What is the clinical evidence from this study?

Study design: Case-Control. Population: Acute Myocardial Infarction complicated by Cardiac Rupture (n=212). Intervention: Systemic Inflammation Response Index (SIRI) vs. Patients without cardiac rupture. Primary outcome: Cardiac rupture (OR 1.105, 95% CI 1.022-1.196, p=0.012).

What does this research mean for the field?

The Systemic Inflammation Response Index (SIRI), admission heart rate, and Killip classification independently increase the risk of cardiac rupture in acute myocardial infarction patients, while primary PCI and early ACEI/ARB administration are protective. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The study aims to identify risk factors for cardiac rupture following acute myocardial infarction and evaluate a risk prediction model.

April 25, 2026Open Access

Risk Factors for Cardiac Rupture After Acute Myocardial Infarction and Development of a Risk Prediction Model

Key Result

Systemic Inflammation Response Index (SIRI) independently increased the risk of cardiac rupture in acute myocardial infarction patients (OR 1.105), while primary PCI and ACEI/ARB were protective.

Key Points

The study aims to identify risk factors for cardiac rupture following acute myocardial infarction and evaluate a risk prediction model.
Enrolled 53 patients with cardiac rupture and 159 control patients without cardiac rupture.
Collected baseline data, clinical indicators, and calculated the systemic inflammation response index (SIRI).
Constructed a nomogram prediction model using logistic regression and evaluated its efficacy with ROC curve analysis.
Admission heart rate (OR = 1.050, 95% CI = 1.024–1.075, P < 0.001) and Killip classification (OR = 2.092, 95% CI = 1.460–2.997, P < 0.001) identified as independent risk factors.
SIRI also identified as an independent risk factor (OR = 1.105, 95% CI = 1.022–1.196, P = 0.012).
The ROC curve model showed an area under the curve (AUC) of 0.885.

Study Design

Type

Case-Control

Multicenter

Structured PICO

Population

212 patients aged ≥60 years diagnosed with ST-segment elevation myocardial infarction (STEMI) upon admission, including 53 patients complicated with cardiac rupture (CR) and 159 matched patients without CR, admitted to a single center in China.

Outcome

Occurrence of cardiac rupture (CR) during hospitalizationhard clinical

Elevated admission heart rate, higher Killip class, and increased systemic inflammation response index (SIRI) are independent risk factors for cardiac rupture after acute myocardial infarction, while primary PCI and early ACEI/ARB use are protective.

Limitations

Single-center retrospective design limits causal inference and may introduce selection bias
10-year study period may introduce temporal bias due to evolving clinical practices
Incomplete data on echocardiographic details, door-to-needle/balloon times, and certain clinical presentations
Restricted to patients aged ≥60 years, limiting generalizability to younger populations
Small number of cardiac rupture events raises potential overfitting risk
Lack of external validation in prospective multicenter cohorts

Abstract

Aim: To investigate the influencing factors for acute myocardial infarction (AMI) complicated by cardiac rupture (CR),evaluate the predictive value of the systemic inflammation response index (SIRI), and construct a clinically practical risk prediction model. Methods: A total of 53 AMI patients complicated with CR admitted to Tianshui First People’s Hospital from January 2013 to December 2023 were enrolled as the CR group.During the same period, 159 AMI patients without CR were selected as the control group at a 1:3 ratio, matched for age and sex.Baseline data, clinical indicators, and laboratory test results of patients in both groups were collected, and SIRI was calculated. Lasso regression was used to screen core variables, multivariate Logistic regression analysis was performed to identify independent influencing factors, a nomogram prediction model was constructed based on key variables, and the receiver operating characteristic (ROC) curve was used to evaluate the model’s efficacy. Results: Multivariate Logistic regression analysis showed that admission heart rate (OR = 1.050,95% CI = 1.024– 1.075, P < 0.001), Killip classification (OR = 2.092,95% CI = 1.460– 2.997, P < 0.001) and SIRI (OR = 1.105,95% CI = 1.022– 1.196, P = 0.012) were independent risk factors for CR in AMI patients. Primary PCI (OR = 0.239,95% CI = 0.097– 0.589, P = 0.002) and taking ACEI / ARB drugs within 24 hours (OR = 0.173,95% CI = 0.060– 0.500, P = 0.001) were protective factors. The ROC curve model constructed based on the above five indicators has an area under the curve (AUC) of 0.885. Conclusion: Admission heart rate, Killip classification, and systemic inflammatory response index are independent risk factors for AMI with CR. Primary PCI and the administration of ACEI/ARB within 24 hours of admission were identified as protective factors against CR. The nomogram model demonstrated good predictive value for the occurrence of cardiac rupture in patients with AMI. Keywords: acute myocardial infarction, cardiac rupture, systemic inflammation response index, predictive factors, nomogram

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Discussion

Authors

Jianfang Gao

Peipei Jia

Zhibin Hong

Journals

International Journal of General Medicine

Actions

Institutions

Gansu University of Traditional Chinese Medicine

The First People's Hospital of Tianmen

References and Citations

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Risk Factors for Cardiac Rupture After Acute Myocardial Infarction and Development of a Risk Prediction Model

Key Result

Key Points

Study Design

Structured PICO

Limitations

Abstract

Citation Network

Connected Papers

Discussion

Authors

Journals

Actions

Institutions

References and Citations

Citation Network

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Discussion

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