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Background: The purpose of this study was to analyze the clinical features of Mycoplasma pneumonia in children, especially refractory Mycoplasma pneumoniae pneumonia (RMPP), and to build a clinical prediction model to separate RMPP from general Mycoplasma pneumoniae pneumoniae (GMPP).
Methods: A total of 234 children with MPP were enrolled in this study, out of which 152 were GMPP and 82 were RMPP. Independent predictors of RMPP were screened via univariate and multivariate logistic regression, analyzing clinical characteristics, laboratory indicators (including inflammatory markers like the neutrophil-to-lymphocyte ratio (NLR), systemic immuneinflammation index (SII), and systemic inflammation response index (SIRI)), and blood immune parameters. Based on these predictors, a clinical prediction model was constructed, and its predictive efficacy and clinical value were evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).
Results: Compared to GMPP patients, those with RMPP had extended hospital stays and total fever days, a higher percentage of high-grade fever, a greater percentage of pulmonary atelectasis and unilateral lung lesions, and increased monocyte counts, platelet counts, CRP levels, SII, and SIRI. CRP, platelet count, monocyte count, SII, and SIRI were significantly associated with the risk of developing RMPP. CRP, monocyte count, and SII were independent predictors of RMPP, with CRP contributing the most, followed by SII. The constructed prediction model achieved an AUC of 0.82 (95% CI, 0.70 to 0.94) in the validation set. The DCA plot showed that the net benefit of the model was already greater than 0 at a high-risk threshold of approximately 0.1, and the smaller the threshold, the greater the net benefit in the range of 0.1 to 0.9.
Conclusions: The constructed model accurately distinguishes RMPP from GMPP, with high predictive efficacy and clinical value.
DOI: 10.7754/Clin.Lab.2025.250245
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