Background Rapid and accurate identification of pathogens is very important for the treatment of Severe community-acquired pneumonia (SCAP) in children. Metagenomic Next-generation sequencing (mNGS) has been applied in the detection of pathogenic bacteria in recent years, while the overall evaluation the application of SCAP in children is lacking. Methods In our study, 84 cases of SCAP were enrolled. Bronchoalveolar lavage fluid (BALF) samples were analysed using mNGS; and sputum, blood, and BALF samples were analysed using conventional technology (CT). Results Among the 84 children, 41 were boys, and 43 were girls, with an average age ranging from 2 months to 14 years. The pathogen detection rate of mNGS was higher than that of CT (83.3% [70/84] vs. 63.1% [53/84], P = 0.003). The mNGS was much greater than that of the CT in detecting Streptococcus pneumoniae (89.2% [25/29] vs. 44.8% [13/29], P = 0.001) and Haemophilus influenzae (91.7% [11/12] vs. 33.3% [4/12], P < 0.005). The mNGS also showed superior fungal detection performance compared with that of the CT (81.8% [9/11] vs. 18.2% [2/11], P = 0.004). The mNGS test can detect viruses, such as bocavirus, rhinovirus, and human metapneumovirus, which are not frequently recognised using CT. However, the mNGS detection rate was lower than that of the CT (52.4% [11/21] vs. 95.2% [20/21], P = 0.004) for Mycoplasma pneumoniae (MP). The detection rate of mNGS for mixed infection was greater than that of the CT, although statistical significance was not observed (26.3% [20/39] vs. 21.1% [16/39], P > 0.005). Treatment for 26 (31.0%) children was changed based on mNGS results, and their symptoms were reduced; nine patients had their antibiotic modified, five had antibiotics added, nine had their antifungal medication, and seven had their antiviral medication. Conclusion mNGS has unique advantages in the detection of SCAP pathogens in children, especially S. pneumoniae, H. influenzae, and fungi. However, the detection rate of MP using mNGS was lower than that of the CT. Additionally, mNGS can detect pathogens that are not generally covered by CT, which is extremely important for the modification of the treatment strategy.

Background 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. Methods 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). Results 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. Conclusion 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.