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.

Evaluation of disease severity and prediction of severe cases in children hospitalized with influenza A (H1N1) infection during the post-COVID-19 era: a multicenter retrospective study.

BACKGROUND: The rebound of influenza A (H1N1) infection in post-COVID-19 era recently attracted enormous attention due the rapidly increased number of pediatric hospitalizations and the changed characteristics compared to classical H1N1 infection in pre-COVID-19 era. This study aimed to evaluate the clinical characteristics and severity of children hospitalized with H1N1 infection during post-COVID-19 period, and to construct a novel prediction model for severe H1N1 infection. METHODS: A total of 757 pediatric H1N1 inpatients from nine tertiary public hospitals in Yunnan and Shanghai, China, were retrospectively included, of which 431 patients diagnosed between February 2023 and July 2023 were divided into post-COVID-19 group, while the remaining 326 patients diagnosed between November 2018 and April 2019 were divided into pre-COVID-19 group. A 1:1 propensity-score matching (PSM) was adopted to balance demographic differences between pre- and post-COVID-19 groups, and then compared the severity across these two groups based on clinical and laboratory indicators. Additionally, a subgroup analysis in the original post-COVID-19 group (without PSM) was performed to investigate the independent risk factors for severe H1N1 infection in post-COIVD-19 era. Specifically, Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to select candidate predictors, and logistic regression was used to further identify independent risk factors, thus establishing a prediction model. Receiver operating characteristic (ROC) curve and calibration curve were utilized to assess discriminative capability and accuracy of the model, while decision curve analysis (DCA) was used to determine the clinical usefulness of the model. RESULTS: After PSM, the post-COVID-19 group showed longer fever duration, higher fever peak, more frequent cough and seizures, as well as higher levels of C-reactive protein (CRP), interleukin 6 (IL-6), IL-10, creatine kinase-MB (CK-MB) and fibrinogen, higher mechanical ventilation rate, longer length of hospital stay (LOS), as well as higher proportion of severe H1N1 infection (all P < 0.05), compared to the pre-COVID-19 group. Moreover, age, BMI, fever duration, leucocyte count, lymphocyte proportion, proportion of CD3+ T cells, tumor necrosis factor α (TNF-α), and IL-10 were confirmed to be independently associated with severe H1N1 infection in post-COVID-19 era. A prediction model integrating these above eight variables was established, and this model had good discrimination, accuracy, and clinical practicability. CONCLUSIONS: Pediatric H1N1 infection during post-COVID-19 era showed a higher overall disease severity than the classical H1N1 infection in pre-COVID-19 period. Meanwhile, cough and seizures were more prominent in children with H1N1 infection during post-COVID-19 era. Clinicians should be aware of these changes in such patients in clinical work. Furthermore, a simple and practical prediction model was constructed and internally validated here, which showed a good performance for predicting severe H1N1 infection in post-COVID-19 era.

Residue behavior and risk assessment of mixed formulation of imidacloprid and chlorfenapyr in chieh-qua under field conditions.

A simple and rapid method based on high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) was developed for the simultaneous determination of imidacloprid and chlorfenapyr residues in chieh-qua. Field trials were designed to investigate the dissipation and terminal residue behavior of the mixed formulation of imidacloprid and chlorfenapyr in chieh-qua in Guangzhou and Nanning areas. Risk assessment was performed by calculating the risk quotient (RQ) values. The developed analytical method exhibited recoveries of 89.9-110.3% with relative standard deviations (RSDs) of 2.8-12.5% at the spiked levels of 0.01, 0.10, and 1.00 mg/kg. The limit of detection (LOD) was 0.003 mg/kg, and the limit of quantification (LOQ) was 0.01 mg/kg for both imidacloprid and chlorfenapyr. It was found that the half-lives of imidacloprid in chieh-qua under field conditions were 3.3 and 3.5 days in Guangzhou and Nanning at a dose of 180 g ai/ha, while the half-lives of chlorfenapyr were 3.3 and 2.6 days, respectively. The terminal residues of imidacloprid and chlorfenapyr were from 0.01 to 0.21 mg/kg and from 0.01 to 0.46 mg/kg, respectively. Results of dietary exposure assessment showed that the RQ values were much lower than 1, indicating that the risk of imidacloprid and chlorfenapyr applied in chieh-qua was negligible to human health under recommended dosage and good agricultural practices. The proposed study would provide guidance for safe and reasonable use of imidacloprid and chlorfenapyr in chieh-qua cultivation in China.

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.

Development and validation of a nomogram for predicting occurrence of severe case in children hospitalized with influenza A (H1N1) infection during the post-COVID-19 era.

Background: The significant rebound of influenza A (H1N1) virus activity, particularly among children, with rapidly growing number of hospitalized cases is of major concern in the post-COVID-19 era. The present study was performed to establish a prediction model of severe case in pediatric patients hospitalized with H1N1 infection during the post-COVID-19 era. Methods: This is a multicenter retrospective study across nine public tertiary hospitals in Yunnan, China, recruiting pediatric H1N1 inpatients hospitalized at five of these centers between February 1 and July 1, 2023, into the development dataset. Screening of 40 variables including demographic information, clinical features, and laboratory parameters were performed utilizing Least Absolute Shrinkage and Selection Operator (LASSO) regression and logistic regression to determine independent risk factors of severe H1N1 infection, thus constructing a prediction nomogram. Receiver operating characteristic (ROC) curve, calibration curve, as well as decision curve analysis (DCA) were employed to evaluate the model's performance. Data from four independent cohorts comprised of pediatric H1N1 inpatients from another four hospitals between July 25 and October 31, 2023, were utilized to externally validate this nomogram. Results: The development dataset included 527 subjects, 122 (23.1 %) of whom developed severe H1N1 infection. The external validation dataset included 352 subjects, 72 (20.5 %) of whom were eventually confirmed as severe H1N1 infection. The LASSO regression identified 19 candidate predictors, with logistic regression further narrowing down to 11 independent risk factors, including underlying conditions, prematurity, fever duration, wheezing, poor appetite, leukocyte count, neutrophil-lymphocyte ratio (NLR), erythrocyte sedimentation rate (ESR), lactate dehydrogenase (LDH), interleukin-10 (IL-10), and tumor necrosis factor-α (TNF-α). By integrating these 11 factors, a predictive nomogram was established. In terms of prediction of severe H1N1 infection, excellent discriminative capacity, favorable accuracy, and satisfactory clinical usefulness of this model were internally and externally validated via ROC curve, calibration curve, and DCA, respectively. Conclusion: Our study successfully established and validated a novel nomogram model integrating underlying conditions, prematurity, fever duration, wheezing, poor appetite, leukocyte count, NLR, ESR, LDH, IL-10, and TNF-α. This nomogram can effectively predict the occurrence of serious case in pediatric H1N1 inpatients during the post-COVID-19 era, facilitating the early recognition and more efficient clinical management of such patients.