Early diagnosis of Mycoplasma pneumoniae pneumonia: Development and validation of a predictive model based on clinical examination indicators
4 affiliations
- 1Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- 2The Respiratory Department of the Second Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China
- 3Zhongnan Hospital of Wuhan University, Institute of Hepatobiliary Diseases of Wuhan University, Transplantation Intensive Care Unit, Transplant Center of Wuhan University, Hubei Key Laboratory of Medical Technology on Transplantation, Wuhan, Hubei, China
- 4Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Clinical Research Center of Hubei Critical Care Medicine, Wuhan, Hubei, China
Abstract
Background: 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. Methods: 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. Results: 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). Conclusions: 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.
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BibTeX
@article{fan2026early,
title = {{Early diagnosis of Mycoplasma pneumoniae pneumonia: Development and validation of a predictive model based on clinical examination indicators}},
author = {Yongwei Fan and Jingrun Zhou and Zhaoxi Wang and Huaqin Pan and Jiarui Zhang and Ling Wang and Guqin Zhang},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0359335},
url = {https://doi.org/10.1371/journal.pone.0359335},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Early diagnosis of Mycoplasma pneumoniae pneumonia: Development and validation of a predictive model based on clinical examination indicators
AU - Yongwei Fan
AU - Jingrun Zhou
AU - Zhaoxi Wang
AU - Huaqin Pan
AU - Jiarui Zhang
AU - Ling Wang
AU - Guqin Zhang
PY - 2026
DA - 2026/10/05
JO - PLOS One
DO - 10.1371/journal.pone.0359335
UR - https://doi.org/10.1371/journal.pone.0359335
AB - Background: 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. Methods: 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. Results: 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). Conclusions: 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.
SN - 1932-6203
ER -