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.