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The diagnostic and predictive value of AI-combined multilayer spiral CT for MACE after emergency PCI in STEMI

Ting Xu1, Renfu Zhang2

  • 1Department of Emergency, The First Affiliated Hospital of Qiqihar Medical University, Qiqihar, Heilongjiang, China.

Medicine
|June 27, 2026
PubMed

Insights

Artificial intelligence (AI) combined with coronary computed tomography angiography (CCTA) significantly improves risk prediction for major adverse cardiovascular events (MACE) in ST-segment elevation myocardial infarction (STEMI) patients after percutaneous coronary intervention (PCI). This AI-CCTA model offers superior accuracy compared to traditional scores.

Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • ST-segment elevation myocardial infarction (STEMI) patients face high risks of major adverse cardiovascular events (MACE) post-percutaneous coronary intervention (PCI).
  • Traditional clinical scores have limitations in accurately stratifying MACE risk in these patients.
  • Coronary computed tomography angiography (CCTA) offers detailed plaque visualization, but its integration with AI may enhance predictive capabilities.

Purpose of the Study:

  • To evaluate the efficacy of an artificial intelligence (AI)-assisted coronary computed tomography angiography (CCTA) model in predicting 1-year MACE in STEMI patients post-PCI.
  • To compare the predictive performance of the AI-CCTA model against traditional risk stratification tools, such as the GRACE score.

Main Methods:

  • A prospective cohort study included 92 STEMI patients undergoing emergency PCI.
  • Patients received 256-slice CCTA with AI analysis within 7 days post-PCI to quantify plaque characteristics.
  • AI algorithms assessed total plaque volume, low-attenuation plaque, positive remodeling, and coronary artery calcium.
  • Multivariate logistic regression and ROC curve analysis were used to determine predictive value for 1-year MACE.

Main Results:

  • The AI-CCTA model, incorporating plaque volume >400 mm³, low-attenuation plaque, and left ventricular end-diastolic volume change, showed superior prediction of 1-year MACE.
  • The AI-CCTA model achieved an area under the curve (AUC) of 0.876, significantly outperforming the GRACE score (AUC 0.742).
  • Optimal cutoffs for the AI-CCTA model demonstrated high sensitivity (87.00%) and negative predictive value (93.60%).

Conclusions:

  • AI-enhanced CCTA provides significant incremental value for predicting 1-year MACE in STEMI patients post-PCI, surpassing traditional risk scores.
  • This integrated approach facilitates personalized risk assessment and may guide intensified follow-up strategies for high-risk individuals.
  • AI-CCTA holds promise for improving patient management and outcomes in STEMI care.

Keywords:
ST-segment elevation myocardial infarctionartificial intelligencecomputed tomography angiographymajor adverse cardiovascular eventspercutaneous coronary interventionrisk prediction

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