ArticleJournal of clinical medicine2021
New Model for Predicting the Presence of Coronary Artery Calcification.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- Development of Mathematical Models Evaluating Presence of Coronary Calcification Independent of Computed Tomography (DEPICT): Radiation-Free Evaluation of Coronary Atherosclerosis.Reviews in cardiovascular medicine · 2026Article
- Reliable biomarkers for diabetic nephropathy using machine learning-assisted contrast-enhanced ultrasonography and clinical characteristics.Clinical and experimental medicine · 2025Article
- Artificial Intelligence-Assisted Perfusion Density as Biomarker for Screening Diabetic Nephropathy.Translational vision science & technology · 2024Article
- Machine learning-based coronary artery calcium score predicted from clinical variables as a prognostic indicator in patients referred for invasive coronary angiography.European radiology · 2024Article
- A machine learning approach to predicting vascular calcification risk of type 2 diabetes: A retrospective study.Clinical cardiology · 2024Article
- Circulating miR-129-3p in combination with clinical factors predicts vascular calcification in hemodialysis patients.Clinical kidney journal · 2024Article
- Understanding Arteriosclerotic Heart Disease Patients Using Electronic Health Records: A Machine Learning and Shapley Additive exPlanations Approach.Healthcare informatics research · 2023Article
- Bibliometric and Visual Analysis of Vascular Calcification Research.Frontiers in pharmacology · 2021Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Coronary artery calcification (CAC) is a feature of coronary atherosclerosis and a well-known risk factor for cardiovascular disease (CVD). As the absence of CAC is associated with a lower incidence rate of CVD, measurement of a CAC score is helpful for risk stratification when the risk decision is uncertain. This was a retrospective study with an aim to build a model to predict the presence of CAC (i.e., CAC score = 0 or not) and evaluate the discrimination and calibration power of the model. Our data set was divided into two set (80% for training set and 20% for test set). Ten-fold cross-validation was applied with ten times of interaction in each fold. We built prediction models using logistic regression (LRM), classification and regression tree (CART), conditional inference tree (CIT), and random forest (RF). A total of 3,302 patients from two cohorts (Soonchunhyang University Cheonan Hospital and Kangbuk Samsung Health Study) were enrolled. These patients' ages were between 40 and 75 years. All models showed acceptable accuracies (LRM, 70.71%; CART, 71.32%; CIT, 71.32%; and RF, 71.02%). The decision tree model using CART and CIT showed a reasonable accuracy without complexity. It could be implemented in real-world practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.