Key result
A five-factor risk model demonstrates good discrimination for 2-year carotid plaque progression with AUC ~0.76.
Why the study?
The authors aimed to create and validate a prognostic model using accessible clinical and laboratory parameters to forecast short-term carotid atherosclerosis progression.
Can a risk prediction model using clinical and laboratory parameters forecast the likelihood of short-term progression of carotid atherosclerosis in early middle-age adults?
Cohort (n=7,765)
Can a risk prediction model using clinical and laboratory parameters forecast the likelihood of short-term progression of carotid atherosclerosis in early middle-age adults?
Effect estimate: AUC 0.755 (95% CI 0.736-0.774)
A newly developed risk prediction model using five routine clinical parameters can accurately forecast the 2-year progression of carotid atherosclerosis in early middle-age adults.
May aid risk stratification; leaves open external validation before clinical adoption.
AIM: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
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Sun et al. (2024) conducted a cohort in Carotid atherosclerosis (n=7,765). Risk prediction model was evaluated on Carotid plaque progression within 2 years (AUC 0.755, 95% CI 0.736-0.774). A risk prediction model incorporating age, diastolic blood pressure, uric acid, HDL-C, and LDL-C demonstrated good discrimination for 2-year carotid plaque progression (AUC 0.755; 95% CI 0.736-0.774).
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