Key result
A linear discriminant analysis model outperformed seven other machine learning models in predicting all-cause mortality within 1 year in ACS patients after PCI, achieving an AUC of 0.83.
Why the study?
Up to 30% of patients with ACS die from adverse events such as renal failure and MI, making accurate risk prediction essential to improve prognosis.
Can machine learning models accurately predict acute kidney injury, myocardial infarction during hospitalization, and all-cause mortality within 1 year in patients with ACS who underwent PCI?
Observational (n=5,240)
Can machine learning models accurately predict acute kidney injury, myocardial infarction during hospitalization, and all-cause mortality within 1 year in patients with ACS who underwent PCI?
Effect estimate: AUC 0.83
A linear discriminant analysis machine learning model using easily accessible in-hospital variables can effectively predict 1-year mortality and in-hospital adverse events in ACS patients undergoing PCI.
ML models may refine ACS risk prediction post-PCI; leaves open prospective validation against existing scores.
BACKGROUND: Up to 30% of patients with acute coronary syndrome (ACS) die from adverse events, mainly renal failure and myocardial infarction (MI). Accurate prediction of adverse events is therefore essential to improve patient prognosis. HYPOTHESIS: Machine learning (ML) methods can accurately identify risk factors and predict adverse events. METHODS: A total of 5240 patients diagnosed with ACS who underwent PCI were enrolled and followed for 1 year. Support vector machine, extreme gradient boosting, adaptive boosting, K-nearest neighbors, random forest, decision tree, categorical boosting, and linear discriminant analysis (LDA) were developed with 10-fold cross-validation to predict acute kidney injury (AKI), MI during hospitalization, and all-cause mortality within 1 year. Features with mean Shapley Additive exPlanations score >0.1 were screened by XGBoost method as input for model construction. Accuracy, F1 score, area under curve (AUC), and precision/recall curve were used to evaluate the performance of the models. RESULTS: Overall, 2.6% of patients died within 1 year, 4.2% had AKI, and 4.7% had MI during hospitalization. The LDA model was superior to the other seven ML models, with an AUC of 0.83, F1 score of 0.90, accuracy of 0.85, recall of 0.85, specificity of 0.68, and precision of 0.99 in predicting all-cause mortality. For AKI and MI, the LDA model also showed good discriminating capacity with an AUC of 0.74. CONCLUSION: The LDA model, using easily accessible variables from in-hospital patients, showed the potential to effectively predict the risk of adverse events and mortality within 1 year in ACS patients after PCI.
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Song et al. (2023) conducted an observational in Acute coronary syndrome (ACS) (n=5,240). Linear discriminant analysis (LDA) model vs. Other machine learning models (SVM, XGBoost, AdaBoost, KNN, Random Forest, Decision Tree, CatBoost) was evaluated on All-cause mortality within 1 year (AUC 0.83). A linear discriminant analysis model outperformed seven other machine learning models in predicting all-cause mortality within 1 year in ACS patients after PCI, achieving an AUC of 0.83.
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