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Published on: September 16, 2022
Development and Internal Validation of a Risk Prediction Model for Carotid Atherosclerosis in the Hyperuricemia
Ximisinuer Tusongtuoheti1,2, Guoqing Huang1,2, Yushan Mao1
1Department of Endocrinology, The First Affiliated Hospital of Ningbo University, Ningbo University, Ningbo, People's Republic of China.
Insights
This study identified key risk factors for carotid atherosclerosis (CAS) in individuals with hyperuricemia (HUA). A new prediction model aids in early CAS detection for this population.
Area of Science:
- Cardiovascular Medicine
- Nephrology
- Metabolic Disorders
Background:
- Hyperuricemia (HUA) is associated with increased cardiovascular risk.
- Carotid atherosclerosis (CAS) is a significant indicator of cerebrovascular events.
- Identifying specific risk factors for CAS in HUA populations is crucial for preventative strategies.
Purpose of the Study:
- To identify independent risk factors for CAS in patients with HUA.
- To develop and validate a predictive model for CAS risk in this specific demographic.
- To enhance early identification and management of CAS in HUA individuals.
Main Methods:
- Retrospective analysis of 3579 HUA individuals.
- Carotid ultrasonography for CAS assessment.
- Multivariable logistic regression and LASSO for risk factor identification and model construction.
- Validation using ROC, calibration curves, and decision curve analysis.
Main Results:
- Identified sex, age, mean red blood cell volume, and fasting blood glucose as independent CAS risk factors in HUA.
- Developed a risk prediction model incorporating age, GGT, serum creatinine, fasting blood glucose, T3, and direct bilirubin.
- The model demonstrated excellent discriminative ability (AUC 0.891-0.901) and good calibration.
Conclusions:
- A validated risk prediction model for CAS in HUA populations has been developed.
- This model facilitates early identification of individuals at high risk for CAS.
- The findings contribute to improved cardiovascular risk management in HUA patients.
Purpose:
The aim of this study was to identify independent risk factors for carotid atherosclerosis (CAS) in a population with hyperuricemia (HUA) and develop a CAS risk prediction model.
Patients And Methods:
This retrospective study included 3579 HUA individuals who underwent health examinations, including carotid ultrasonography, at the Zhenhai Lianhua Hospital in Ningbo, China, in 2020. All participants were randomly assigned to the training and internal validation sets in a 7:3 ratio. Multivariable logistic regression analysis was used to identify independent risk factors associated with CAS. The characteristic variables were screened using the least absolute shrinkage and selection operator combined with 10-fold cross-validation, and the resulting model was visualized by a nomogram. The discriminative ability, calibration, and clinical utility of the risk model were validated using the receiver operating characteristic curve, calibration curve, and decision curve analysis.
Results:
Sex, age, mean red blood cell volume, and fasting blood glucose were identified as independent risk factors for CAS in the HUA population. Age, gamma-glutamyl transpeptidase, serum creatinine, fasting blood glucose, total triiodothyronine, and direct bilirubin, were screened to construct a CAS risk prediction model. In the training and internal validation sets, the risk prediction model showed an excellent discriminative ability with the area under the curve of 0.891 and 0.901, respectively, and a high level of fit. Decision curve analysis results demonstrated that the risk prediction model could be beneficial when the threshold probabilities were 1-87% and 1-100% in the training and internal validation sets, respectively.
Conclusion:
We developed and internally validated a risk prediction model for CAS in a population with HUA, thereby contributing to the CAS early identification.
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