Early diagnosis of Mycoplasma pneumoniae pneumonia: Development and validation of a predictive model based on clinical examination indicators.

Authors

Yongwei Fan, Jingrun Zhou, Zhaoxi Wang, Huaqin Pan, Jiarui Zhang, Ling Wang, Guqin Zhang

Published in

PloS one. Volume 21. Issue 10. Pages e0359335. Epub Oct 05, 2026.

Abstract

Mycoplasma 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.
We 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.
A 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).
Our 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.

PMID:
42832491
Bibliographic data and abstract were imported from PubMed on 06 Oct 2026.

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