A Risk Score Model for Predicting Cardiac Rupture After Acute Myocardial Infarction
Cardiac rupture (CR) is one of the most severe and lethal complications of acute myocardial infarction (AMI). Despite advancements in medical treatments, CR remains associated with extremely poor outcomes, particularly in the absence of timely intervention. Historically, CR has been a significant cause of mortality in AMI patients, with incidences ranging from 2% to 6.2% in the pre-reperfusion era. Although the widespread adoption of percutaneous coronary intervention (PCI) has reduced the incidence of CR to less than 1%, it continues to pose a significant threat to patient survival. This study aimed to develop a simple and effective risk score model to predict CR after AMI, providing clinicians with a tool to identify high-risk patients early and improve outcomes.
Background and Significance
Coronary artery disease (CAD) is a leading cause of death globally, and AMI is a critical manifestation of CAD. Among the mechanical complications of AMI, free wall rupture (FWR) and ventricular septal rupture (VSR) are particularly devastating. FWR has historically accounted for up to 30% of AMI-related mortality, while VSR has been associated with in-hospital mortality rates of 45% for surgical treatment and 90% for medical management. Despite the decline in CR incidence due to modern therapies, the mortality rate remains alarmingly high, underscoring the need for early identification and intervention.
Previous studies have identified several risk factors for CR, including older age, female gender, and delayed reperfusion. However, no validated risk score model has been established to predict CR after AMI. The Global Registry of Acute Coronary Events (GRACE) risk score, commonly used for risk stratification in acute coronary syndromes (ACS), has not been specifically validated for predicting CR. This study sought to fill this gap by developing a novel risk score model tailored to CR prediction.
Study Design and Methods
This retrospective case-control study analyzed data from 53 consecutive patients who experienced CR after AMI between January 1, 2010, and December 31, 2017. The control group consisted of 524 patients randomly selected from 7,932 AMI patients without CR, maintaining a 1:10 ratio. Patients with incomplete records were excluded. The study adhered to the Declaration of Helsinki and received approval from the Institutional Ethics Committee of Beijing Chaoyang Hospital. Informed consent was waived due to the retrospective nature of the study.
AMI was defined as ST-segment elevation myocardial infarction (STEMI) or non-STEMI based on standard diagnostic criteria. CR was classified as FWR or VSR, diagnosed using echocardiography or clinical findings such as sudden cardiogenic shock, pericardial effusion, or cardiac murmurs. Data on demographics, medical history, laboratory parameters, and treatment details were collected. The GRACE risk score was calculated for all patients, and statistical analyses were performed using SPSS and R software.
Results
Among the 7,985 AMI patients included in the study, 53 (0.67%) experienced CR, comprising 39 cases of FWR (0.49%) and 14 cases of VSR (0.18%). The in-hospital mortality rate was significantly higher in CR patients (92.5%) compared to non-CR patients (4.01%). The median time from AMI onset to CR was 3.1 days, with 48.7% of FWR cases and 42.9% of VSR cases occurring within 24 hours of symptom onset.
Univariate analysis identified several factors associated with CR, including older age, female gender, higher heart rate at admission, lower body mass index (BMI), lower left ventricular ejection fraction (LVEF), and lack of primary PCI (pPCI) treatment. Multivariate logistic regression confirmed these factors as independent predictors of CR. The GRACE risk score was significantly higher in CR patients (198.04 ± 41.03) compared to non-CR patients (165.32 ± 37.54), but its discriminatory power for CR prediction was modest (AUC = 0.716).
The study developed a novel risk score model based on six independent predictors: female gender (2 points), no pPCI treatment (2 points), LVEF < 40% (1 point), heart rate ≥ 94 beats/min (2 points), BMI < 25 kg/m² (1 point), and age ≥ 68 years (4 points). The total risk score ranged from 0 to 12. Patients were categorized into low-risk (score ≤ 3), moderate-risk (score 4–7), and high-risk (score ≥ 8) groups. The incidence of CR increased linearly with the risk score, from 0% in the low-risk group to 100% in patients with a score ≥ 11. The risk score model demonstrated excellent discriminatory power (AUC = 0.843) and accurately predicted 48 of the 53 in-hospital CR events.
Discussion
The findings of this study highlight the critical role of early risk stratification in managing AMI patients. The developed risk score model provides a simple and effective tool for identifying patients at high risk of CR, enabling timely intervention and potentially improving outcomes. The model’s performance was superior to the GRACE risk score, which, while useful for general ACS risk stratification, lacks specificity for CR prediction.
Older age emerged as the most significant predictor of CR, consistent with previous studies. Female gender was also independently associated with CR, possibly due to differences in pathophysiology and delayed presentation. Lower BMI, often overlooked in AMI risk assessment, was identified as a novel predictor, aligning with studies showing a protective effect of higher BMI in AMI outcomes. The protective role of pPCI underscores the importance of early reperfusion in preventing CR.
The study’s limitations include its retrospective design, relatively small sample size, and lack of an external validation cohort. Additionally, the absence of data on collateral vessel formation may have influenced the results. Future studies should address these limitations and explore the integration of the risk score model into clinical practice.
Conclusion
This study successfully developed a novel risk score model for predicting CR after AMI. The model, based on six easily obtainable clinical variables, demonstrated high accuracy and discriminatory power. By enabling early identification of high-risk patients, the model has the potential to improve clinical outcomes and reduce mortality associated with CR. Further validation and prospective studies are needed to confirm its utility and applicability in diverse patient populations.
doi.org/10.1097/CM9.0000000000000175
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