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A nomogram risk prediction model for no-reflow after primary percutaneous coronary intervention based on rapidly
Yehong Liu1, Ting Ye1, Ke Chen1,2
1Department of Cardiology, The 904th Hospital of Joint Logistic Support Force of PLA, Wuxi, China.
Insights
A new nomogram predicts no-reflow after percutaneous coronary intervention (PCI) in ST-segment elevation myocardial infarction (STEMI) patients. This tool helps identify high-risk individuals and guides prognosis, improving cardiovascular event management.
Area of Science:
- Cardiology
- Interventional Cardiology
- Predictive Modeling
Background:
- No-reflow phenomenon post-primary percutaneous coronary intervention (PCI) in ST-segment elevation myocardial infarction (STEMI) is linked to increased major adverse cardiovascular events (MACE).
- Accurate risk stratification for no-reflow is crucial for optimizing patient outcomes.
Purpose of the Study:
- To develop and validate a predictive nomogram model for no-reflow risk in STEMI patients undergoing primary PCI.
- To assess the prognostic utility of this nomogram in predicting MACE.
Main Methods:
- A cohort of 443 STEMI patients undergoing primary PCI was analyzed.
- Independent risk factors for no-reflow were identified using multivariate logistic regression.
- A nomogram was constructed and validated using bootstrap resampling and ROC curve analysis.
Main Results:
- The incidence of no-reflow was 18%. Independent predictors included Killip class ≥2, pre-operative D-dimer, fibrinogen, and Systemic Immune-Inflammation Index (SII).
- The nomogram demonstrated good discrimination (AUC 0.716, C-index 0.706) and calibration.
- Higher nomogram scores predicted increased MACE risk (HR 2.062, P=0.004).
Conclusions:
- A novel, easily accessible nomogram for predicting no-reflow post-PCI in STEMI patients was successfully developed.
- This nomogram aids in identifying high-risk patients and serves as a prognostic tool for MACE.
- The model shows clinical utility and good predictive performance for no-reflow and subsequent cardiovascular events.
Background:
No-reflow occurring after primary percutaneous coronary intervention (PCI) in patients with ST-segment elevation myocardial infarction (STEMI) can increase the incidence of major adverse cardiovascular events (MACE). The present study aimed to construct a nomogram prediction model that can be quickly referred to before surgery to predict the risk for no-reflow after PCI in STEMI patients, and to further explore its prognostic utility in this patient population.
Methods:
Research subjects included 443 STEMI patients who underwent primary PCI between February 2018 and February 2021. Rapidly available clinical data obtained from emergency admissions were collected. Independent risk factors for no-reflow were analyzed using a multivariate logistic regression model. Subsequently, a nomogram for no-reflow was constructed and verified using bootstrap resampling. A receiver operating characteristic (ROC) curve was plotted to evaluate the discrimination ability of the nomogram model and a calibration curve was used to assess the concentricity between the model probability curve and ideal curve. Finally, the clinical utility of the model was evaluated using decision curve analysis.
Results:
The incidence of no-reflow was 18% among patients with STEMI. Killip class ≥2 on admission, pre-operative D-dimer and fibrinogen levels, and systemic immune-inflammation index (SII) were independent risk factors for no-reflow. A simple and quickly accessible prediction nomogram for no-reflow after PCI was developed. This nomogram demonstrated good discrimination, with an area under the ROC curve of 0.716. This nomogram was further validated using bootstrapping with 1,000 repetitions; the C-index of the bootstrap model was 0.706. Decision curve analysis revealed that this model demonstrated good fit and calibration and positive net benefits. Kaplan-Meier survival curve analysis revealed that patients with higher model scores were at a higher risk of MACE. Multivariate Cox regression analysis revealed that higher model score(s) was an independent predictor of MACE (hazard ratio 2.062; P = 0.004).
Conclusions:
A nomogram prediction model that can be quickly referred to before surgery to predict the risk for no-reflow after PCI in STEMI patients was constructed. This novel nomogram may be useful in identifying STEMI patients at higher risk for no-reflow and may predict prognosis in this patient population.
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