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 present study aims to explore machine learning (ML) models to identify in-hospital mortality markers for women and men in ACS sub-populations collected from a public database of electronic health records (EHR). Methods We extracted 1,299 patients with ST-elevation myocardial infarction (STEMI) and 2,820 patients with non-ST-elevation myocardial infarction (NSTEMI) from the Medical Information Mart for Intensive Care (MIMIC)-III database. We trained and validated mortality prediction models and used an interpretability technique to identify sex-specific markers for each sub-population. Results The models based on eXtreme Gradient Boosting (XGBoost) achieved the highest performance: area under 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 chronic kidney failure, high heart rate, and age over 70 years. For men, the top markers are acute kidney failure, high troponin T levels, and age over 75 years. 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 high heart rate, creatinine levels, and age over 70 years. Conclusions Our results show possible significant and coherent sex-specific risk markers of different ACS 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 implementing more appropriate treatment strategies and better clinical outcomes.
Vazquez, B., Fuentes-Pineda, G., Garcia, F., Borrayo, G., & Prohias, J. (2021). Risk markers by sex for in-hospital mortality in patients with acute coronary syndrome: a machine learning approach. Informatics in Medicine Unlocked, 27, 100791.