Table 1_Development and validation of a nomogram for early prediction of macrolide-unresponsive Mycoplasma pneumoniae pneumonia in children.docx

BackgroundMycoplasma pneumoniae (MP) is a leading cause of community-acquired pneumonia in children, with a significant increase in incidence following the COVID-19 pandemic. The emergence of macrolide-resistant M. pneumoniae (MRMP) has complicated treatment, leading to the concept of macrolide-unresponsive M. pneumoniae pneumonia (MUMPP), defined as lack of improvement after 72 h of macrolide therapy. Early identification of MUMPP is critical for timely intervention and improved outcomes. This study aimed to develop and validate a nomogram for early prediction of MUMPP in children.
MethodsWe conducted a retrospective study involving 278 pediatric patients with MP pneumonia, divided into training (n = 188) and validation (n = 90) sets. Demographic, clinical, laboratory, and chest CT imaging data were collected. Univariate and multivariate logistic regression analyses were used to identify independent predictors of MUMPP. A nomogram was constructed and validated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).
ResultsSix independent predictors were identified: tree-in-bud pattern, neutrophil-value, lymphocyte-value, creatine kinase (CK), platelet-to-lymphocyte ratio (PLR), and male gender. The nomogram demonstrated strong discriminatory power, with area under the curve (AUC) values of 0.838 (95% CI: 0.779–0.897) in the training set and 0.835 (95% CI: 0.752–0.918) in the validation set. Calibration and DCA confirmed good clinical utility.
ConclusionWe developed and validated a simple-to-use nomogram for predicting MUMPP in early stage. The nomogram demonstrates strong discriminatory power and calibration, and may be a practical tool for clinical practice.