Construction and evaluation of a nomogram prediction model for aspiration pneumonia in patients with acute ischemic stroke.

Background: Aspiration Pneumonia (AP) is a leading cause of death in patients with Acute Ischemic Stroke (AIS). Early detection, diagnosis and effective prevention measures are crucial for improving patient prognosis. However, there is a lack of research predicting AP occurrence after AIS. This study aimed to identify risk factors and develop a nomogram model to determine the probability of developing AP after AIS. Method: A total of 3258 AIS patients admitted to Jinshan Hospital of Fudan University between January 1, 2016, and August 20, 2022, were included. Among them, 307 patients were diagnosed with AP (AP group), while 2951 patients formed the control group (NAP group). Univariate and multivariate logistic regression analyses were conducted to identify relevant risk factors for AP after AIS. These factors were used to establish a scoring system and develop a nomogram model using R software. Results: Univariate analysis revealed 20 factors significantly associated (P < 0.05) with the development of AP after AIS. These factors underwent multivariate logistic regression analysis, which identified age (elderly), National Institute of Health Stroke Scale (NIHSS) score, dysphagia, atrial fibrillation, cardiac insufficiency, renal insufficiency, hepatic insufficiency, elevated Fasting Blood Glucose (FBG), elevated C-Reactive Protein (CRP), elevated Neutrophil percentage (NEUT%), and decreased prealbumin as independent risk factors. A nomogram model incorporating these 11 risk factors was constructed, with a C-index of 0.872 (95 % CI: 0.845-0.899), indicating high accuracy. Calibration and clinical decision analyses demonstrated the model's reliability and clinical value. Conclusion: A nomogram model incorporating age, NIHSS score, dysphagia, atrial fibrillation, cardiac insufficiency, renal insufficiency, hepatic insufficiency, FBG, CRP, NEUT%, and prealbumin effectively predicts AP risk in AIS patients. This model provides guidance for early intervention strategies, enabling the identification of high-risk individuals for timely preventive measures.