An Auxiliary Scoring Model for Patients with Acute Pulmonary Embolism Complicated with Atrial Fibrillation Was Established Based on Random Forests.

The purpose of this study was to explore the establishment of an auxiliary scoring model for patients with acute pulmonary embolism (APE) complicated with atrial fibrillation (AF) based on random forest (RF) and its application effect. A retrospective analysis was performed on the general data, underlying diseases, laboratory indicators, and cardiac indicators of 100 patients with APE admitted to our hospital from 2018 to 2021. The occurrence of atrial fibrillation in patients with pulmonary embolism was taken as a categorical variable, and the general data, underlying diseases, laboratory indicators, and cardiac indicators were taken as input variables. Then, the risk auxiliary scoring model for patients with APE complicated with AF was established based on RF and logistic regression. Finally, the accuracy, sensitivity, specificity, recall rate, accuracy, F1 value, and the receiver operator characteristic (ROC) curve were used to evaluate the predictive value of the models. After statistical analysis, the optimal node value was 3 and the optimal number of decision trees was 500 in the RF model. The importance of predictors in descending order were Hcy, diabetes mellitus, FT3 level, UA level, left atrial diameter, hypertension, and smoking history. The prediction accuracy of the RF model was 0.934, sensitivity 0.966, specificity 0.876, recall rate 0.9660, accuracy 0.934, and F1 value 0.950. The logistic regression model prediction accuracy was 0.816, sensitivity 0.915, specificity 0.125, recall rate 0.902, accuracy 0.811, and F1 value 0.896. The RF model and logistic regression prediction model AUC values were 0.984 and 0.883, respectively. From this, we conclude that the RF model was better than the logistic regression model in predicting AF in APE patients. So, the RF model had the clinical application value.