Construction and evaluation of a nomogram model for predicting the risk of hospital-acquired pneumonia in elderly patients with acute ischemic stroke

OBJECTIVE: In this study, we aimed to develop and validate an easy-to-use model to predict the risk of hospital-acquired pneumonia (HAP) in elderly patients with acute ischemic stroke (AIS). METHODS: A total of 2861 elderly AIS patients who were admitted to Jiading District Central Hospital Affiliated with Shanghai University of Medicine & Health Science from January 2016 to December 2023 were selected. Among these patients, 699 were diagnosed with HAP (HAP group), and 2162 patients were included in the control group (non-HAP group). Univariate and multivariate logistic regression analyses were performed to determine the risk factors for HAP after AIS. These factors were then used to establish a scoring system, from which a nomogram model was developed with R software. RESULTS: Univariate analysis revealed 17 factors that were significantly associated with the development of HAP after AIS in elderly patients (P < 0.05). Multivariate logistic regression analysis including these factors revealed that age, the national institute of health stroke scale (NIHSS) score within 24 h of admission (Kwah LK. J Physiother 60:61, 2014), the stress hyperglycemia ratio (SHR), smoking status, and dysphagia status were independent risk factors for HAP after AIS. According to the oxfordshire community stroke project (OCSP) classification, patients classified as having the total anterior circulation infarct (TACI), partial anterior circulation infarct (PACI), and posterior circulation infarct (POCI) sub-types had a significantly increased risk of HAP compared with those classified as having the lacunar infarct (LACI) sub-type. A nomogram model constructed from these six risk factors yielded a C-index of 0.834 (95% confidence interval (CI): 0.811-0.857), indicating high accuracy. Calibration and clinical decision curve analyses revealed the reliability and clinical value of the proposed model. CONCLUSION: Our proposed nomogram provides clinicians with a simple and reliable tool for predicting HAP from conventional data. The model can also help clinicians make personalized treatment decisions for patients at different risk levels. CLINICAL TRIAL NUMBER: Not applicable.