ArticleClinical cardiology2024
Incorporating Inflammation Biomarker-Driven Multivariate Predictive Model for Coronary Microcirculatory Dysfunction in Acute Myocardial Infarction Following Emergency Percutaneous Coronary Intervention.
Article in Clinical cardiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Multiple machine-learning-driven metabolic frameworks for long-term prognostic risk assessment in patients with coexisting hypertension and obstructive sleep apnea:insights from a multicenter cohort study.Frontiers in physiology · 2026Article
- Multimodal Data-Driven Explainable Prognostic Model for Major Adverse Cardiovascular Events Prediction in Patients With Unstable Angina and Heart Failure With Preserved Ejection Fraction: Multicenter, Cross-Regional Cohort Study.Journal of medical Internet research · 2025Article
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9 authors.
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Abstract
backgroundDespite patients with successful revascularization as evidenced by angiographic findings, inadequate clinical management of coronary microcirculatory dysfunction (CMD) may result in preventable adverse outcomes. Therefore, it is imperative to use a multimodal data‑driven predictive model for the occurrence of CMD in patients with acute myocardial infarction (AMI) following emergency percutaneous coronary intervention (PCI).
methodsA prospective case-control analysis was conducted on a cohort of 77 patients with AMI who underwent PCI. The most informative predictors were selected for the predictive model through the application of LASSO analysis and multi-factor logistic regression. The diagnosis of CMD is based on findings from cardiac magnetic resonance (CMR).
resultsBased on the findings from LASSO analysis and multi-factor logistic regression, variables including sex, neutrophil-to-lymphocyte ratio (NLR), Gensini score, and diabetes mellitus were identified as independent predictors for the development of CMD in AMI patients who underwent emergency PCI. The predictive model was evaluated using bootstrap self-sampling 500 times. The resulting predictive model demonstrated an AUC value of 0.897 (95% CI: 0.827-0.958). The calibration curves exhibited good concordance between the predictions generated by the model and the CMR analysis. Furthermore, decision curve analysis revealed that the predictive model provided valuable clinical benefit in predicting CMD.
conclusionsThe multivariate predictive model, constructed using readily available clinical variables in patients with AMI who underwent PCI, demonstrates satisfactory predictability for identifying comorbid CMD, thereby facilitating the identification of high-risk patients.
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