ST-elevation myocardial infarction (STEMI), the most serious type of cardiovascular disease, is one of the leading causes of mortality worldwide [1, 2, 3]. Multiple longitudinal studies have shown that mortality from STEMI is higher in women than in men [4, 5, 6, 7, 8]. Risk stratification is critical in identifying high-risk patients and assisting physicians in decision making [9, 10]. The traditional risk assessment tools are the Global Registry of Acute Coronary Events (GRACE) [11] score and the Thrombolysis in Myocardial Infarction (TIMI) score [12, 13], but the following three conditions are usually taken as major limitations for these tools: (1) the predictors are not immediately available on admission, and medical history is unreliable; (2) these tools were used without accounting for sex-specific disease characteristics of STEMI, whereas growing evidence has demonstrated sex differences in both symptom presentation and management efficacy STEMI patients [8, 14]. The symptoms of myocardial infarction (MI) in women patients are atypical, which make women often suffer from an incorrect or delayed management [15, 16, 17, 18, 19]; (3) These tools were developed based on a traditional statistical method, which may lead to the loss of important information [20, 21, 22, 23]. Recently, the GRACE 3.0 score, based on machine learning (ML), was developed to reduce sex inequalities, but it was specially designed for the risk assessment of non-ST-elevation acute coronary syndromes (NSTE-ACS) [7]. Therefore, it is necessary to develop a new risk-prediction model for women with STEMI using predictors that can be obtained at the time of initial evaluation.

ML algorithms can capture nonlinear relationships among clinical variables, and have many successful applications [24, 25, 26, 27, 28]. However, real-world medical data are often imbalanced. When trained with imbalanced data, the developed ML models can be overwhelmed by the majority class (i.e., survival group) and can ignore the minority class (i.e., death group) [29], which is the focus of clinical attention. To alleviate this problem, an effective strategy is data preprocessing. The data-preprocessing approach is to resample the imbalanced training set prior to model training. In order to create the balanced training set, the original imbalanced data set can be oversampled for the minority class and/or undersampled for the majority class [30]. Since the undersampling strategy leads to the loss of information from the majority class, we adopted the adaptive synthetic (ADASYN) oversampling approach [31], which has been proven effective [32]. Due to the “black box” nature of ML algorithms, the SHapley Additive exPlanations (SHAP) value was employed to explain the predictors’ impact on the outcome [33].

The aims of this study were to: (1) develop prediction models for all-cause in-hospital mortality in women with STEMI using four commonly used ML algorithms combined with the ADASYN sampling approach, and (2) explain the prediction models with SHAP values.

STEMI is the leading cause of death among women worldwide [1, 2, 3, 4, 5, 6, 7, 8], which may be partly attributed to atypical symptoms and insufficient risk assessment. Therefore, in the present study, four commonly used ML algorithms were selected for the development of models to predict the in-hospital mortality in women with STEMI. Additionally, ADASYN was applied in order to improve the performance of the models [31]. The best performing female-specific model achieved an accuracy, sensitivity, specificity, G-mean, and AUC of 72.25%, 82.14%, 71.69%, 76.74% and 79.26%, respectively, leading to a more convenient and effective identification of high-risk patients at the first medical contact.

Consistent with previous studies [4, 41], our results demonstrated that women were more likely than men to have a delay between symptom and medical contact (121 min vs. 116 min, p = 0.0006), lower rates of reperfusion treatment (75.27% vs. 82.89%, p 0.0001), and higher mortality (4.92% vs. 2.37%, p 0.0001). The mechanisms behind these differences may be the following: (1) women with STEMI are more likely to present with multiple non-chest pain symptoms [19, 42, 43], which often results in an incorrect or delayed management; (2) competing responsibilities, as well as embarrassment or fear of disturbing others, lead women to be more likely to wait until symptoms subside rather than seek care [44]; and (3) because of lower socioeconomic status and lower perception for the risk of heart disease, women are less willing to opt for invasive coronary angiography [45, 46, 47]. As a result, physicians, patients, and relatives all tend to choose conservative treatments due to the lack of sex-specific guidelines [48]. Therefore, it is critical to optimize risk assessment and subsequent management, although the traditional risk-assessment tools are far from perfect. As machine learning blossoms, there are many successful applications of machine-learning models in the cardiovascular field. A machine-learning-based model called the PRAISE score was developed for predicting all-cause death, recurrent acute myocardial infarction, and major bleeding after acute coronary syndrome [49], but it was not designed for women. Recently, the GRACE 3.0 score, based on machine learning, was developed to reduce sex inequalities, but it was specifically developed for risk assessment of NSTE-ACS [7].

Although ML algorithms are accurate in capturing complex nonlinear relationships between clinical variables, when trained with imbalanced real-world medical data, the developed models are vulnerable to incorrectly predicting the minority class as the majority class [50], which leads the models to ignore high-risk patients. Therefore, an oversampling technology called ADASYN was applied to generate more samples from the minority class to alleviate the above problem. Compared with undersampling technology, which balances the Training Set by discarding the majority class samples, ADASYN can fully utilize precious medical data, resulting in a higher level of robustness [31]. Due to the “black-box” nature of ML models, the SHAP value was applied for explanation. The SHAP assesses the effect of each feature on results and presents it in an intuitive way [33], which can help doctors better understand how the model works, rather than blindly trusting the predictions.

The present study demonstrated that the female-specific models significantly outperformed the all-population-derived models in predicting in-hospital mortality in women with STEMI, and sex was considered to be an important predictor according to the feature importance scores (Fig. 4). However, women were not well represented in the study sample of the TIMI trial, where they accounted for only 24.7% [12], and in the study sample of the GRACE trial, where they accounted for 33.5% [11]. Additionally, our models can provide predictive results at the initial evaluation, resulting in an improvement in applicability. The 2017 ESC Guidelines recommend an aggressive treatment strategy for high-risk patients [34]. However, physicians, and women with STEMI, are more likely to choose conservative treatment (treatment-risk paradox), which can be inappropriate [51]. The proposed female-specific models can conveniently and effectively identify high-risk patients at the first medical contact, which can provide a basis for physicians to choose intensive treatment for high-risk patients, thereby improving treatment compliance.

This study has several limitations to be acknowledged. First, this is a single-center study. Therefore, the models should be validated in external centers to confirm their generalizability. Nonetheless, the risk prediction model proposed in this study still provides a convenient and effective method to predict in-hospital mortality in women with STEMI. Second, this is a retrospective study. Bias in patient enrollment and data collection is inevitable. However, the patients’ data were collected from a high-quality database, which reflected the real world. Third, the endpoint of this study included only in-hospital mortality, with no information on myocardial infarction, ischemic stroke, or heart failure; information on longitudinal follow-up was not obtained. Thus, further long-term follow-up studies are needed to obtain more detailed and comprehensive information in order to develop more clinically instructive models. Finally, some important predictors were not included in this study, such as creatinine level, myocardial injury biomarkers, and sex-specific risk factors, which attenuated the performance of models and made the comparison to other risk scores impossible. Conversely, our models can be used conveniently and effectively in pre-hospital or emergency departments. In addition, the symptoms of MI are usually atypical in elderly patients and in patients with diabetes, which makes these patients less willing to seek medical service. Therefore there are many papers and literature works that focus on diabetes and the elderly as distinct groups [52, 53, 54, 55]. Accordingly, future studies should focus on applying machine learning to improve the prognosis of these patients.

In this study, four commonly used ML models (DT, RF, and SVM) were developed to predict in-hospital mortality in women with STEMI. The predictors could be obtained at initial evaluation. Additionally, ADASYN was applied to assess and mitigate the effects of class imbalance, thereby improving model performance. By capturing the non-linear association of predictors, the proposed female-specific model could conveniently and effectively identify high-risk female patients at the first medical contact. Therefore, the integration of our female-specific model into daily clinical practice may improve the prognosis of women with STEMI who often suffer from an incorrect or delayed diagnosis.

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

JZho, JZha and GLS designed the research study. JZho, JZha, XMZ, CZ, GLS collected the patient data. PYZ, CL performed the research. YHH provided help and advice on machine learning modeling. PYZ, CL, CZ, XMZ, YHH, JZho, JZha, GLS analyzed the data. PYZ, CL and CZ wrote the manuscript. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript.

This study was conducted after the acquisition of written informed consent from the participating patients and upon the approval by the ethics committee of Tianjin Chest Hospital (2020KY-007-01).

Not applicable.

The trial was funded by National Natural Science Foundation of China (62206197), Applied and Basic Research by Multi-input Foundation of Tianjin (21JCYBJC00820), Tianjin Health Research Project (TJWJ2022QN067), Tianjin Key Research Program of Traditional Chinese Medicine (2022001 and 2023006) and grants from Committee on Science and Technology, Jinnan District, Tianjin (20220108).

The authors declare no conflict of interest.