Related Experiment Video
Updated: Jan 12, 2026
Real-time Pressure-volume Analysis of Acute Myocardial Infarction in Mice
Published on: July 2, 2018
Establishment and Evaluation of a Predictive Model for Cardiac Rupture Risk in Acute Myocardial Infarction Patients
Tuersunayi Yisimitila1, Alimijiang Abulimiti2, Bumayreyemu Mamuti1
1Xinjiang Emergency Center, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China.
Insights
This study identifies key risk factors for cardiac rupture (CR) in acute myocardial infarction (AMI) patients. A validated nomogram model predicts CR risk, aiding clinical decision-making for AMI management.
Area of Science:
- Cardiology
- Medical Prognostics
Background:
- Cardiac rupture (CR) is a severe complication of acute myocardial infarction (AMI).
- Identifying risk factors and predicting CR is crucial for patient outcomes.
Purpose of the Study:
- To analyze risk factors for CR in AMI patients.
- To develop and validate a prognostic prediction model for CR.
Main Methods:
- Retrospective analysis of 89 CR patients and 451 control AMI patients.
- LASSO regression and logistic regression identified risk factors.
- A nomogram model was developed and validated using ROC, Hosmer-Lemeshow, and DCA.
Main Results:
- Six key indicators identified: age, sex, Killip classification, ACEI/ARB, CKMB, and ejection fraction (EF).
- The nomogram model demonstrated good predictive accuracy (AUC 0.874 training, 0.820 validation).
- The model showed clinical validity and good calibration in both training and validation sets.
Conclusions:
- Higher age, advanced Killip classification, and elevated CKMB are risk factors for CR.
- ACEI/ARB therapy and higher EF are protective factors against CR.
- The developed nomogram is a clinically valid tool for CR risk prediction in AMI patients.
Objective:
To identify risk factors for cardiac rupture (CR) in patients with acute myocardial infarction (AMI) and develop a validated prognostic model.
Methods:
This study included 89 consecutive AMI patients with CR and 451 randomly selected controls without CR from a pool of 4,559 patients (2013-2024). Risk factors were identified using least absolute shrinkage and selection operator regression alongside univariate and multivariate logistic regression. A nomogram prediction model was built and evaluated using the receiver operating characteristic curve, calibration plots, the Hosmer-Lemeshow test, and decision curve analysis.
Results:
Among 540 participants (74.3% male, 25.7% female), CR occurred in 89 (16.5%) cases. Univariate analysis identified 24 associated factors. Least absolute shrinkage and selection operator regression refined these to six key predictors for the final model: older age, male sex, higher Killip classification, lower use of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers medications, higher CK-MB levels, and lower ejection fraction. The nomogram demonstrated strong predictive performance, with an area under the curve of 0.874 in the training set and 0.820 in the validation set. Calibration curves showed excellent agreement between predictions and observations, and the Hosmer-Lemeshow test indicated good fit ( P > 0.05).
Conclusion:
Advanced age, higher Killip class, and elevated creatine kinase MB are risk factors for CR, while angiotensin-converting enzyme inhibitors/angiotensin receptor blockers therapy and higher ejection fraction are protective. The developed nomogram provides a reliable tool for individualized risk prediction.
Related Concept Videos
01:30Acute Coronary Syndrome III: Diagnostic Studies
01:20Blood Studies for Cardiovascular System I: Cardiac Biomarkers
The essential diagnostic tools for detecting myocardial necrosis and monitoring individuals suspected of having acute coronary syndrome (ACS) include:
Troponins
Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...