ArticleCirculation2023
Heterogeneity in the Association Between the Presence of Coronary Artery Calcium and Cardiovascular Events: A Machine-Learning Approach in the MESA Study.
Article in Circulation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed, 38 citations in OpenAlex.
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- Heterogeneous cardiovascular effects of sodium-glucose cotransporter 2 inhibitors in type 2 diabetes: a causal forest and target trial emulation study.European journal of preventive cardiology · 2026Article
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- Therapeutic target of high fresh frozen plasma to red blood cell ratio in severe blunt trauma.Critical care (London, England) · 2025Article
- Review
- Cost-Effectiveness of Coronary Artery Calcium Scoring for Cardiovascular Disease Prevention in Diabetes: An Analysis From MESA.Journal of the American Heart Association · 2025Article
- Machine learning-based high-benefit approach versus traditional high-risk approach in statin therapy: the Shizuoka Kokuho database study.Scientific reports · 2025Article
- Risk factors of breast cancer patients developing multiple primary cancers: a retrospective study and establishing/testing of machine learning models.BMC medical informatics and decision making · 2025Article
- Understanding the causal effects and heterogeneity between metabolic syndrome and lung function: a nationwide prospective cohort study in China.Diabetology & metabolic syndrome · 2025Article
- Application of non-invasive imaging in myocardial infarction: a bibliometric analysis from January 2003 to December 2022.Quantitative imaging in medicine and surgery · 2025Article
- Percutaneous coronary intervention for calcified and resistant lesions.EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology · 2025Review
- Generalized framework for identifying meaningful heterogenous treatment effects in observational studies: A parametric data-adaptive G-computation approach.Statistical methods in medical research · 2025Article
- Research on Prediction model of Carotid-Femoral Pulse Wave Velocity: Based on Machine Learning Algorithm.Journal of clinical hypertension (Greenwich, Conn.) · 2025Article
- Investigating the feasibility of machine learning to guide personalized red blood cell (RBC) transfusion: analyzing the heterogeneity of RBC transfusion in septic patients with hemoglobin levels of 7-9 g/dL based on the causal forest model.Frontiers in pharmacology · 2025Article
- Causal inference and machine learning in endocrine epidemiology.Endocrine journal · 2024Review
- Article
- Machine learning reveals heterogeneous associations between environmental factors and cardiometabolic diseases across polygenic risk scores.Communications medicine · 2024Article
- 2024 Guidelines of the Taiwan Society of Cardiology on the Primary Prevention of Atherosclerotic Cardiovascular Disease --- Part I.Acta Cardiologica Sinica · 2024Article
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCoronary artery calcium (CAC) has been widely recognized as an important predictor of cardiovascular disease (CVD). Given the finite resources, it is important to identify individuals who would receive the most benefit from detecting positive CAC by screening. However, the evidence is limited as to whether the burden of positive CAC on CVD differs by multidimensional individual characteristics. We sought to investigate the heterogeneity in the association between positive CAC and incident CVD.
methodsThis cohort study included adults from MESA (Multi-Ethnic Study of Atherosclerosis) ages ≥45 years and free of cardiovascular disease. After propensity score matching in a 1:1 ratio, we applied a machine learning causal forest model to (1) evaluate the heterogeneity in the association between positive CAC and incident CVD, and (2) predict the increase in CVD risk at 10-years when CAC>0 (versus CAC=0) at the individual level. We then compared the estimated increase in CVD risk when CAC>0 to the absolute 10-year atherosclerotic CVD (ASCVD) risk calculated by the 2013 American College of Cardiology/American Heart Association pooled cohort equations.
resultsAcross 3328 adults in our propensity score-matched analysis, our causal forest model showed the heterogeneity in the association between CAC>0 and incident CVD. We found a dose-response relationship of the estimated increase in CVD risk when CAC>0 with higher 10-year ASCVD risk. Almost all individuals (2293 of 2428 [94.4%]) with borderline risk of ASCVD or higher showed ≥2.5% increase in CVD risk when CAC>0. Even among 900 adults with low ASCVD risk, 689 (69.2%) showed ≥2.5% increase in CVD risk when CAC>0; these individuals were more likely to be male, Hispanic, and have unfavorable CVD risk factors than others.
conclusionsThe expected increases in CVD risk when CAC>0 were heterogeneous across individuals. Moreover, nearly 70% of people with low ASCVD risk showed a large increase in CVD risk when CAC>0, highlighting the need for CAC screening among such low-risk individuals. Future studies are needed to assess whether targeting individuals for CAC measurements based on not only the absolute ASCVD risk but also the expected increase in CVD risk when CAC>0 improves cardiovascular outcomes.
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