The XGB model effectively predicts 30-day all-cause mortality post-PCI, highlighting key predictors like ejection fraction and age 80 or older.
Can an XGB machine learning model accurately predict 30-day all-cause mortality following percutaneous coronary intervention?
An XGB machine learning model can effectively identify key predictors of 30-day mortality post-PCI, potentially supporting individualized risk assessment and clinical decision-making.
Absolute Event Rate: 0% vs 0%
The XGB model demonstrated the best performance in predicting 30-day all-cause mortality post-PCI, identified most influential predictors such as severely reduced ejection fraction, ST-elevation myocardial infarction presentation, severe renal impairment, age 80 years and older and complex lesion. These factors from the XGB model could support individualised risk assessment, informed clinical decision-making, improved patient care or efficient resource utilisation for an Australian population. Further external validation is essential to confirm the model's generalisability across different populations.
Chowdhury et al. (Thu,) reported a other. The XGB model effectively predicts 30-day all-cause mortality post-PCI, highlighting key predictors like ejection fraction and age 80 or older.