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Background: Several studies have highlighted the importance of consideringsex differences in the diagnosis and treatment of Acute Coronary Syndrome(ACS). However, the identification of sex-specific risk markers in ACSsub-populations has been scarcely studied. The present study aims to exploremachine learning (ML) models to identify in-hospital mortality markers forwomen and men in ACS sub-populations collected from a public database ofelectronic health records (EHR). Methods: We extracted 1,299 patients withST-elevation myocardial infarction (STEMI) and 2,820 patients withnon-ST-elevation myocardial infarction (NSTEMI) from the Medical InformationMart for Intensive Care (MIMIC)-III database. We trained and validatedmortality prediction models and used an interpretability technique to identifysex-specific markers for each sub-population. Results: The models based oneXtreme Gradient Boosting (XGBoost) achieved the highest performance: areaunder the curve (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 chronickidney failure, high heart rate, and age over 70 years. For men, the topmarkers are acute kidney failure, high troponin T levels, and age over 75years. However, for NSTEMI, the top markers in women are low troponin levels,high urea levels, and age over 80 years. For men, the top markers are highheart rate, creatinine levels, and age over 70 years. Conclusions: Our resultsshow possible significant and coherent sex-specific risk markers of differentACS sub-populations by interpreting ML mortality models trained on EHRs.Differences are observed in the identified risk markers between women and men,highlighting the importance of considering sex-specific markers in implementingmore appropriate treatment strategies and better clinical outcomes.

Risk markers by sex for in-hospital mortality in patients with acute coronary syndrome: a machine learning approach