Development and internal validation of a nomogram for predicting the severity of community-acquired pneumonia in children.

OBJECTIVE

To investigate the etiological spectrum, epidemiological characteristics of community-acquired pneumonia (CAP) in hospitalized children, identify risk factors for severe CAP (sCAP) and establish a predictive nomogram model.

METHODS

A retrospective study included 1486 children with CAP admitted to Jinan Children's Hospital from January 2023 to December 2024. Etiological and epidemiological features were analyzed by age and season. Univariate and binary multivariate logistic regression were used to screen risk factors for sCAP. The nomogram model was constructed, with discriminative ability and calibration evaluated by receiver operating characteristic (ROC) and calibration curves.

RESULTS

The top detected bacteria were Streptococcus pneumoniae and Haemophilus influenzae, and the most common viruses were rhinovirus and adenovirus. The detection rate of Mycoplasma pneumoniae was 64.06%, and mixed infection rate was 50.47% (predominantly bacteria-virus co-infection). With increasing age, single bacterial/viral infection rates decreased, while M. pneumoniae and multi-pathogen mixed infection rates increased. Univariate analysis showed significant differences in initial body temperature, hospital stay, inflammatory indices (fibrinogen, CRP, D-dimer), immune ratios (NLR, PLR, SII) and pathogen type between sCAP and non-sCAP groups (all p5<0.05). Multivariate regression confirmed the initial body temperature, D-dimer, mixed infection and Mycoplasma. pneumoniae infection were independent risk factors for sCAP.

CONCLUSION

Initial body temperature, D-dimer, mixed infection and M. pneumoniae infection are reliable predictors of pediatric sCAP. The constructed early prediction model has significant value for clinical diagnosis, treatment and prognosis evaluation of pediatric CAP.