Abstract
OBJECTIVES: This study aimed to identify the risk factors and construct the diagnostic model associated with pneumocystis pneumonia (PCP) in pediatric patients. METHODS: This retrospective observational study analyzed 34 cases of PCP and 51 cases of other types of pneumonia treated at Children's Hospital Affiliated to Shandong University between January 2021 and August 2023. Multivariate binary logistic regression was used to identify the risk factors associated with PCP. Receiver operating characteristic curves and calibration plots were constructed to evaluate the diagnostic model. RESULTS: Twenty clinical variables significantly differed between the PCP and non-PCP groups. Multivariate binary logistic regression analysis revealed that dyspnea, body temperature>36.5°C, and age<1.46 years old were risk factors for PCP. The area under the curve of the diagnostic model was 0.958, the P-value of Hosmer-Lemeshow calibration test was 0.346, the R(2) of the calibration plot for the actual and predicted probability of PCP was 0.9555 (P<0.001), and the mean Brier score was 0.069. In addition, metagenomic next-generation sequencing revealed 79.41% (27/34) and 52.93% (28/53) mixed infections in the PCP and non-PCP groups, respectively. There was significantly more co-infection with cytomegalovirus and Streptococcus pneumoniae in the PCP group than that in the non-PCP group (p<0.05). CONCLUSIONS: Dyspnea, body temperature>36.5°C, and age<1.46 years old were found to be independent risk factors for PCP in pediatric patients. The probability of co-infection with cytomegalovirus and S. pneumoniae in the PCP group was significantly higher than that in the non-PCP group.