Clinical characteristics of Mycoplasma pneumoniae pneumonia in children and construction of a severe case prediction model: a retrospective study from Yan’an, China

Objective This study aims to systematically analyze the clinical characteristics of mycoplasma pneumoniae pneumonia (MPP) in community-acquired pneumonia in children, identify the risk factors for severe mycoplasma pneumoniae pneumonia (SMPP), construct a visual nomogram prediction model, and provide a theoretical basis for the early clinical identification and timely diagnosis and treatment of SMPP. Methods A retrospective analysis was conducted using clinical data from 500 children diagnosed with MPP who had been admitted to Affiliated Hospital of Yan'an University and Yan'an People's Hospital from January 2023 to December 2024.The patients were divided into mild and severe groups. Statistical analyses involved group comparisons using appropriate tests for continuous and categorical variables, multivariate logistic regression to identify independent risk factors, and receiver operating characteristic (ROC) curves to evaluate predictive performance ( P < 0.05 considered significant). Results Univariate and multivariate analyses showed that elevated Erythrocyte Sedimentation Rate (ESR), elevated Lactate Dehydrogenase (LDH), lung consolidation and elevated D-dimer were independent risk factors for SMPP, while elevated Albumin(ALB) was an independent protective factor for SMPP (all P < 0.05). ROC curve analysis showed that the efficacy of each indicator in diagnosing SMPP from high to low was: ESR >ALB >LDH >lung consolidation >D-dimer. The area under the ROC curve (AUC) of the nomogram prediction model for SMPP based on the above risk factors was 0.963 (95% confidence interval CI: 0.949–0.978); Both internal and external validated calibration curve and decision curve analysis (DCA) results confirmed that the nomogram model had good predictive power and clinical applicability. Conclusion The nomogram, with its intuitive graphical interface, can transform complex mathematical predictive models into convenient and accessible tools in clinical practice, and has significant clinical predictive value. Clinicians only need to add up the corresponding scores of the patient's indicators (LDH ≈ 22.5 points, ESR ≈ 40.5 points, albumin ≈ 57.5 points, D-dimer ≈ 23.5 points, lung consolidation ≈ 22 points, total ≈ 22.5 + 40.5 + 57.5 + 23.5 + 22 = 166 points), it can quickly assess the risk of developing SMPP [P (SMPP) ≈ 80%–90%], and this tool is particularly applicable in primary care scenarios.