Mycoplasma pneumoniae infection prediction model for hospitalized community-acquired pneumonia children

Objectives We sought to develop a nomogram to predict Mycoplasma pneumoniae (Mp) infection among hospitalized children with community-acquired pneumonia (CAP) and compare it with another model developed from age and duration of fever. Methods Data on 5904 CAP children who were enrolled at Shanghai Children's Medical Center were retrospectively collected and divided into a training set (n = 4133) and a validation set (n = 1771). The model's performance was determined by concordance index (C-index), calibration curves, Brier scores, and decision curve analyses (DCAs). Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used for model comparisons. Results Incorporating five factors (age, duration of fever, erythrocyte sedimentation rate, leukocyte count, and neutrophil proportion), the nomogram achieved good C-index values of 0.74 (95% confidence interval [CI]: 0.72-0.76) and 0.75 (95% CI: 0.73-0.78) and good Brier scores of 0.14 (95% CI: 0.13-0.15) and 0.17 (95% CI: 0.15-0.18) in predicting Mp infection in the training and validation cohorts, respectively, and had moderate fitted calibration plots. The DCAs showed good clinical usefulness of the nomogram. Patients were effectively divided into low, medium, and high risk groups by two cut-off score points of the nomogram, 210 and 300. With the lower AIC (3673.5) and BIC (3774.7) value, the model of five predictors is the better model. Conclusions By using five predictor variables, a simple nomogram of good predictive accuracy for Mp infection and moderate agreements between the actual outcome and the predicted probability was constructed. It could serve as a tool to aid physicians in clinical decision-making processes.

Automated summary

A nomogram incorporating five factors achieved good predictive accuracy for Mp infection in hospitalized children with CAP, with moderate agreements between the actual outcome and the predicted probability. The model could effectively stratify patients into low, medium, and high risk groups, and outperformed the model developed from age and duration of fever based on AIC and BIC values.