Development and validation of a risk prediction model for short-term progression of carotid atherosclerosis among early middle age adults

• Published in: Atherosclerosis 2024-10, Vol.397, p.118557, Article 118557
• Main Authors: Sun, Xinghe, Wu, Chaoqun, Kang, Junping, Lv, Hui, Liu, Xiaohui
• Format: Article
• Language: English
• Subjects: Adult; Age Factors; Carotid Artery Diseases - diagnosis; Carotid Artery Diseases - diagnostic imaging; Carotid Artery Diseases - epidemiology; Carotid atherosclerosis; Carotid Stenosis - diagnosis; Carotid Stenosis - diagnostic imaging; Carotid Stenosis - epidemiology; China - epidemiology; Disease Progression; Female; Humans; Male; Middle Aged; Physical examination; Plaque, Atherosclerotic; Predictive Value of Tests; Prognosis; Reproducibility of Results; Risk Assessment; Risk Factors; Risk prediction; Time Factors
• ISSN: 0021-9150; 1879-1484; 1879-1484
• DOI: 10.1016/j.atherosclerosis.2024.118557

We aimed at creating and validating a prognostic model incorporating easily accessible clinical and laboratory parameters to forecast the likelihood of short-term progression of carotid atherosclerosis. A prediction model was developed and validated for carotid plaque progression within 2 years in an early middle-age population in China. Progression was defined as the new appearance of carotid plaque or stenosis among participants who had normal carotid status at baseline. Leveraging data from a health check-up chain, predictors were identified using statistical methods including stepwise logistic regression, Markov Chain Monte Carlo (MCMC) simulation, random forest analysis and least absolute shrinkage selection operator (Lasso). Model performance was assessed. Bootstrap internal validation, validation on another check-up population and subgroup analysis were also conducted. Among 7765 participants, predictors including age, diastolic blood pressure, uric acid levels, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were identified for carotid plaque progression in 2 years. The developed prediction model demonstrated good discrimination (AUC = 0.755, 95%CI:0.736–0.774) and calibration ability (slope = 0.922 and interception = 0.007) among development data, as well as among validation data (AUC = 0.759, 95%CI:0.674–0.770; slope = 1.076 and intercept = −0.014). Internal validation using bootstrap method yielded an adjusted AUC of 0.753. The model's performance remained consistent across different subgroups. Our study presents a validated risk prediction model for carotid plaque progression in an early middle age population, offering a valuable tool for early identification and monitoring of cardiovascular risks. The model's robustness and applicability across different subgroups highlight its potential utility in preemptive cerebrovascular and cardiovascular disease management. [Display omitted] •Development and validation of a prognostic model for short-term progression of carotid atherosclerosis in an early middle-age Chinese population.•Easily accessible predictors were identified including age, diastolic blood pressure, uric acid levels, LDL-C, and HDL-C.•Model demonstrated good discrimination and calibration in validation data.•Internal validation yielded an adjusted AUC of 0.753, and performance was consistent across different subgroups.•The model offers a practical tool for early identification and monitor