ArticleVascular health and risk management2025
Constructing a Predictive Model to Evaluate the Risk of CHD Based on New Metabolic Indicators.
Article in Vascular health and risk management, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Development and validation of a population-based prediction model for prevalent hypertension: evidence from Qatar biobank.Frontiers in cardiovascular medicine · 2026Article
- Association of cardiometabolic index and glomerular filtration rate with sleep disorders: NHANES 2005-2020.Journal of health, population, and nutrition · 2025Article
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Authors and funding
3 authors.
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Abstract
Objective: Constructing a predictive model to evaluate the risk of coronary heart disease (CHD) for early identification of patients with CHD risk based on new metabolic indicators. Methods: A retrospective analysis was conducted based on NHANES databases. Collect general information, cardiovascular comorbidities, new metabolic indicators (BMI, Triglycerides/Glucose, Waist Circumference-to-Height ratio, Cholesterol/HDL, Triglycerides/HDL, Cardiometabolic index, Neutrophil percentage-to-albumin ratio, etc). The least absolute shrinkage and selection operator (LASSO) regression model and multivariate logistic regression were performed to analyze the risk factors of CHD and develop a CHD risk predictive model using R software. Results: A total of 3741 individuals were included and 160 (4.3%) individuals had CHD. According to the results of the LASSO regression model and multivariate logistic regression, 9 factors were related to CHD such as Hypertension (Yes), Cardiometabolic index (≥0.672), Mean arterial pressure (<70 mmHg), Gender (male), COPD (Yes), Age (>69), Neutrophil percentage-to-albumin ratio (≥1.465), Thyroid problem (Yes) and Stroke (Yes), which were developed a CHD risk prediction nomogram. The nomogram presented good discrimination with a C-index value of 0.869 (95% confidence interval: 0.82196-0.91604), AUC (0.868) and good calibration. Based on the maximum point of the Youden index, the individuals with a score greater than 136.5 are at high risk for CHD. Conclusion: A risk prediction model for CHD has been developed based on new metabolic indicators in this study and boasts a relatively high accuracy in the early identification of patients with CHD risk. It may help clinicians develop strategies to prevent CHD and improve care quality.
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