Constructing a Predictive Model to Evaluate the Risk of CHD Based on New Metabolic Indicators
Wenqiang Wang,1 Zonghan Du,2 Peng Xie3 1Department of Nursing, Beijing Anzhen Nanchong Hospital of Capital Medical University & Nanchong Central Hospital, Nanchong, Sichuan, 637000, People’s Republic of China; 2Department of Gastroenterology, Beijing Anzhen Nanchong Hospital of Capital Medic...
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Dove Medical Press
2025-05-01
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| description | Wenqiang Wang,1 Zonghan Du,2 Peng Xie3 1Department of Nursing, Beijing Anzhen Nanchong Hospital of Capital Medical University & Nanchong Central Hospital, Nanchong, Sichuan, 637000, People’s Republic of China; 2Department of Gastroenterology, Beijing Anzhen Nanchong Hospital of Capital Medical University & Nanchong Central Hospital, Nanchong, Sichuan, 637000, People’s Republic of China; 3Department of Cardiovascular Medicine, Beijing Anzhen Nanchong Hospital of Capital Medical University & Nanchong Central Hospital, Nanchong, Sichuan, 637000, People’s Republic of ChinaCorrespondence: Peng Xie, Department of Cardiovascular Medicine, Beijing Anzhen Nanchong Hospital of Capital Medical University & Nanchong Central Hospital, Nanchong, Sichuan, 637000, People’s Republic of China, Email billxiewang@163.comObjective: 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.Keywords: CHD, risk factors, predictive model, metabolic indicators |
| format | Article |
| id | doaj-art-0be04d9dcde9448288b6ffa456058ce0 |
| institution | OA Journals |
| issn | 1178-2048 |
| language | English |
| publishDate | 2025-05-01 |
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| series | Vascular Health and Risk Management |
| spelling | doaj-art-0be04d9dcde9448288b6ffa456058ce02025-08-20T02:15:29ZengDove Medical PressVascular Health and Risk Management1178-20482025-05-01Volume 21Issue 1371382102783Constructing a Predictive Model to Evaluate the Risk of CHD Based on New Metabolic IndicatorsWang W0Du Z1Xie P2Department of NursingDepartment of GastroenterologyDepartment of Critical Care Medicine, Nanchong Central HospitalWenqiang Wang,1 Zonghan Du,2 Peng Xie3 1Department of Nursing, Beijing Anzhen Nanchong Hospital of Capital Medical University & Nanchong Central Hospital, Nanchong, Sichuan, 637000, People’s Republic of China; 2Department of Gastroenterology, Beijing Anzhen Nanchong Hospital of Capital Medical University & Nanchong Central Hospital, Nanchong, Sichuan, 637000, People’s Republic of China; 3Department of Cardiovascular Medicine, Beijing Anzhen Nanchong Hospital of Capital Medical University & Nanchong Central Hospital, Nanchong, Sichuan, 637000, People’s Republic of ChinaCorrespondence: Peng Xie, Department of Cardiovascular Medicine, Beijing Anzhen Nanchong Hospital of Capital Medical University & Nanchong Central Hospital, Nanchong, Sichuan, 637000, People’s Republic of China, Email billxiewang@163.comObjective: 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.Keywords: CHD, risk factors, predictive model, metabolic indicatorshttps://www.dovepress.com/constructing-a-predictive-model-to-evaluate-the-risk-of-chd-based-on-n-peer-reviewed-fulltext-article-VHRMCHDRisk factorsPredictive modelMetabolic indicators |
| spellingShingle | Wang W Du Z Xie P Constructing a Predictive Model to Evaluate the Risk of CHD Based on New Metabolic Indicators Vascular Health and Risk Management CHD Risk factors Predictive model Metabolic indicators |
| title | Constructing a Predictive Model to Evaluate the Risk of CHD Based on New Metabolic Indicators |
| title_full | Constructing a Predictive Model to Evaluate the Risk of CHD Based on New Metabolic Indicators |
| title_fullStr | Constructing a Predictive Model to Evaluate the Risk of CHD Based on New Metabolic Indicators |
| title_full_unstemmed | Constructing a Predictive Model to Evaluate the Risk of CHD Based on New Metabolic Indicators |
| title_short | Constructing a Predictive Model to Evaluate the Risk of CHD Based on New Metabolic Indicators |
| title_sort | constructing a predictive model to evaluate the risk of chd based on new metabolic indicators |
| topic | CHD Risk factors Predictive model Metabolic indicators |
| url | https://www.dovepress.com/constructing-a-predictive-model-to-evaluate-the-risk-of-chd-based-on-n-peer-reviewed-fulltext-article-VHRM |
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