The diagnostic and predictive value of AI-combined multilayer spiral CT for MACE after emergency PCI in STEMI patients: A prospective cohort study

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ST-segment elevation myocardial infarction (STEMI) patients remain at substantial risk for major adverse cardiovascular events (MACE) following emergency percutaneous coronary intervention (PCI). The integration of artificial intelligence (AI) with coronary computed tomography angiography (CCTA) may enhance risk stratification beyond traditional clinical scores. This prospective cohort study enrolled 92 consecutive STEMI patients who underwent emergency PCI between June 2022 and June 2025. All patients underwent 256-slice CCTA with AI-assisted analysis within 7 days post-PCI. AI algorithms quantified plaque characteristics including total plaque volume, low-attenuation plaque burden, positive remodeling, and coronary artery calcium score. The primary endpoint was MACE (composite of cardiac death, recurrent myocardial infarction, target vessel revascularization, and heart failure hospitalization) at 1-year follow-up. Multivariate logistic regression and receiver operating characteristic (ROC) curve analysis were performed to assess predictive value. AI-enhanced multilayer spiral CT provides excellent discriminatory power for predicting 1-year MACE in STEMI patients post-PCI, offering significant incremental value beyond traditional risk stratification tools. This integrated approach enables personalized risk assessment and may guide intensified follow-up strategies in high-risk patients. During 12-month follow-up, MACE occurred in 23 patients (25.00%). The AI-CCTA model incorporating total plaque volume >400 mm3 (odds ratio [OR] 2.87, 95% confidence interval [CI]: 1.34-6.15, P = .007), low-attenuation plaque presence (OR 3.42, 95% CI: 1.28-9.14, P = .014), and left ventricular end-diastolic volume change (OR 2.64, 95% CI: 1.19-5.86, P = .017) demonstrated superior predictive performance. The combined AI-CCTA model achieved an area under the curve (AUC) of 0.876 (95% CI: 0.791-0.937), significantly outperforming the GRACE score alone (AUC 0.742, 95% CI: 0.639-0.829, P = .012). The optimal cutoff yielded a sensitivity of 87.00%, specificity of 79.70%, positive predictive value of 62.50%, and negative predictive value of 93.60%.