Risk markers by sex for in-hospital mortality in patients with acute coronary syndrome based on machine learning

Background: Several studies have highlighted the importance of considering sex differences in the diagnosis and treatment of Acute Coronary Syndrome (ACS). However, the identification of sex-specific risk markers in ACS sub-populations has been scarcely studied. The goal of this paper is to identify in-hospital mortality markers for women and men in ACS sub-populations from a public database of electronic health records (EHR) using machine learning methods. Methods: From the MIMIC-III database, we extracted 1,299 patients with ST-elevation myocardial infarction and 2,820 patients with Non-ST-elevation myocardial infarction. We trained and validated mortality prediction models and used an interpretability technique based on Shapley values to identify sex-specific markers for each sub-population. Results: The models based on eXtreme Gradient Boosting achieved the highest performance: AUC=0.94 (95\% CI:0.84-0.96) for STEMI and AUC=0.94 (95\% CI:0.80-0.90) for NSTEMI. For STEMI, the top markers in women are chronic kidney failure, high heart rate, and age over 70 years, while for men are acute kidney failure, high troponin T levels, and age over 75 years. In contrast, for NSTEMI, the top markers in women are low troponin levels, high urea level, and age over 80 years, and for men are high heart rate and creatinine levels, and age over 70 years. Conclusions: Our results show that it is possible to find significant and coherent sex-specific risk markers of different ACS sub-populations by interpreting machine learning mortality models trained on EHRs. Differences are observed in the identified risk markers between women and men, which highlight the importance of considering sex-specific markers to have more appropriate treatment strategies and better clinical outcomes.