Trial reportFrontiers in cardiovascular medicine2025
Combined prognostic value of AI-derived CT-FFR and high-risk plaque characteristics in patients with newly diagnosed chronic coronary syndrome: a prospective cohort study.
Trial report in Frontiers in cardiovascular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Associations of AI-derived coronary CT angiography features with CZT-SPECT coronary flow reserve and FFR-CT.Frontiers in cardiovascular medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: While coronary computed tomography angiography (CTA) is widely used for diagnosing chronic coronary syndrome (CCS), its potential for assessing physiological function and plaque vulnerability-through AI-derived fractional flow reserve (CT-FFR) and high-risk plaque characteristics (HRPC)-is not fully leveraged in clinical practice. The combined prognostic value of these non-invasive tools in newly diagnosed CCS patients remains underexplored. Objective: To evaluate the individual and combined prognostic value of AI-based CT-FFR and HRPC in predicting major adverse cardiovascular events (MACE) in patients with newly diagnosed CCS. Methods: In this observational cohort study, 222 inpatients newly diagnosed with CCS who were admitted for non-acute chest pain and underwent coronary CTA were included. Patients were stratified into four groups based on their CT-FFR and HRPC values. Kaplan-Meier survival curves and multivariate Cox proportional hazards models were used to assess the predictive value of CT-FFR and HRPC for MACE. Model performance was evaluated using the C-index, area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results: Vessels with CT-FFR ≤0.8 had a higher prevalence and number of HRPC compared to those with CT-FFR >0.8. Over a median follow-up period of 22 months, 52 patients (23.4%) experienced MACE. Both CT-FFR ≤0.8 [hazard ratio (HR): 2.62, 95% confidence interval (CI): 1.06-6.47; Conclusion: The combined use of AI-derived CT-FFR and HRPC significantly improves risk stratification in patients with newly diagnosed CCS, offering better predictive accuracy for adverse cardiovascular events. This enhanced risk assessment could enable clinicians to identify high-risk patients more effectively and tailor management strategies accordingly. Further multicenter studies are warranted to validate these findings across diverse populations.
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