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

Liu HF ，Zhang XZ ，Liu CY ，Li W ，Li WH ，Wang YY ，Li HY ，Xiang M ，Lu R ，Yuan TY ，Fu HM ... -  《Frontiers in Immunology》

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.

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. 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. 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. 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.

Liu HF ，Hu XZ ，Liu CY ，Guo ZH ，Lu R ，Xiang M ，Wang YY ，Yin ZQ ，Wang M ，Sui MZ ，Yang JW ，Fu HM ... -  《Heliyon》

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

Liu HF ，Wang YY ，Zhang XZ ，Li HY ，Xiang M ，Lu R ，Liu CY ，Li W ，Feng QL ，Guo YJ ，Huang RW ，Fu HM ... -  《BMC INFECTIOUS DISEASES》

[Clinical characteristics of hospitalized children with respiratory syncytial virus infection and risk prediction of severe illness during the post-COVID-19 era in Kunming].

Liu HF ，Feng QL ，Huang RW ，Yuan TY ，Sui MZ ，Li PL ，Liu K ，Li F ，Li Y ，Jiang L ，Fu HM ... -  《-》

Development and validation of a nomogram for predicting severe respiratory syncytial virus-associated bronchiolitis.

Respiratory syncytial virus (RSV) is the most common cause of bronchiolitis and is related to the severity of the disease. This study aimed to develop and validate a nomogram for predicting severe bronchiolitis in infants and young children with RSV infection. A total of 325 children with RSV-associated bronchiolitis were enrolled, including 125 severe cases and 200 mild cases. A prediction model was built on 227 cases and validated on 98 cases, which were divided by random sampling in R software. Relevant clinical, laboratory and imaging data were collected. Multivariate logistic regression models were used to determine optimal predictors and to construct nomograms. The performance of the nomogram was evaluated by the area under the characteristic curve (AUC), calibration ability and decision curve analysis (DCA). There were 137 (60.4%) mild and 90 (39.6%) severe RSV-associated bronchiolitis cases in the training group (n = 227) and 63 (64.3%) mild and 35 (35.7%) severe cases in the validation group (n = 98). Multivariate logistic regression analysis identified 5 variables as significant predictive factors to construct the nomogram for predicting severe RSV-associated bronchiolitis, including preterm birth (OR = 3.80; 95% CI, 1.39-10.39; P = 0.009), weight at admission (OR = 0.76; 95% CI, 0.63-0.91; P = 0.003), breathing rate (OR = 1.11; 95% CI, 1.05-1.18; P = 0.001), lymphocyte percentage (OR = 0.97; 95% CI, 0.95-0.99; P = 0.001) and outpatient use of glucocorticoids (OR = 2.27; 95% CI, 1.05-4.9; P = 0.038). The AUC value of the nomogram was 0.784 (95% CI, 0.722-0.846) in the training set and 0.832 (95% CI, 0.741-0.923) in the validation set, which showed a good fit. The calibration plot and Hosmer‒Lemeshow test indicated that the predicted probability had good consistency with the actual probability both in the training group (P = 0.817) and validation group (P = 0.290). The DCA curve shows that the nomogram has good clinical value. A nomogram for predicting severe RSV-associated bronchiolitis in the early clinical stage was established and validated, which can help physicians identify severe RSV-associated bronchiolitis and then choose reasonable treatment.

Yan J ，Zhao L ，Zhang T ，Wei Y ，Guo D ，Guo W ，Zheng J ，Xu Y ... -  《BMC INFECTIOUS DISEASES》