Zelong Cao

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; P = 0.036] and HRPC ≥2 (HR: 2.39, 95% CI: 1.20–4.77; P = 0.014) independently predicted MACE. Patients with 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 P = 0.017). Combining CT-FFR and HRPC significantly improved the predictive accuracy of risk models, reflected in increases in C-index, AUC, NRI, and IDI ( P ≤ 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.

In the published article, there was an error in the supplementary material. The file "Supplementary_Data.xlsx" was erroneously published with the original version of this paper due to a file selection error during submission. The file has now been replaced with the correct version, "Supplementary_Data_Corrected.xlsx." The corrected file contains the verified and finalized data consistent with the published manuscript. This correction does not affect the results, interpretation, or conclusions of the article.No changes have been made to the main text of the article; only the supplementary material has been updated.The original version of this article has been updated.

Background: Coronary atherosclerotic heart disease (CAD) remains a major global health burden and a leading cause of mortality. Its pathogenesis is closely linked to multiple risk factors, among which inflammation plays a central role. While inflammatory biomarkers such as platelet and monocyte counts have been incorporated into prognostic assessments, their predictive accuracy remains limited. Further investigation of novel inflammatory indices is needed to refine risk stratification and guide clinical management. Objective: This study aimed to evaluate the prognostic value of the mean platelet volume-to-monocyte count ratio (MMR) for predicting major adverse cardiovascular events (MACE) in patients with newly diagnosed CAD. Methods: A total of 652 treatment-naïve CAD patients were enrolled. Kaplan–Meier survival analysis and univariate Cox proportional hazards models were applied to assess the association between MMR levels and MACE. Subgroup analyses were performed to test for effect modification. Restricted cubic spline (RCS) models were used to explore the dose–response relationship. The incremental predictive value of MMR beyond conventional risk factors was examined using changes in the concordance index (C-index), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results: Patients were stratified into quintiles based on MMR values (L1: 7.89–14.43; L2: 14.50–17.96; L3: 18.00–22.16; L4: 22.25–28.53; L5: 28.67–60.67). Kaplan–Meier analysis revealed significantly poorer outcomes in the L3 group compared with other quintiles (log-rank P = 0.0014). RCS analysis demonstrated a significant nonlinear association between MMR levels and MACE risk ( P = 0.001), characterized by an inverted U-shaped relationship. Incorporating MMR into conventional risk models significantly improved predictive performance (AUC 0.718 vs. 0.673; P = 0.018). Conclusion: In newly diagnosed CAD patients, MMR shows a nonlinear, inverted U-shaped association with MACE risk. The addition of MMR to standard risk models enhances prognostic accuracy. Further multicenter prospective studies and mechanistic trials are needed to verify the prognostic value of MMR and to elucidate its mechanism of action.