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Development and Validation of a Prediction Model for Elevated Arterial Stiffness in Chinese Patients With Diabetes
Qingqing Li1, Wenhui Xie1, Liping Li1
1Fujian Key Laboratory of Vascular Aging, Department of Geriatrics, Department of Cardiology, Department of Cardiac Surgery, Fujian Heart Disease Center, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, China.
Insights
Machine learning accurately predicts arterial stiffness, a key cardiovascular disease risk factor, especially in diabetics. A user-friendly tool is now available for clinical use.
Area of Science:
- Cardiovascular disease research
- Biomedical data science
- Clinical prediction modeling
Background:
- Arterial stiffness, measured by pulse wave velocity, is a significant risk factor for cardiovascular diseases.
- Diabetics exhibit a high incidence of cardiovascular events.
- A clinical prediction model for elevated arterial stiffness using machine learning is needed to identify high-risk individuals.
Purpose of the Study:
- To develop and validate a machine learning-based clinical prediction model for elevated arterial stiffness.
- To identify key predictors of arterial stiffness in a clinical population.
- To create an accessible tool for clinical application of the prediction model.
Main Methods:
- Feature selection was performed using Least Absolute Shrinkage and Selection Operator and Support Vector Machine-Recursive Feature Elimination.
- Four machine learning algorithms were employed to construct the prediction model.
- Model performance was evaluated using the area under the receiver operating characteristic curve in discovery and validation cohorts.
Main Results:
- The gradient boosting model demonstrated superior performance in predicting elevated arterial stiffness.
- Key predictors identified include age, systolic blood pressure, diastolic blood pressure, and body mass index.
- The model achieved good discrimination capacity in both discovery and validation cohorts, with a cutoff of 0.46 offering a favorable sensitivity and specificity trade-off.
Conclusions:
- The gradient boosting-based prediction system effectively classifies individuals with elevated arterial stiffness.
- The developed web online tool enhances the accessibility of the gradient boosting model for clinical studies and practice.
Background:
Arterial stiffness assessed by pulse wave velocity is a major risk factor for cardiovascular diseases. The incidence of cardiovascular events remains high in diabetics. However, a clinical prediction model for elevated arterial stiffness using machine learning to identify subjects consequently at higher risk remains to be developed.
Methods:
Least absolute shrinkage and selection operator and support vector machine-recursive feature elimination were used for feature selection. Four machine learning algorithms were used to construct a prediction model, and their performance was compared based on the area under the receiver operating characteristic curve metric in a discovery dataset (n = 760). The model with the best performance was selected and validated in an independent dataset (n = 912) from the Dryad Digital Repository (https://doi.org/10.5061/dryad.m484p). To apply our model to clinical practice, we built a free and user-friendly web online tool.
Results:
The predictive model includes the predictors: age, systolic blood pressure, diastolic blood pressure, and body mass index. In the discovery cohort, the gradient boosting-based model outperformed other methods in the elevated arterial stiffness prediction. In the validation cohort, the gradient boosting model showed a good discrimination capacity. A cutoff value of 0.46 for the elevated arterial stiffness risk score in the gradient boosting model resulted in a good specificity (0.813 in the discovery data and 0.761 in the validation data) and sensitivity (0.875 and 0.738, respectively) trade-off points.
Conclusion:
The gradient boosting-based prediction system presents a good classification in elevated arterial stiffness prediction. The web online tool makes our gradient boosting-based model easily accessible for further clinical studies and utilization.
