ArticleJournal of inflammation research2023
Development and Validation of Nomogram for the Prediction of Malignant Ventricular Arrhythmia Including Circulating Inflammatory Cells in Patients with Acute ST-Segment Elevation Myocardial Infarction.
Article in Journal of inflammation research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Establishment and validation of a risk prediction model for malignant ventricular arrhythmia in acute myocardial infarction patients based on 24-hour dynamic electrocardiogram parameters.American journal of translational research · 2026Article
- Prognostic value of inflammatory markers for all-cause mortality in patients with acute myocardial infarction in the coronary care unit: a retrospective study based on MIMIC-IV database.Frontiers in cardiovascular medicine · 2025Article
- Article
- A case-control study on the clinical characteristics of granisetron-related arrhythmias and the development of a predictive nomogram.International journal of clinical pharmacy · 2024Article
- Establishment and application of an intraoperative reperfusion arrhythmia prediction model for PCI in elderly patients with acute coronary syndrome.American journal of translational research · 2024Article
- Interpretable Machine Learning Models for Predicting Malignant Ventricular Arrhythmia in Patients with Acute ST-Segment Elevation Myocardial Infarction Based on Systemic Inflammation Index.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
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4 authors.
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Abstract
Background: Malignant ventricular arrhythmia (MVA) can seriously affect the hemodynamic changes of the body. In this study, we developed and validated a nomogram to predict the in-hospital MVA risk in patients with STEMI after emergency PCI. Methods: The multivariable logistic regression analysis included variables with a P<0.05 in the univariate logistic regression analysis and investigated the independent predictors affecting in-hospital MVA after PCI in patients with STEMI in the training cohort. The construction of a nomogram model used independent predictors to predict the risk of in-hospital MVA, and C-index, Hosmer-Lemeshow (HL) test, calibration curves, decision curve analysis (DCA), and receiver operating characteristic (ROC) were used to validate the nomogram. Results: Killip class [OR=5.034 (95% CI: 1.596-15.809), P=0.005], CK-MB [OR=1.002 (95% CI: 1.001-1.004), P=0.022], serum potassium [OR=0.618 (95% CI: 0.406-0.918), P=0.020], NLR [OR=1.073 (95% CI: 1.034-1.115), P<0.001], and monocyte [OR=1.974 (95% CI: 1.376-2.925), P<0.001] were the independent predictors of in-hospital MVA after PCI in patients with STEMI. A nomogram including the 5 independent predictors was developed to predict the risk of in-hospital MVA. The C-index, equivalent to the area under the ROC curve (AUC), was 0.803 (95% confidence interval [CI]: 0.738-0.868) in the training cohort, and 0.801 (95% CI:0.692-0.911) in the validation cohort, showing that the nomogram had a good discrimination. The HL test ( Conclusion: Killip class, CK-MB, serum potassium, NLR, and monocyte were independent factors for in-hospital MVA after PCI in patients with STEMI. The nomogram model constructed based on the above factors to predict the risk of in-hospital MVA had satisfactory discrimination, calibration, and clinical effectiveness, and was an excellent tool for early prediction of the risk of in-hospital MVA after PCI in patients with STEMI.
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