利用机器学习预测ST段抬高型心肌梗死女性患者的院内死亡率。
Using Machine Learning to Predict the In-Hospital Mortality in Women with ST-Segment Elevation Myocardial Infarction.
作者信息
Zhao Pengyu, Liu Chang, Zhang Chao, Hou Yonghong, Zhang Xiaomeng, Zhao Jia, Sun Guolei, Zhou Jia
机构信息
Department of Communication Engineering, School of Electrical and Information Engineering, Tianjin University, 300072 Tianjin, China.
Department of Emergency, Thoracic Clinical College, Tianjin Medical University, 300070 Tianjin, China.
出版信息
Rev Cardiovasc Med. 2023 Apr 24;24(5):126. doi: 10.31083/j.rcm2405126. eCollection 2023 May.
BACKGROUND
Several studies have shown that women have a higher mortality rate than do men from ST-segment elevation myocardial infarction (STEMI). The present study was aimed at developing a new risk-prediction model for all-cause in-hospital mortality in women with STEMI, using predictors that can be obtained at the time of initial evaluation.
METHODS
We enrolled 8158 patients who were admitted with STEMI to the Tianjin Chest Hospital and divided them into two groups according to hospital outcomes. The patient data were randomly split into a training set (75%) and a testing set (25%), and the training set was preprocessed by adaptive synthetic (ADASYN) sampling. Four commonly used machine-learning (ML) algorithms were selected for the development of models; the models were optimized by 10-fold cross-validation and grid search. The performance of all-population-derived models and female-specific models in predicting in-hospital mortality in women with STEMI was compared by several metrics, including accuracy, specificity, sensitivity, G-mean, and area under the curve (AUC). Finally, the SHapley Additive exPlanations (SHAP) value was applied to explain the models.
RESULTS
The performance of models was significantly improved by ADASYN. In the overall population, the support vector machine (SVM) combined with ADASYN achieved the best performance. However, it performed poorly in women with STEMI. Conversely, the proposed female-specific models performed well in women with STEMI, and the best performing model achieved 72.25% accuracy, 82.14% sensitivity, 71.69% specificity, 76.74% G-mean and 79.26% AUC. The accuracy and G-mean of the female-specific model were greater than the all-population-derived model by 34.64% and 9.07%, respectively.
CONCLUSIONS
A machine-learning-based female-specific model can conveniently and effectively identify high-risk female STEMI patients who often suffer from an incorrect or delayed management.
背景
多项研究表明，ST段抬高型心肌梗死（STEMI）女性患者的死亡率高于男性。本研究旨在利用初始评估时可获取的预测指标，开发一种针对STEMI女性患者全因院内死亡率的新型风险预测模型。
方法
我们纳入了8158例因STEMI入住天津市胸科医院的患者，并根据医院结局将其分为两组。患者数据被随机分为训练集（75%）和测试集（25%），训练集通过自适应合成（ADASYN）采样进行预处理。选择四种常用的机器学习（ML）算法来开发模型；通过10折交叉验证和网格搜索对模型进行优化。通过包括准确率、特异性、敏感性、G均值和曲线下面积（AUC）在内的多个指标，比较全人群模型和女性特异性模型在预测STEMI女性患者院内死亡率方面的性能。最后，应用SHapley加性解释（SHAP）值来解释模型。
结果
ADASYN显著提高了模型的性能。在总体人群中，支持向量机（SVM）结合ADASYN表现最佳。然而，它在STEMI女性患者中表现不佳。相反，所提出的女性特异性模型在STEMI女性患者中表现良好，表现最佳的模型准确率达到72.25%，敏感性达到82.14%，特异性达到71.69%，G均值达到76.74%，AUC达到79.26%。女性特异性模型的准确率和G均值分别比全人群模型高34.64%和9.07%。
结论
基于机器学习的女性特异性模型能够方便有效地识别经常遭受管理不当或延误的高危女性STEMI患者。
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