Comparison of characteristics of children hospitalized for respiratory syncytial virus infection during the pre- and post-COVID-19 eras: a multicenter retrospective study.

BACKGROUND: Respiratory syncytial virus (RSV), a leading cause of lower respiratory tract infection (LRTI) among children, has resurged in the form of endemic or even pandemic in many countries and areas after the easing of COVID-19 containment measures. This study aimed to investigate the differences in epidemiological and clinical characteristics of children hospitalized for RSV infection during pre- and post-COVID-19 eras in Yunnan, China. METHODS: A total of 2553 pediatric RSV inpatients from eight hospitals in Yunnan were retrospectively enrolled in this study, including 1451 patients admitted in 2018-2019 (pre-COVID-19 group) and 1102 patients admitted in 2023 (post-COVID-19 group). According to the presence or absence of severe LRTI (SLRTI), patients in the pre- and post-COVID-19 groups were further divided into the respective severe or non-severe subgroups, thus analyzing the risk factors for RSV-associated SLRTI in the two eras. Demographic, epidemiological, clinical, and laboratory data of the patients were collected for the final analysis. RESULTS: A shift in the seasonal pattern of RSV activity was observed between the pre-and post-COVID-19 groups. The peak period of RSV hospitalizations in the pre-COVID-19 group was during January-April and October-December in both 2018 and 2019, whereas that in the post-COVID-19 group was from April to September in 2023. Older age, more frequent clinical manifestations (fever, acute otitis media, seizures), and elevated laboratory indicators [neutrophil-to-lymphocyte ratio (NLR), c-reactive protein (CRP), interleukin 6 (IL-6), co-infection rate] were identified in the post-COVID-19 group than those in the pre-COVID-19 group (all P < 0.05). Furthermore, compared to the pre-COVID-19 group, the post-COVID-19 group displayed higher rates of SLRTI and mechanical ventilation, with a longer length of hospital stay (all P < 0.05). Age, low birthweight, preterm birth, personal history of atopy, underlying condition, NLR, IL-6 were the shared independent risk factors for RSV-related SLRTI in both pre- and post-COVID-19 groups, whereas seizures and co-infection were independently associated with SLRTI only in the post-COVID-19 group. CONCLUSIONS: An off-season RSV endemic was observed in Yunnan during the post-COVID-19 era, with changed clinical features and increased severity. Age, low birthweight, preterm birth, personal history of atopy, underlying condition, NLR, IL-6, seizures, and co-infection were the risk factors for RSV-related SLRTI in the post-COVID-19 era.

A novel combined nomogram for predicting severe acute lower respiratory tract infection in children hospitalized for RSV infection during the post-COVID-19 period.

Introduction: Off-season upsurge of respiratory syncytial virus (RSV) infection with changed characteristics and heightened clinical severity during the post-COVID-19 era are raising serious concerns. This study aimed to develop and validate a nomogram for predicting the risk of severe acute lower respiratory tract infection (SALRTI) in children hospitalized for RSV infection during the post-COVID-19 era using machine learning techniques. Methods: A multicenter retrospective study was performed in nine tertiary hospitals in Yunnan, China, enrolling children hospitalized for RSV infection at seven of the nine participating hospitals during January-December 2023 into the development dataset. Thirty-nine variables covering demographic, clinical, and laboratory characteristics were collected. Primary screening and dimension reduction of data were performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, followed by identification of independent risk factors for RSV-associated SALRTI using Logistic regression, thus finally establishing a predictive nomogram model. Performance of the nomogram was internally evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) based on the development dataset. External validation of our model was conducted using same methods based on two independent RSV cohorts comprising pediatric RSV inpatients from another two participating hospitals between January-March 2024. Results: The development dataset included 1102 patients, 239 (21.7%) of whom developed SALRTI; while the external validation dataset included 249 patients (142 in Lincang subset and 107 in Dali subset), 58 (23.3%) of whom were diagnosed as SALRTI. Nine variables, including age, preterm birth, underlying condition, seizures, neutrophil-lymphocyte ratio (NLR), interleukin-6 (IL-6), lactate dehydrogenase (LDH), D-dimer, and co-infection, were eventually confirmed as the independent risk factors of RSV-associated SALRTI. A predictive nomogram was established via integrating these nine predictors. In both internal and external validations, ROC curves indicated that the nomogram had satisfactory discrimination ability, calibration curves demonstrated good agreement between the nomogram-predicted and observed probabilities of outcome, and DCA showed that the nomogram possessed favorable clinical application potential. Conclusion: A novel nomogram combining several common clinical and inflammatory indicators was successfully developed to predict RSV-associated SALRTI. Good performance and clinical effectiveness of this model were confirmed by internal and external validations.