Influence of C-reactive Protein, Immunoglobulin M, IgG, and Procalcitonin on Diagnosing <i>Mycoplasma pneumoniae</i> Pneumonia in Pediatric Patients

Abstract

Background: Serum C-reactive protein (CRP), immunoglobulin M (IgM), immunoglobulin G (IgG), and procalcitonin (PCT) are potential biomarkers for diagnosing Mycoplasma pneumoniae pneumonia (MPP) in pediatric patients. Objectives: This study evaluated the diagnostic value of CRP, IgM, IgG, and PCT for diagnosing MPP in pediatric patients. Methods: A cohort of 100 children with suspected MPP who were treated at our hospital between January 2021 and January 2024 was enrolled. MPP was diagnosed using a predefined composite reference standard comprising microbiological evidence, clinical features, and imaging findings, with blinded adjudication. Patients were categorized into MPP and non-MPP groups. Serum CRP, IgM, IgG, and PCT levels were compared between the groups. Results: The MPP group included 36 cases (36.00%), and the non-MPP group included 64 cases (64.00%). Serum CRP, PCT, IgG, and IgM levels were significantly higher in the MPP group than in the non-MPP group (P < 0.05). Elevated serum CRP, PCT, IgG, and IgM levels were independently associated with MPP (odds ratio > 1, P < 0.05). The areas under the receiver operating characteristic curves (AUCs) for the diagnosis of MPP were 0.722 (95% CI, 0.613 - 0.830) for CRP, 0.773 (95% CI, 0.676 - 0.871) for PCT, 0.764 (95% CI, 0.667 - 0.861) for IgM, 0.700 (95% CI, 0.598 - 0.803) for IgG, and 0.905 (95% CI, 0.842 - 0.968) for the combination of all 4 markers. The optimal cutoff values were 8.520 mg/L for CRP, 0.280 ng/mL for PCT, 1.600 g/L for IgM, and 11.165 g/L for IgG, with corresponding sensitivities of 0.806, 0.917, 0.861, and 0.833 and specificities of 0.453, 0.500, 0.562, and 0.547, respectively. The combined model showed a sensitivity of 0.889 and a specificity of 0.719 at the optimal cutoff. Conclusions: Serum CRP, IgM, IgG, and PCT levels were significantly higher in children with MPP. In this dataset, the combined model showed greater discrimination (AUC = 0.905) than any single biomarker, suggesting improved diagnostic performance when these markers are used in combination.