ArticlePloS one2026
Early diagnosis of Mycoplasma pneumoniae pneumonia: Development and validation of a predictive model based on clinical examination indicators.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundMycoplasma pneumoniae accounts for a significant proportion of pathogens responsible for community-acquired pneumonia (CAP). Identifying Mycoplasma pneumoniae pneumonia (MPP) is challenging but essential for guiding early and appropriate antibiotic use.
methodsWe developed a predictive model using data from hospitalized CAP patients diagnosed with either MPP or bacterial pneumonia between January and December 2023. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to explore associations between clinical features (age, symptoms, laboratory findings) and the causative pathogens, and then a prediction model was developed based on the results. The model's clinical utility was assessed in an independent validation cohort comprising CAP patients who met the same inclusion/exclusion criteria in our hospital from 01/01/2024-30/06/2024.
resultsA total of 244 patients were diagnosed with CAP, including 117 with MPP and 127 with bacterial pneumonia. Our analysis revealed that patients with MPP were younger (37.00 years vs. 61.00 years), had procalcitonin levels mostly within the normal range, exhibited higher serum albumin levels (40.68 g/L vs. 36.34g/L), and higher levels of complement C1q (209.30 mg/L vs. 169.55 mg/L) compared to patients with bacterial pneumonia. In contrast, thrombin time was relatively prolonged in patients with bacterial pneumonia (14.00 s vs. 13.10 s). Based on these findings, we developed a clinical prediction model with strong discriminatory performance for early differentiation between MPP and bacterial pneumonia with an area under the curve (AUC) of 0.879 (95% CI, 0.833-0.925). Temporal validation demonstrated the model's stability and good predictive performance (AUC: 0.922 vs. derivation 0.879).
conclusionsOur study showed that age, serum albumin, complement C1q level, thrombin time, and normal-range procalcitonin levels were highly predictive of MPP. This study provides a predictive model for early differentiation between MPP and bacterial pneumonia to aid in timely clinical decision-making.
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