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Abstract

Objective: This study analyzed the relationship between clinical parameters and prognosis in children with Mycoplasma pneumoniae pneumonia positive lobar pneumonia and developed an early identification model. Methods: Relevant clinical parameters were collected. Patients were then categorized into two groups based on their length of hospital stay: 116 cases in the refractory group (≥10 days) and 94 cases in the non-refractory group (<10 >days). A univariate analysis of variance and binary logistic regression were utilized to develop a predictive model, accompanied by the construction of a nomogram. The model's performance was assessed using ROC curves, diagnostic calibration curves, and DCA curves. Furthermore, clinical data from 100 additional cases of MP-positive lobar pneumonia in children treated at other centers were gathered for external validation of the model. Results: Binary logistic regression analysis identified four independent risk factors for prolonged disease duration in children with MP-positive lobar pneumonia: ESR, globulin, LDH, and SF. We constructed a nomogram model based on these risk factors. In the training set, the area under the curve (AUC) was 0.869 (95% CI: 0.822–0.917), with a sensitivity of 68.54% and a specificity of 82.61%. For the test set, the AUC increased to 0.918 (95% CI: 0.866–0.971), demonstrating a sensitivity of 91.67% and a specificity of 78.69%. The DeLong test results indicated that the difference in AUC between the two datasets was not statistically significant (D = -1.724, P = 0.086). Calibration curve analysis confirmed that the nomogram model exhibited a good fit in both the training set (Hosmer-Lemeshow test, χ² = 8.120, P = 0.421) and the validation set (Hosmer-Lemeshow test, χ² = 14.601, P = 0.067. Decision curve analysis further demonstrated that the model performed significantly across a range of threshold probabilities. Conclusion: The nomogram model developed for predicting refractory MP-positive lobar pneumonia in children has significant clinical value and can guide personalized treatment strategies.

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