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Development and Validation of a Nomogram to Predict Ventricular Fibrillation During Percutaneous Coronary
Ruifeng Liu1, Xiangyu Gao1, Jihong Fan1
1Department of Cardiology, Beijing Friendship Hospital Affiliated to Capital Medical University, 100050 Beijing, China.
Insights
Ventricular fibrillation (VF) during percutaneous coronary intervention (PCI) for acute myocardial infarction (AMI) is predicted by diabetes, neutrophil-to-lymphocyte ratio (NLR), right coronary artery (RCA) intervention, Gensini score, and lack of beta-blocker use. A developed nomogram accurately identifies high-risk patients.
Area of Science:
- Cardiology
- Interventional Cardiology
- Medical Informatics
Background:
- Ventricular fibrillation (VF) is a critical complication of acute myocardial infarction (AMI).
- Patients undergoing percutaneous coronary intervention (PCI) for AMI face a heightened risk of VF.
- Early identification of patients at high risk for VF is essential for timely intervention and improved outcomes.
Purpose of the Study:
- To identify independent predictors of VF during PCI in AMI patients.
- To develop and validate a predictive model (nomogram) for VF risk in this population.
- To enhance clinical decision-making for preventive strategies in high-risk AMI patients undergoing PCI.
Main Methods:
- Retrospective analysis of clinical, laboratory, and angiographic data from 155 AMI patients.
- Variable selection using LASSO regression, elastic net regression, and random forest.
- Multivariable logistic regression to identify independent predictors; nomogram development and validation using ROC and calibration curves.
Main Results:
- Key predictors of VF included diabetes, elevated neutrophil-to-lymphocyte ratio (NLR), right coronary artery (RCA) intervention, higher Gensini score, and absence of beta-blocker use.
- The developed nomogram demonstrated strong predictive performance with an AUC of 0.882.
- The nomogram showed good calibration, indicating reliable prediction of VF risk.
Conclusions:
- Diabetes, NLR, RCA intervention, Gensini score, and lack of beta-blocker use are significant predictors of VF during PCI in AMI.
- A validated nomogram incorporating these factors provides a valuable tool for identifying high-risk patients.
- This predictive model can guide targeted preventive strategies to improve outcomes for AMI patients undergoing PCI.
Background:
Ventricular fibrillation (VF) is a life-threatening complication of acute myocardial infarction (AMI), particularly in patients undergoing percutaneous coronary intervention (PCI). Early identification of high-risk patients is crucial for implementing preventive measures and improving outcomes.
Methods:
This retrospective study analyzed clinical, laboratory, and angiographic data from 155 AMI patients to identify predictors of VF during PCI. Variable selection was performed using least absolute shrinkage and selection operator (LASSO) regression, elastic net regression, and random forest. Independent predictors were identified through multivariable logistic regression, and a nomogram was developed and validated to predict VF risk. Model performance was assessed using receiver operating characteristic (ROC) and calibration curves.
Results:
Independent predictors of VF included diabetes (OR = 3.676 (1.365-10.668); p = 0.012), neutrophil-to-lymphocyte ratio (NLR) (odds ratio (OR) = 1.149 (1.053-1.265); p = 0.002), right coronary artery (RCA) intervention (OR = 3.185 (1.088-9.804); p = 0.037), Gensini score (OR = 1.020 (1.007-1.033); p = 0.003), and absence of beta blockers (OR = 0.168 (0.054-0.472); p = 0.001). The nomogram, incorporating these predictors, demonstrated a strong discriminative ability with an area under the ROC curve (AUC) of 0.882 (0.825-0.939) and good calibration (Hosmer-Lemeshow test, p = 0.769). The calibration curve showed a strong alignment between predicted probabilities and observed outcomes, with a mean absolute error of 0.033.
Conclusions:
This study identified diabetes, NLR, RCA intervention, Gensini score, and absence of beta-blocker use as key predictors of VF during PCI in AMI patients. A nomogram incorporating these factors showed strong predictive performance, aiding clinicians in identifying high-risk patients for targeted preventive strategies.
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