Objectives To analyze the clinical characteristics of hospitalized children with respiratory syncytial virus (RSV) infection and identify independent risk factors for intensive care unit (ICU) admission. Methods This retrospective study included hospitalized children with polymerase chain reaction (PCR)-confirmed RSV infection between July 2022 and April 2025. Epidemiological, clinical, laboratory, and imaging data were collected. Multivariable logistic regression analysis was employed to determine independent predictors of ICU admission. Results Of 95,311 children tested for acute respiratory infections, 14,583 (15.3%) were RSV-positive, of whom 5,814 (39.9%) were hospitalized. The hospitalized cohort was predominantly infants under one year of age (47.2%). The most common manifestations were cough (91.6%), sputum production (69.3%), and fever (55.8%). Laboratory findings revealed elevated median serum levels of procalcitonin, creatine kinase-MB, and lactate dehydrogenase. Chest computed tomography primarily revealed increased bronchial wall thickening (87.3%) and patchy shadows (67.2%). Complications were frequent, affecting the respiratory system in 53.4% of cases and other systems in 33.5%. A total of 710 children (12.2%) required ICU admission. To identify early warning predictors, a multivariable logistic regression model excluding acute clinical presentations (shortness of breath and dyspnea) identified respiratory complications (OR = 5.20, 95% CI: 3.82–7.07, P 0.001), bilateral consolidation on chest imaging (OR = 2.08, 95% CI: 1.52–2.85, P 0.001), and prolonged fever duration (OR = 1.19 per day, 95% CI: 1.14–1.23, P 0.001) as the strongest independent risk factors for ICU admission. Conclusions Hospitalized children with RSV infection are predominantly young infants and are prone to complex manifestations and multi-system complications. Respiratory complications, bilateral consolidation, and prolonged fever duration are key predictors of ICU admission. This study provides important evidence for the early identification of high-risk children and the optimization of critical care resource allocation.
Fu et al. (Thu,) studied this question.