Objective: Socioeconomic status (SES) is considered a key social determinant infuencing the development of cardiovascular disorders (CVDs). This study aims to assess the association between SES and CVD risk and to develop and validate a nomogram prediction model incorporating SES. Methods: Data were obtained from American adults enrolled in the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018. Individuals aged 18 and older with complete data on the poverty income ratio (PIR) and CVD outcomes were included. SES was measured using the PIR. Multivariable logistic regression was employed to evaluate the correlation between SES and CVD risk, while the least absolute shrinkage and selection operator (LASSO) regression was used to identify key predictors and construct the nomogram model. Data were randomly split into training and validation sets in a 7:3 ratio. Model performance and clinical utility were assessed using the area under the receiver operating characteristic curve, calibration curves, and decision curve analysis (DCA). Results: A total of 11,180 participants were included, with an overall CVD prevalence of 11.15%. Middle SES was associated with a moderately reduced risk of CVD (OR = 0.75, 95% CI: 0.62-0.91, p = 0.003), while high SES was significantly associated with a lower risk of CVD (OR = 0.52, 95% CI: 0.41-0.66, p <0.0001), compared to the low SES reference group. The nomogram model incorporating SES and other risk factors achieved an area under the curve (AUC) of 0.846 in the training set and 0.834 in the validation set, demonstrating good discrimination and calibration. DCA further confirmed the potential clinical benefits of the model in predicting CVD risk. Conclusion: SES is an important factor influencing CVD among American adults. The nomogram prediction model based on SES and other variables provides a scientific basis for individualized CVD risk assessment and optimal allocation of health resources. Further validation in diverse populations and prospective studies is warranted to confirm the model's generalizability.
[1]Shanghai Univ Med & Hlth Sci, Chongming Hosp, Dept Cardiol, Shanghai 202150, Peoples R China.
[2]Univ Sci & Technol China, Affiliated Hosp USTC 1, Dept Emergency, Div Life Sci & Med, Hefei 230000, Anhui, Peoples R China.
[3]Wannan Med Coll, Maanshan Peoples Hosp, Maanshan Hosp, Dept Cardiol, Maanshan 243000, Anhui, Peoples R China.
[4]Tongling Peoples Hosp, Dept Geriatr Ctr, Tongling 244000, Anhui, Peoples R China.
[5]Shanghai Univ Med & Hlth Sci, Chongming Hosp, Dept Gynaecol, Shanghai 202150, Peoples R China.
(8.0+极速模式)