Risk Predication of Stroke-Associated Pneumonia in Acute Ischemic Stroke with Atrial Fibrillation Using Machine Learning Models

Tai Su¹Peng Zhang²Bingyin Zhang³Zihao Liu⁴Zexing Xie⁵Xiaomei Li^1*Jixiang Ma^3*Tao Xin^6*

• ¹School of Public Health and Management, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong Province, China
• ²Department of Neurology, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, Shandong Province, China
• ³Shandong Center for Disease Control and Prevention, Shandong, China
• ⁴Department of Neurosurgery, Shandong Provincial Hospital, Shandong University, Jinan, China., Jinan, Shandong Province, China
• ⁵Department of Neurosurgery, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, Shandong Province, China
• ⁶Department of Neurosurgery, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China

Stroke-associated pneumonia (SAP) is a serious complication of acute ischemic stroke (AIS), significantly affecting patient prognosis and increasing healthcare burden. AIS patients are often accompanied by basic diseases, and atrial fibrillation (AF) is one of the common basic diseases. Despite the high prevalence of AF in AIS patients, few studies have specifically addressed SAP prediction in this comorbid population. We aimed to analyze the factors influencing the occurrence of SAP in patients with AIS and AF and to assess the risk of SAP development through an optimal predictive model. We performed a case-control study. This study included 4496 hospitalized patients with AIS and AF in China between January 2020 and September 2023. The primary outcome was SAP during hospitalization. Univariate analysis and LASSO regression analysis methods were used to screen predictors. The patients with AIS and AF were randomly divided into a training set, validation set, and test set. Then, we established Logistic Regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) models. The accuracy, sensitivity, specificity, area under the curve, Youden index and F1 score were adopted to evaluate the predictive value of each model. The optimal prediction model was visualized using a nomogram. In this study, SAP was identified in 10.16% of cases. The variables screened by univariate analysis and LASSO regression, Variables such as coronary artery disease, hypertension, and dysphagia, identified by univariate and LASSO regression analyses (P<0.05), were included in the LR, RF, SVM. The LR model outperformed other models, achieving an AUC of 0.866, accuracy of 90.13%, sensitivity of 79.49%, specificity of 86.11%, F1 score of 0.80. A nomogram based on the LR model was developed to predict SAP risk, providing a practical tool for early identification of high-risk patients, and enabling targeted interventions to reduce SAP incidence and improve outcomes.