Compassion fatigue (CF) is a common phenomenon among nurses working in intensive care units (ICU). Their prolonged exposure to the suffering and trauma of critical patients can lead to emotional exhaustion and psychological stress. To develop a classification model for compassion fatigue among intensive care unit nurses using multiple machine learning algorithms and identify the key factors. A multicenter cross-sectional study was utilized and collected valid questionnaire data from 1110 intensive care unit nurses from 25 tertiary hospitals. The dataset was randomly split into a training set (70%) and a test set (30%). The variables were evaluated using the Boruta algorithm and the LASSO regression. Four classification models—Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Light Gradient Boosting Machine (LGBM)—were developed. The optimal model was selected using the area under the curve (AUC) of the receiver operating characteristics, accuracy, precision, recall, F1 score, and Brier score. Shapley additive explanations (SHAP) were employed to visualize the significance of each variable of the optimal model. The prevalence of compassion fatigue among intensive care unit nurses was 78.2%. XGBoost exhibited the best performance over the other models (AUC = 0.832, 95% CI: 0.779–0.885, accuracy = 0.733, precision = 0.932, recall = 0.718, F1 = 0.811, Brier score = 0.116). According to SHAP values in the XGBoost model, the most important variables were psychological resilience, perceived social support, practice environment, income satisfaction, educational level, physical condition, and nursing career re-choice intention. The classification model for compassion fatigue of intensive care unit nurses constructed by XGBoost exhibited the best performance. SHAP explained the key variables. These findings provide novel insights for optimizing intervention strategies for nursing managers. Not applicable.
Bian et al. (Sat,) studied this question.