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oai:figshare.com:article/34071874 2026-10-05T17:30:43Z category_7 category_8 category_13 category_21 category_46 category_931 category_61 category_64 category_132 portal_5 item_type_3 month_year_10_2026

<p>Imaging and bronchoscopy findings.</p> Yongwei Fan (22335925) Jingrun Zhou (22335931) Zhaoxi Wang (71954) Huaqin Pan (8040) Jiarui Zhang (2561809) Ling Wang (56577) Guqin Zhang (715853) Medicine Microbiology Genetics Biotechnology Immunology Information Systems not elsewhere classified Developmental Biology Cancer Infectious Diseases strong discriminatory performance receiver operating characteristic mycoplasma pneumoniae </ 833 &# 8211 2024 &# 8211 model &# 8217 timely clinical decision clinical examination indicators good predictive performance temporal validation demonstrated range procalcitonin levels complement c1q level 00 years ), clinical prediction model 00 years vs predictive model based predictive model prediction model complement c1q clinical utility clinical features highly predictive higher levels l ), xlink "> thrombin time study showed study provides significant proportion serum albumin relatively prolonged pathogens responsible normal range logistic regression including 117 guiding early explore associations exclusion criteria early differentiation early diagnosis december 2023 causative pathogens analysis revealed <div> <p>Background</p><p><i>Mycoplasma pneumoniae</i> accounts for a significant proportion of pathogens responsible for community-acquired pneumonia (CAP). Identifying <i>Mycoplasma pneumoniae</i> pneumonia (MPP) is challenging but essential for guiding early and appropriate antibiotic use.</p> <p>Methods</p><p>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.</p> <p>Results</p><p>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).</p> <p>Conclusions</p><p>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.</p></div> 2026-10-05T17:30:29Z Dataset Dataset 10.1371/journal.pone.0359335.t004 https://figshare.com/articles/dataset/_p_Imaging_and_bronchoscopy_findings_p_/34071874 CC BY 4.0