9.2pack-years, Phosphate≥1.172 mmol/L, to the most specifics: Cystatin c≤0.901 mg/L, Triglycerides≥1.487 mmol/L, Urate≥291.9 μmol/L, ALT≥22.13 U/L, AST≤32.5 U/L, Albumin≤45.92 g/L, Testosterone≥5.181 nmol/L. A decision tree model was performed to determine rules to highlight the different hierarchization and interactions between these classifiers with a higher performance than multiple logistic regression (p<0.001). The stiffness index could be an integrator of CV risk factors and participate in future CV risk management evaluations for preventive strategies. Decision trees can provide accurate and useful classification for clinicians.
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