Background Influenza A virus (IAV) pneumonia poses a significant threat to the elderly, often leading to severe outcomes due to age-related immunological decline. Early and accurate assessments of disease severity is critical for improving clinical management and prognosis. The Systemic Inflammatory Response Index (SIRI), which integrates neutrophil, monocyte, and lymphocyte counts, may serve as a novel biomarker to reflect the systemic inflammatory state and predict disease progression in older adults. Methods A retrospective analysis was conducted involving 160 patients aged 65 years or older who were diagnosed with IAV pneumonia at China Resources Wuhan Iron and Steel General Hospital between December 2024 and March 2025. Patients were categorized into favorable (CURB-65 confusion, urea, respiratory rate, blood pressure, age ≥ 65 years score 3, n = 71) and unfavorable (CURB-65 score ≥3, n = 89) prognosis groups. Demographic, clinical, and laboratory data were collected. The SIRI was calculated as (neutrophils × monocytes)/lymphocytes. Statistical analyses were performed using t -tests, chi-squared tests, logistic regression, and receiver operating characteristic (ROC) curve analysis. Results SIRI levels were significantly higher in the unfavorable prognosis group than in the favorable prognosis group ( p 0.05). Additionally, a positive correlation was found between SIRI levels and CURB-65 scores (r = 0.433, p 0.001). A multivariate logistic regression analysis confirmed that the SIRI is an independent predictor of disease severity after adjusting for gender, age, and smoking history ( p 0.05). The ROC analysis demonstrated that the SIRI had an area under the curve of 0.806 for predicting severe disease, outperforming traditional inflammatory markers such as C-reactive protein, interleukin-6, and erythrocyte sedimentation rate. Conclusion The SIRI is a reliable and objective biomarker for assessing disease severity and predicting prognosis in elderly patients with IAV pneumonia. Its use may facilitate early identification of high-risk individuals, guide timely clinical intervention, and ultimately improve patient outcomes.
Wu et al. (Wed,) studied this question.