Machine learning-based prediction of infarct size in patients with ST-segment elevation myocardial infarction: A multi-center study

Background: Cardiac magnetic resonance imaging (CMR) is the gold standard for measuring infarct size (IS). However, this method is expensive and requires a specially trained technologist to administer. We therefore sought to quantify the IS using machine learning (ML) based analysis on clinical features, which is a convenient and cost-effective alternative to CMR.Methods and results: We included 315 STEMI patients with CMR examined one week after morbidity in final analysis. After feature selection by XGBoost on fifty-six clinical features, we used five ML algorithms (random forest (RF), light gradient boosting decision machine, deep forest, deep neural network, and stacking) to predict IS with 26 (selected by XGBoost with information gain greater than average level of 56 features) and the top 10 features, during which 5-fold cross-validation were used to train and optimize models. We then evaluated the value of actual and ML-IS for the prediction of adverse remodeling. Our finding indicates that MLs outperform the linear regression in predicting IS. Specifically, the RF with five predictors identified by the exhaustive method performed better than linear regression (LR) with 10 indicators (R2 of RF: 0.8; LR: 0). The finding also shows that both actual and ML-IS were independently associated with adverse remodeling. ML-IS >= 21% was associated with a twofold increase in the risk of LV remodeling (P < 0.01) compared with patients with reference IS (1st tertile). Conclusion: ML-based methods can predict IS with widely available clinical features, which provide a proof-ofconcept tool to quantitatively assess acute phase IS.

Automated summary

The study aims to predict infarct size of STEMI patients using machine learning methods. After feature selection, five ML algorithms were used to predict infarct size and evaluate the risk of adverse remodeling based on actual and ML-predicted infarct size. The results showed that ML methods outperformed linear regression in predicting infarct size, and both actual and ML-predicted infarct size were associated with adverse remodeling.