Acute myocardial infarction (AMI) is the most common type of cardiovascular disease in clinical practice, characterized by rapid progression, and a high mortality rate [1]. Its pathogenesis is primarily based on atherosclerotic lesions in the coronary arteries, leading to plaque rupture, platelet aggregation, and thrombosis, which ultimately cause prolonged and severe ischemic necrosis of the myocardium, and in severe cases, may endanger the patient’s life [2]. Percutaneous coronary intervention (PCI) has become the most widely used therapeutic approach in clinical practice because it can safely, rapidly, and efficiently help AMI patients restore coronary perfusion and alleviate acute-phase symptoms. However, studies have indicated that while PCI provides significant benefits, patients remain at risk of major adverse cardiovascular events (MACE) such as recurrent AMI, heart failure, malignant arrhythmias, and sudden cardiac death post PCI [3], with incidence rates as high as 27% [4]. The occurrence of MACE not only reduces the patient’s quality of life and increases their economic and social burdens, but also severely impacts their long-term prognosis and well-being [5]. Although current studies on MACE risk assessment in AMI patients have made some progress [6, 7], methodological limitations prevent a comprehensive understanding of the complex mechanisms and the strength of associations between clinical data, thus limiting the clinical applicability of these findings. In recent years, the rapid development of machine learning technology and its application in clinical data analysis have gained significant attention from medical institutions and researchers for their accurate and efficient predictive performance and clinical decision-making. By comprehensively analyzing large volumes of clinical and biochemical data, machine learning technology identifies risk factors that may be overlooked by traditional methods and plays a crucial role in guiding healthcare professionals to conduct more accurate risk assessments and make informed clinical decisions [8]. According to the results of previous studies, machine learning models have unlimited potential and value in dealing with complex and high-dimensional cardiovascular disease data and postoperative complication risk prediction [9]. It can help doctors better understand patients’ individualized risk of disease and adjust their treatment strategies accordingly, which can better optimize a patient’s long-term treatment outcomes [10].

In this study, we collected clinical data from patients with ST-segment elevation myocardial infarction (STEMI) admitted to the Department of Cardiology of a tertiary care hospital in Anhui Province, China, and constructed a risk prediction model for MACE in STEMI patients with the help of advanced machine learning algorithms, to provide effective assessment tools and methodological references to help healthcare professionals understand the risk of the disease in more depth and to formulate personalized scientific management strategies.

In this study, we employed four machine learning algorithms to conduct an in-depth analysis and modeling of the demographic, laboratory, and clinical characteristics of acute STEMI patients. Our goal was to accurately predict the risk of MACE following PCI in STEMI patients. After thoroughly assessing the importance of characteristic variables and comparing model performance, the logistic regression model proved to be the most effective in terms of discriminatory power, accuracy, and clinical applicability. By ranking feature importance, we identified seven key variables—Killip, Gensini, BUN, HR, CR, GLT, and PCT—that significantly impacted the prediction model. These variables are discussed in detail below.

Previous research has established that MACE after PCI is a significant contributor to prolonged hospital stays, increased healthcare costs, and elevated mortality risk in STEMI patients. This can significantly affect their long-term prognosis and health-related quality of life [14, 15]. Consequently, early identification of critical risk factors for MACE following PCI in STEMI patients, along with timely interventions, is crucial.

The Killip classification, an important indicator of heart failure severity, was identified in this study as a strong predictor of MACE. This aligns with the findings of Takasaki et al, who showed that higher Killip classifications were strongly correlated with both short- and long-term mortality, particularly in post-STEMI patients [16]. This correlation likely arises because heart failure is a direct result of diminished myocardial contractility, which is closely linked to elevated inflammatory markers, endothelial dysfunction, and cardiac remodeling. Furthermore, heart failure often coincides with reduced renal function, resulting in fluid retention and increased cardiac workload, thereby elevating the risk of adverse cardiac events [17]. Clinically, STEMI patients with high Killip classifications require more aggressive monitoring and interventions. This may involve the use of diuretics to manage fluid overload, as well as medications such as $\beta$-blockers and angiotensin converting enzyme (ACE) inhibitors to improve cardiac function. These measures are critical in reducing post-PCI adverse events and improving long-term patient outcomes.

The Gensini score, which quantifies the severity of coronary lesions based on the degree of stenosis and lesion location seen on coronary angiography, was also identified as an independent predictor of MACE in STEMI patients after a PCI. This finding is consistent with previous studies [18, 19]. The Gensini score provides valuable insights into the severity and prognosis of STEMI by accounting for the number, location, and extent of coronary lesions [20]. For patients with high Gensini scores, clinical teams should be proactive in adjusting treatment strategies, providing close follow-up care, and focusing on delaying the progression of coronary stenosis to improve therapeutic and rehabilitative outcomes.

Additionally, our study found that BUN levels were closely linked to the risk of MACE after PCI in STEMI patients. BUN, a byproduct of protein metabolism, reflects early cardiac and renal hemodynamic changes and serves as a strong predictor of poor cardiovascular outcomes, as supported by numerous studies [21, 22, 23]. Horiuchi et al. [21] also demonstrated that high BUN levels significantly increased the risk of MACE in STEMI patients post-PCI, leading to longer hospital stays and higher mortality rates.

HR, a common clinical marker of cardiac function, has received increasing attention for its predictive value in cardiovascular disease. A chronically elevated heart rate is a key risk factor for adverse cardiovascular events, heart failure, and longer hospitalizations [24, 25, 26, 27, 28]. Sympathetic hyperactivity and excessive catecholamine secretion are common causes of increased heart rate, which leads to higher intravascular pressure, damage to the coronary endothelium, and the promotion of inflammatory factors. These processes accelerate atherosclerosis, increase the risk of arrhythmias, and elevate the likelihood of recurrent myocardial infarction. Effective heart rate management through $\beta$-blockers and other medications can alleviate patient symptoms and reduce adverse events in STEMI patients.

Our study also revealed that CR, an indicator of renal function, was an important predictor of MACE following PCI in STEMI patients. This aligns with previous studies, which showed that high creatinine levels correlate with a 3 to 5 times greater risk of death in STEMI patients [29, 30, 31]. Persistent high creatinine levels can indicate systemic congestion and fluid overload, leading to cardiac dysfunction and an increased risk of heart failure and arrhythmias. Monitoring renal function in post-PCI patients is essential to better tailor treatment strategies and reduce the risk of mortality.

GLT, a metabolic enzyme, also showed significant predictive value for MACE in STEMI patients in our study. While the relationship between GLT and cardiovascular diseases is still not fully understood, its role in metabolic regulation warrants further research to clarify its impact on adverse cardiac events.

Finally, PCT, an indicator of platelet function, was positively associated with MACE risk in this study, a finding consistent with the work of Reddy et al. [32]. Higher PCT levels suggest increased platelet aggregation, accelerating coronary plaque formation and an increased risk of cardiovascular disease. This may be due to the fact that elevated PCT reduces vasodilation and myocardial blood flow after PCI, contributing to further cardiac damage [33]. Additional large-scale studies are needed to further investigate the link between PCT and the risk of MACE in STEMI patients.

This study utilized a retrospective research methodology. Therefore, the results may be affected by sample selection bias. There are numerous factors affecting the occurrence of MACE after PCI in patients with STEMI, and the present study may not have taken into account all the potential predictive variables, especially the relevant variables such as lifestyle and socioeconomic status, which have a significant impact on the risk of developing cardiovascular disease. Future studies will need to further incorporate these factors into the constructed model in order to more comprehensively assess the risk for adverse cardiovascular events in patients with an acute STEMI after a PCI. In addition, our model was constructed and validated based on a single dataset, which may limit the applicability and generalization of the model to other patient groups. Therefore, in order to better validate and optimize the predictive performance of the model, further larger sample, prospective, multicenter cohort studies will need to be conducted in the future.

In summary, in this study, a total of 7 important characteristic variables related to the occurrence of MACE after PCI in patients with an acute STEMI were screened based on ML algorithms and incorporated into an optimal logistic regression model, which can provide sufficient evidence-based support and methodological references for the early identification of high-risk groups of patients at risk for developing MACE after a PCI. This will help healthcare workers to more deeply understand the complex relationship between these indicators and the disease, and make targeted clinical scientific decisions, so as to improve more favorable patient prognoses and enhance long-term quality of life. The powerful predictive power of the logistic regression model in this study highlights the great potential of ML in the management and application of cardiovascular diseases. The advantage of such models lies in their ability to efficiently handle nonlinear relationships and a large number of interaction effects among data, yielding more accurate and reliable results, and can be extended, applied, and optimized to suit different clinical scenarios. However, to transform these models into practical tools in clinical practice, a series of factors will still need to be incorporated, including the quality and accessibility of data, the interpretive power of the models, and their integration with existing clinical workflows. In addition, with the rise of personalized medicine, the application of ML models in providing individualized treatment recommendations will need to be further explored and investigated in the future.

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

The study was designed by MC, JZ, and CLC. The article was written by MC, CLS, and LY. Data collection was carried out with the participation of CLS and TZ. Data analysis was performed by MC and LY. All authors made significant contributions to the reported work. All authors participated in the editorial revision of the manuscript. All authors read and approved the final manuscript. All authors participated fully and agreed to be responsible for all aspects of the work.

The study protocol and informed consent procedures were approved by the Ethics Committee of the Second People’s Hospital of Hefei (ethical approval number: 2020-ke-058). All methods were performed following the Declaration of Helsinki. Informed written consent for participate without direct personal identification details (such as name and address) was obtained from all the participants.

This research was funded by Scientific research project of Anhui Provincial Health Construction Committee (AHWJ2023BAa20168).

The authors declare no conflict of interest.

See Table 5.