Abstract
OBJECTIVE: To investigate the risk factors for hospital-acquired infection in patients with stroke and to analyze the distribution of pathogens and characteristics of infection sites. METHODS: A retrospective cohort study was conducted, enrolling 393 patients with stroke hospitalized at Beijing Tiantan Hospital, Capital Medical University, from January 2024 to July 2025. Among them, 184 patients were assigned to the infection group and 209 to the non-infection group. Multivariable logistic regression analysis was used to identify risk factors for hospital-acquired infection, and a nomogram prediction model was constructed. RESULTS: Multivariable logistic regression analysis identified cerebral infarction (odds ratio = 25.09, 95% confidence interval: 5.38-117.10), no history of alcohol consumption (odds ratio = 4.47, 95% confidence interval: 1.51-13.18), no history of cerebrovascular disease (odds ratio = 5.04, 95% confidence interval: 1.35-18.76), elevated neutrophil count (odds ratio = 1.29, 95% confidence interval: 1.08-1.55), and elevated C-reactive protein (odds ratio = 1.03, 95% confidence interval: 1.01-1.06) as independent risk factors for hospital-acquired infection in patients with stroke. Absence of nasogastric tube feeding (odds ratio = 0.09, 95% confidence interval: 0.03-0.31), no indwelling urinary catheterization (odds ratio = 0.21, 95% confidence interval: 0.06-0.77), and absence of postoperative coma (odds ratio = 0.07, 95% confidence interval: 0.01-0.36) were identified as protective factors. The nomogram model achieved an area under the curve of 0.96 (95% confidence interval: 0.94-0.98) in the training set and 0.94 (95% confidence interval: 0.90-0.99) in the validation set, with good calibration. Gram-negative bacteria were the predominant pathogens (70.2%), and the respiratory tract was the most common infection site (92.9%). Endotracheal intubation and impaired consciousness were identified as common risk factors for both pulmonary and Gram-negative bacterial infections. CONCLUSION: This study identified independent risk factors for hospital-acquired infection in patients with stroke, developed a nomogram model with good predictive performance, and preliminarily characterized the risk profiles associated with different infection types and sites. These findings provide a reference for early identification of high-risk patients and the formulation of targeted prevention and control strategies.