Key result
XGBoost model predicts severe coronary lesions in premature MI with an AUC of 0.80.
Why the study?
Studies using machine learning to identify target characteristics and develop predictive models for coronary artery disease severity in patients with premature myocardial infarction are limited.
Can machine learning models accurately predict coronary artery disease severity in patients with premature myocardial infarction?
Observational (n=1,111)
No
Can machine learning models accurately predict coronary artery disease severity in patients with premature myocardial infarction?
An XGBoost-based machine learning model can effectively predict severe coronary artery lesions in patients with premature myocardial infarction, offering a tool for pre-intervention risk assessment.
May support pre-intervention risk stratification in premature MI; hypothesis-generating and requires prospective validation.
Background: Studies using machine learning to identify the target characteristics and develop predictive models for coronary artery disease severity in patients with premature myocardial infarction (PMI) are limited. Methods: In this observational study, 1111 PMI patients (≤55 years) at Tianjin Chest Hospital from 2017 to 2022 were selected and divided according to their SYNTAX scores into a low-risk group (≤22) and medium-high-risk group (>22). These groups were further randomly assigned to a training or test set in a ratio of 7:3. Lasso-logistic was initially used to screen out target factors. Subsequently, Lasso-logistic, random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) were used to establish prediction models based on the training set. After comparing prediction performance, the best model was chosen to build a prediction system for coronary artery severity in PMI patients. Results: Glycosylated hemoglobin (HbA1c), angina, apolipoprotein B (ApoB), total bile acid (TBA), B-type natriuretic peptide (BNP), D-dimer, and fibrinogen (Fg) were associated with the severity of lesions. In the test set, the area under the curve (AUC) of Lasso-logistic, RF, KNN, SVM, and XGBoost were 0.792, 0.775, 0.739, 0.656, and 0.800, respectively. XGBoost showed the best prediction performance according to the AUC, accuracy, F1 score, and Brier score. In addition, we used decision curve analysis (DCA) to assess the clinical validity of the XGBoost prediction model. Finally, an online calculator based on the XGBoost was established to measure the severity of coronary artery lesions in PMI patients. Conclusions: In summary, we established a novel and convenient prediction system for the severity of lesions in PMI patients. This system can swiftly identify PMI patients who also have severe coronary artery lesions before the coronary intervention, thus offering valuable guidance for clinical decision-making.
No takes yet. Share an insight, caveat, or question.
Wang et al. (2025) conducted an observational in Premature myocardial infarction (n=1,111). XGBoost machine learning model vs. Other machine learning models (Lasso-logistic, RF, KNN, SVM) was evaluated on Area under the curve (AUC) for predicting SYNTAX score >22. The XGBoost machine learning model effectively predicted severe coronary artery lesions in patients with premature myocardial infarction, achieving the highest area under the curve of 0.800.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: