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.
翻译:在诊断和治疗急性冠状腺综合征(ACS)时,若干研究强调了考虑性别差异的重要性。然而,在ACS亚人口群中,我们很少研究确定性别特定风险指标。本文的目的是利用机器学习方法,从一个电子健康记录公共数据库(EHR)中,为ACS亚人口群中妇女和男子确定医院内死亡率指标。方法:从MIMIC-III数据库中,我们提取了1 299名患有高血压心肌梗塞症的病人和2 820名患有非高血压心肌肌梗塞年龄的病人。我们培训和验证了死亡率预测模型,并使用了基于Sapley值的可判读性技术,为每个亚人口群确定了性别特定死亡率指标。结果:AUC=0.94(95°Ci:0.84-0.96),STEMI和AUC=0.94(95°CR:80-0.90.90)的病人。 在70岁和80-90年的NSTEMI上,女性在70岁、80岁、80岁和80岁和80岁前肾脏病前的死亡率指标指标显示高。