ArticleAging2020
Development of a nomogram that predicts the risk for coronary atherosclerotic heart disease.
Article in Aging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- IMMT downregulation promotes osteoarthritis development by inducing mitochondrial dysfunction.Journal of orthopaedic surgery and research · 2026Article
- Development and external validation of an interpretable machine learning model for diagnosing coronary heart disease in patients with type 2 diabetes and MASLD.Frontiers in endocrinology · 2026Article
- Effects of rosuvastatin and atorvastatin treatment on ET-1, ADMA, and safety efficacy in elderly patients with coronary heart disease combined with hyperlipidemia.Open life sciences · 2026Article
- Constructing a clinical prediction model for patients with coronary heart disease: a study based on carotid ultrasound and clinical related factors.Frontiers in cardiovascular medicine · 2025Article
- Advanced Machine Learning to Predict Coronary Artery Disease Severity in Patients with Premature Myocardial Infarction.Reviews in cardiovascular medicine · 2025Article
- Coronary artery bypass grafting in octogenarians: a nomogram for predicting all-cause mortality.Journal of cardiothoracic surgery · 2024Article
- Predicting angiographic coronary artery disease using machine learning and high-frequency QRS.BMC medical informatics and decision making · 2024Article
- Analysis of risk factors for PCI no-reflow in coronary heart disease and construction of related prediction models.American journal of translational research · 2024Article
- A nomogram for predicting the 4-year risk of chronic kidney disease among Chinese elderly adults.International urology and nephrology · 2023Review
- Comparison of in-hospital outcomes after coronary artery bypass graft surgery in elders and younger patients: a multicenter retrospective study.Journal of cardiothoracic surgery · 2023Article
- Development and validation of a novel 10-year cardiovascular risk prediction nomogram for the United Arab Emirates national population.BMJ open · 2022Article
- Novel Insights into the Predictors of Obstructive Sleep Apnea Syndrome in Patients with Chronic Coronary Syndrome: Development of a Predicting Model.Oxidative medicine and cellular longevity · 2022Article
- Predictive nomogram for coronary heart disease in patients with type 2 diabetes mellitus.Frontiers in cardiovascular medicine · 2022Article
- Knockdown of lncRNA ENST00000609755.1 Confers Protection Against Early oxLDL-Induced Coronary Heart Disease.Frontiers in cardiovascular medicine · 2021Article
- Development and Validation of a Predictive Model for Coronary Artery Disease Using Machine Learning.Frontiers in cardiovascular medicine · 2021Article
- Nomograms Based on the Advanced Lung Cancer Inflammation Index for the Prediction of Coronary Artery Disease and Calcification.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Studies seldom combine biological, behavioral and psychological factors to estimate coronary atherosclerotic heart disease (CHD) risk. Here, we evaluated the associations between these factors and CHD to develop a predictive nomogram to identify those at high risk of CHD. This case-control study included 4392 participants (1578 CHD cases and 2814 controls) in southeast China. Thirty-three biological, behavioral and psychological variables were evaluated. Following multivariate logistic regression analysis, which revealed eight risk factors associated with CHD, a predictive nomogram was developed based on a final model that included the three non-modifiable (sex, age and family history of CHD) and five modifiable (hypertension, hyperlipidemia, diabetes, recent experience of a major traumatic event, and anxiety) variables. The higher total nomogram score, the greater the CHD risk. Final model accuracy (as estimated from the area under the receiver operating characteristic curve) was 0.726 (95% confidence interval: 0.709-0.747). Validation analysis confirmed the high accuracy of the nomogram. High risk of CHD was associated with several biological, behavioral and psychological factors. We have thus developed an intuitive nomogram that could facilitate development of preliminary prevention strategies for CHD.
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