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Development and validation of a risk prediction model for myocardial hypoperfusion after primary PCI in ST-segment
Yan Zhao1, Xiaoxia Fang2,3, Huilin Li2
1East Medical Imaging Department DSA (Catheter) Operating Room, Xinxiang Central Hospital, The Fourth Clinical College of Henan Medical University, Xinxiang, China.
Insights
Time to treatment, atorvastatin dose, balloon deflation, red cell distribution width, and monoamine oxidase levels predict myocardial hypoperfusion after primary PCI in STEMI patients. A new model aids early risk stratification.
Area of Science:
- Cardiology
- Interventional Cardiology
- Biomedical Engineering
Background:
- Myocardial hypoperfusion is a critical complication after primary percutaneous coronary intervention (PCI) for ST-segment elevation myocardial infarction (STEMI).
- Identifying predictors of hypoperfusion is essential for improving patient outcomes and guiding treatment strategies.
Purpose of the Study:
- To analyze the determinants of myocardial hypoperfusion following primary PCI in STEMI patients.
- To develop and validate a risk prediction model for myocardial hypoperfusion.
Main Methods:
- Retrospective analysis of 434 STEMI patients undergoing primary PCI.
- Utilized Boruta and LASSO regression for variable selection, followed by multivariable logistic regression.
- Developed a nomogram-based risk prediction model and assessed its performance using ROC curves, calibration curves, and DCA.
Main Results:
- Key predictors identified: time from onset to PCI, atorvastatin dose, balloon deflation method, red cell distribution width (RDW), and monoamine oxidase (MAO) levels.
- The prediction model showed good discrimination (AUC 0.855 training, 0.838 validation) and calibration.
- Decision curve analysis indicated the model's clinical utility across a wide range of threshold probabilities.
Conclusions:
- Time to reperfusion, pre-PCI atorvastatin dose, balloon deflation technique, RDW, and MAO levels are significant determinants of post-PCI myocardial hypoperfusion in STEMI.
- The developed risk prediction model demonstrates strong predictive performance and clinical utility for early risk stratification.
- This model can aid clinicians in identifying STEMI patients at higher risk of myocardial hypoperfusion, enabling timely interventions.
Objective:
To analyze the determinants of myocardial hypoperfusion following primary percutaneous coronary intervention (PCI) in patients with acute ST-segment elevation myocardial infarction (STEMI) and to develop a risk prediction model.
Methods:
Clinical data from 434 patients with STEMI who underwent primary PCI at our hospital between January 2023 and June 2025 were retrospectively collected. Patients were randomly assigned to a training cohort (n = 304) and a validation cohort (n = 130) at a 7:3 ratio. Based on postprocedural myocardial perfusion, the training cohort was further divided into a hypoperfusion group (n = 103) and a normal perfusion group (n = 201). Candidate variables were screened using Boruta and least absolute shrinkage and selection operator (LASSO) regression, followed by multivariable logistic regression to identify independent predictors. A risk prediction model was constructed using R software and visualized as a nomogram. Model performance and clinical utility were evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).
Results:
Multivariable logistic regression identified time from onset to primary PCI, atorvastatin dose before PCI, balloon deflation method during PCI, red cell distribution width (RDW), and monoamine oxidase (MAO) levels as independent predictors of myocardial hypoperfusion (all P < 0.05). The nomogram demonstrated good discrimination, with area under the curve (AUC) values of 0.855 (95% CI: 0.811-0.900) in the training cohort and 0.838 (95% CI: 0.764-0.912) in the validation cohort. Calibration curves indicated good agreement between predicted and observed outcomes. Decision curve analysis showed that the model provided greater net benefit than both treat-all and treat-none strategies across threshold probabilities of 0.01-0.99 in the training cohort and 0.07-0.99 in the validation cohort.
Conclusion:
Time from onset to primary PCI, atorvastatin dose before PCI, balloon deflation method during PCI, RDW, and MAO levels are important determinants of myocardial hypoperfusion following primary PCI in patients with STEMI. The proposed prediction model demonstrated favorable predictive performance and clinical utility, suggesting its potential value for early risk stratification.
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