A comparative study on the influencing factors and risk prediction models for stroke- associated pneumonia in patients with acute ischemic stroke and atrial fibrillation

Background To analyze the factors influencing stroke-associated pneumonia (SAP) in patients with acute ischemic stroke (AIS) and atrial fibrillation (AF), and to explore an optimal model for risk prediction. Methods Data were sourced from the Shandong Provincial Center for Disease Control and Prevention, encompassing all patients diagnosed with AIS and AF from 2020 to 2023. First, univariate analysis and LASSO (Least absolute shrinkage and selection operator) regression analysis methods were used to screen predictors. Secondly, the patients with AIS and AF were randomly divided into a training set, validation set, and test set in a ratio of 7:2:1, which were utilized for model training, model parameter adjustment, and model performance evaluation, respectively. The training set was balanced by synthetic minority oversampling technique (SMOTE), logistic regression, random forest (RF), and support vector machine (SVM),extreme gradient boosting (XGboost) models were constructed. Finally, we compared the models based on accuracy, sensitivity, specificity, AUC (area under the curve), and Youden index. We clarified the optimal prediction model and influencing factors ,the nomogram for risk prediction was constructed for SAP in patients with AIS and AF. Results Among the 4496 patients with AIS and AF, SAP was identified in 10.16% of cases. In the test set, the AUC for logistic regression, RF, SVM, and XGboost models were 0.866, 0.817, 0.816, and 0.838, respectively. The most predictive factors included coronary heart disease [OR = 1.05 (1.03, 1.07), p < 0.001], hypertension [OR = 1.05 (1.04, 1.07), p < 0.001], consciousness disorder [OR = 1.19 (1.16–1.23), p < 0.001], cognitive impairment [OR = 1.10 (1.08–1.13), p < 0.001], limb movement disorder [OR = 1.07 (1.04–1.09), p < 0.001], dysphagia [OR = 1.13 (1.08–1.19), p < 0.001], nasal feeding [OR = 0.95 (0.92–0.98), p = 0.003], and oxygen intake [OR = 0.65 (0.62–0.67), p < 0.001]. the nomogram average absolute error of calibration curve was 0.014. Conclusions Coronary artery disease, hypertension, consciousness disorder, cognitive impairment, limb movement disorder, and dysphagia were identified as independent risk factors for SAP in patients with AIS and AF. In contrast, nasal feeding and oxygen intake served as independent protective factors. The logistic regression model demonstrated the best predictive performance for SAP in patients with AIS and AF compared to RF, SVM, and XGboost models. The risk prediction model established by nomogram can better predict the risk of SAP.