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Machine Learning-Based Prediction of Long-Term Mortality in STEMI Patients Using Clinical, Laboratory, and
Gökhan Keskin1, Abdulkadir Çakmak1, Mehmet Uğur Çalışkan1
1Department of Cardiology, Faculty of Medicine, Amasya University, Amasya 05200, Türkiye.
Insights
The XGBoost machine learning model accurately predicts long-term mortality risk in ST-segment elevation myocardial infarction (STEMI) patients undergoing primary percutaneous coronary intervention (pPCI). Novel inflammatory-metabolic indices improve risk stratification.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Informatics
Background:
- ST-segment elevation myocardial infarction (STEMI) poses a significant long-term mortality risk.
- Primary percutaneous coronary intervention (pPCI) is a standard treatment for STEMI.
- Accurate prediction of mortality risk is crucial for patient management and treatment optimization.
Purpose of the Study:
- To compare the performance of various machine learning (ML) models in predicting long-term mortality in STEMI patients post-pPCI.
- To evaluate the prognostic value of novel inflammatory-metabolic indices in this patient cohort.
- To identify key predictors of mortality for improved risk stratification.
Main Methods:
- Retrospective analysis of 329 STEMI patients who underwent pPCI.
- Development and comparison of five ML algorithms: Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Artificial Neural Networks (ANN).
- Performance evaluation using accuracy, sensitivity, specificity, and ROC-AUC; SHAP analysis for model interpretation.
Main Results:
- The XGBoost model demonstrated superior performance with 98.99% accuracy, 0.999 ROC-AUC, and 100% sensitivity.
- Higher door-to-balloon time (DTBT), Systemic Inflammatory Response Index (SIRI), and pan-immune-inflammation value (PIV) were associated with mortality.
- Lower body mass index (BMI), Prognostic Nutritional Index (PNI), and Advanced Lung Cancer Inflammation Index (ALI) were observed in the mortality group.
- SHAP analysis identified DTBT, albumin, and ALI as the strongest mortality predictors.
Conclusions:
- The XGBoost algorithm is a highly accurate and reliable tool for predicting long-term mortality in STEMI patients.
- Integrating novel inflammatory-metabolic indices, such as ALI and TyG, alongside DTBT into ML models can enhance early risk identification and stratification.
- These findings suggest a potential for improved clinical decision-making and patient outcomes through advanced predictive modeling.
Abstract:
Background: This study aims to compare the performance of machine learning (ML) models developed to predict long-term mortality risk in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (pPCI) and to investigate the prognostic value of novel inflammatory-metabolic indices. Methods: In this retrospective study, 329 consecutive STEMI patients who underwent pPCI (292 survivors, 37 deaths) were included. Five ML algorithms-Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Artificial Neural Networks (ANN)-were developed for mortality prediction. Model performance was evaluated using accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC). SHAP (Shapley Additive exPlanations) analysis was used to interpret model decision mechanisms. Results: The mortality group had significantly higher door-to-balloon time (DTBT), Systemic Inflammatory Response Index (SIRI), pan-immune-inflammation value (PIV), whereas body mass index (BMI), Prognostic Nutritional Index (PNI), and Advanced Lung Cancer Inflammation Index (ALI) values were significantly lower (p < 0.001). Among the ML models, the XGBoost algorithm achieved the best performance, with 98.99% accuracy, a ROC-AUC of 0.999, and 100% sensitivity, correctly identifying all mortality cases. SHAP analysis identified DTBT, albumin level, and ALI score as the strongest predictors of mortality, in that order. Conclusions: The XGBoost algorithm provides high accuracy and reliability for predicting long-term mortality in STEMI patients. Beyond DTBT, integrating novel indices-especially ALI and TyG-into ML models may serve as a powerful clinical tool for early identification of high-risk patients and improved risk stratification.
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