ArticleThe international journal of cardiovascular imaging2024
Coronary computed tomographic angiography-derived anatomic and hemodynamic plaque characteristics in prediction of cardiovascular events.
Article in The international journal of cardiovascular imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Machine Learning Models in Major Cardiovascular Adverse Events Prediction Based on Coronary Computed Tomography Angiography: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Explainable ML for ACS culprit plaques: a multidimensional CCTA model highlighting hemodynamic increment.The international journal of cardiovascular imaging · 2026Article
- Mediation of stenosis-induced ischemia by proximal peri-coronary adipose tissue: A cross-sectional study.Medicine · 2026Article
- Artificial intelligence in coronary CT angiography: transforming the diagnosis and risk stratification of atherosclerosis.The international journal of cardiovascular imaging · 2025Review
- Emerging Applications of Positron Emission Tomography in Coronary Artery Disease.Journal of personalized medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
This study investigated the association of anatomic and hemodynamic plaque characteristics based on deep learning coronary computed tomography angiography (CCTA) with high-risk plaques that caused subsequent major adverse cardiovascular events (MACE). A retrospective analysis was conducted on patients who underwent CCTA between 1 month and 3 years prior to the occurrence of a MACE. Deep learning and computational fluid dynamics algorithms based on CCTA were applied to extract adverse plaque characteristics (low-attenuation plaque, positive remodeling, napkin-ring sign, and spotty calcification), and hemodynamic parameters (fractional flow reserve derived by coronary computed tomographic angiography [FFR
Indexed as
Identifiers
38878147What OpenQuestion holds
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.