Patients with newly diagnosed chronic coronary syndrome who had both CT-FFR ≤ 0.8 and HRPC ≥ 2 had a 6.06-fold increased risk of MACE compared to those with CT-FFR >0.8 and HRPC <2.
Cohort (n=222)
No
Does the combined use of AI-derived CT-FFR and high-risk plaque characteristics improve the prediction of major adverse cardiovascular events in patients with newly diagnosed chronic coronary syndrome?
The combined use of AI-derived CT-FFR and high-risk plaque characteristics significantly improves risk stratification and predictive accuracy for adverse cardiovascular events in patients with newly diagnosed chronic coronary syndrome.
Effect estimate: HR 6.06 (95% CI 1.38-26.52)
p-value: p=0.017
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: ≤ 0.038), providing superior predictive performance compared to using either metric alone. 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.
Zhang et al. (Wed,) conducted a cohort in newly diagnosed chronic coronary syndrome (n=222). CT-FFR ≤ 0.8 and HRPC ≥ 2 vs. CT-FFR >0.8 and HRPC <2 was evaluated on Major adverse cardiovascular events (MACE) (HR 6.06, 95% CI 1.38-26.52, p=0.017). Patients with newly diagnosed chronic coronary syndrome who had both CT-FFR ≤ 0.8 and HRPC ≥ 2 had a 6.06-fold increased risk of MACE compared to those with CT-FFR >0.8 and HRPC <2.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: