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Updated: Jan 14, 2026
Murine Myocardial Infarction Model using Permanent Ligation of Left Anterior Descending Coronary Artery
Published on: August 16, 2019
Multiparametric integration of cardiac markers in differentiating myocardial infarction with non-obstructive coronary
Qing Huang1, Peixuan Zhao1, Yi He1
1Department of Cardiovascular Division, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China.
Background:
Myocardial infarction with non-obstructive coronary arteries (MINOCA) represents a heterogeneous clinical entity requiring angiography-assisted diagnostic confirmation.
Objectives:
This study proposes an innovative predictive algorithm for identifying MINOCA using non-invasive variables.
Methods:
This retrospective cohort study included patients with acute myocardial infarction admitted to the Department of Cardiology at the Third Affiliated Hospital of Soochow University from June 2021 to October 2024. We systematically collected and analyzed baseline clinical data of demographics, imaging, and laboratory tests. Potential predictors were screened via Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, followed by multivariate logistic modeling. Performance evaluation included discrimination metrics (receiver operating characteristic), calibration assessment, and decision curve analysis. A nomogram was created to visualize the multivariable prediction model, with internal validation achieved through bootstrap resampling.
Results:
613 patients were included, and 46 had MINOCA (7.50 %). Using LASSO for variable selection, five predictors were retained at log(λ) = -3.90: High-sensitivity cardiac troponin I, type of myocardial infarction, gender, age, and diabetes mellitus. In the subsequent multivariable logistic regression, all five were independently associated with MINOCA(P < 0.05). Among these predictors, high-sensitivity cardiac troponin I emerged as the best biomarker for MINOCA (AUC = 0.673, 95 % CI: 0.604-0.743). Finally, a multifactorial model was built with an AUC of 0.796 (95 % CI: 0.761 - 0.831). A dynamic calculator based on the multifactorial model was also deployed and is available in open-access format.
Conclusion:
The model is efficacious in facilitating the accurate diagnosis of MINOCA, assisting clinicians with early identification of MINOCA, thereby improving patients' prognostic outcomes.
