Vascular Health and Risk Management (Apr 2024)
Development and Internal Validation of a Risk Prediction Model for Carotid Atherosclerosis in the Hyperuricemia Population
Abstract
Ximisinuer Tusongtuoheti,1,2 Guoqing Huang,1,2 Yushan Mao1 1Department of Endocrinology, The First Affiliated Hospital of Ningbo University, Ningbo University, Ningbo, People’s Republic of China; 2Health Science Center, Ningbo University, Ningbo, People’s Republic of ChinaCorrespondence: Guoqing Huang; Yushan Mao, Department of Endocrinology, The First Affiliated Hospital of Ningbo University, 247 Renmin Road, Ningbo, People’s Republic of China, Tel +86 15737939838; +86 13867878937, Email [email protected]; [email protected]: 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.Keywords: hyperuricemia, carotid atherosclerosis, independent risk factors, prediction model, nomogram
