Abstract
Co-infection with Mycoplasma pneumoniae (MP) represents a clinically significant complication that often leads to prolonged hospital stay, increased mortality risk, and a higher demand for mechanical ventilation. This study constructed a risk prediction model for early identification of SARS-CoV-2 and MP co-infections. We retrospectively analyzed SARS-CoV-2 patients admitted between December 2022 and February 2023. Patients were stratified into co-infection and mono-infection groups based on MP antibody results. LASSO regression screened 55 variables, followed by multicollinearity checks. Restricted cubic splines (RCS) analyzed nonlinear relationships between continuous variables and infection risk. Conventional logistic and integrated RCS-logistic models were constructed and compared. LASSO identified seven predictors: age, globulin, anion gap, blood urea nitrogen (BUN), uric acid, prothrombin time (PT), and thrombin time (TT). Multivariate analysis showed globulin, anion gap, uric acid, and TT were independent risk factors, whereas BUN was protective. RCS revealed significant nonlinear associations between globulin, PT, and TT levels. The RCS-logistic model outperformed the conventional linear model, with a higher AUC of 0.827, better calibration (Brier score = 0.169), and greater net clinical benefit on decision curve analysis. This model enables early risk assessment and optimizes treatment, offering a methodological reference for predicting co-infections with emerging respiratory pathogens.
Similar content being viewed by others
Data availability
The datasets analyzed during the current study are available from the corresponding author on reasonable request.
References
Huang, A. C. C., Huang, C. G., Yang, C. T. & Hu, H. C. Concomitant infection with COVID-19 and Mycoplasma pneumoniae. Biomed. J. 43, 458–461. https://doi.org/10.1016/j.bj.2020.07.002 (2020).
Suzuki, R. et al. Attenuated fusogenicity and pathogenicity of SARS-CoV-2 Omicron variant. Nature 603, 700–705. https://doi.org/10.1038/s41586-022-04462-1 (2022).
WHO COVID-19 Dashboard. https://data.who.int/dashboards/covid19/deaths?n=c.
Ruiz-Bastián, M., Falces-Romero, I., Ramos-Ramos, J. C., de Pablos, M. & García-Rodríguez, J. Bacterial co-infections in COVID-19 pneumonia in a tertiary care hospital: Surfing the first wave. Diagn. Microbiol. Infect. Dis. 101, 115477. https://doi.org/10.1016/j.diagmicrobio.2021.115477 (2021).
Hoque, M. N. et al. Diversity and genomic determinants of the microbiomes associated with COVID-19 and non-COVID respiratory diseases. Gene Rep. 23, 101200. https://doi.org/10.1016/j.genrep.2021.101200 (2021).
Hoque, M. N. et al. Microbial co-infections in COVID-19: Associated microbiota and underlying mechanisms of pathogenesis. Microb. Pathog. 156, 104941. https://doi.org/10.1016/j.micpath.2021.104941 (2021).
Lansbury, L., Lim, B., Baskaran, V. & Lim, W. S. Co-infections in people with COVID-19: A systematic review and meta-analysis. J. Infect. 81, 266–275. https://doi.org/10.1016/j.jinf.2020.05.046 (2020).
Ai, T. et al. Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases. Radiology 296, E32–E40. https://doi.org/10.1148/radiol.2020200642 (2020).
Rangroo, R. et al. The severity of the co-infection of mycoplasma pneumoniae in COVID-19 patients. Cureus 14, e24563. https://doi.org/10.7759/cureus.24563 (2022).
Paget, C. & Trottein, F. Mechanisms of bacterial superinfection post-influenza: A role for unconventional T cells. Front. Immunol. 10, 336. https://doi.org/10.3389/fimmu.2019.00336 (2019).
Jia, L. et al. Mechanisms of severe mortality-associated bacterial co-infections following influenza virus infection. Front. Cell. Infect. Microbiol. 7, 338. https://doi.org/10.3389/fcimb.2017.00338 (2017).
Dhanoa, A., Fang, N. C., Hassan, S. S., Kaniappan, P. & Rajasekaram, G. Epidemiology and clinical characteristics of hospitalized patients with pandemic influenza A (H1N1) 2009 infections: the effects of bacterial coinfection. Virol. J. 8, 501. https://doi.org/10.1186/1743-422X-8-501 (2011).
Klein, E. Y. et al. The frequency of influenza and bacterial coinfection: A systematic review and meta-analysis. Influenza and Other Respiratory Viruses 10, 394–403. https://doi.org/10.1111/irv.12398 (2016).
Yang, M. et al. Bacterial coinfection is associated with severity of avian influenza A (H7N9), and procalcitonin is a useful marker for early diagnosis. Diagn. Microbiol. Infect. Dis. 84, 165–169. https://doi.org/10.1016/j.diagmicrobio.2015.10.018 (2015).
Zha, L. et al. Clinical features and outcomes of adult COVID-19 patients co-infected with Mycoplasma pneumoniae. J. Infect. 81, e12–e15. https://doi.org/10.1016/j.jinf.2020.07.010 (2020).
Zhang, L. et al. D-dimer levels on admission to predict in-hospital mortality in patients with Covid-19. J. Thromb. Haemost. 18, 1324–1329. https://doi.org/10.1111/jth.14820 (2020).
Tang, N., Li, D., Wang, X. & Sun, Z. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J. Thromb. Haemost. 18, 844–847. https://doi.org/10.1111/jth.14768 (2020).
Bryce, C. et al. Pathophysiology of SARS-CoV-2: The Mount Sinai COVID-19 autopsy experience. Mod. Pathol. 34, 1456–1467. https://doi.org/10.1038/s41379-021-00793-y (2021).
Wiersinga, W. J. & van der Poll, T. Immunopathophysiology of human sepsis. EBioMedicine 86, 104363. https://doi.org/10.1016/j.ebiom.2022.10436 (2022).
Qiu, J., Ge, J., Cao, L. & D-dimer The risk factor of children’s severe mycoplasma pneumoniae pneumonia. Front. Pediatr. 10, 828437. https://doi.org/10.3389/fped.2022.828437 (2022).
Chen, Q., Hu, T., Wu, L. & Chen, L. Clinical features and biomarkers for early prediction of refractory mycoplasma pneumoniae pneumonia in children. Emerg. Med. Int. 2024, 9328177. https://doi.org/10.1155/2024/9328177 (2024).
Kant, R. et al. Critical insights from recent outbreaks of Mycoplasma pneumoniae: decoding the challenges and effective interventions strategies. Int. J. Infect. Dis. 147, 107200. https://doi.org/10.1016/j.ijid.2024.107200 (2024).
Nicolson, G. L. Pathogenic mycoplasma infections in chronic illnesses: General considerations in selecting conventional and integrative treatments. Int. J. Clin. Med. 10, 477–522. https://doi.org/10.4236/ijcm.2019.1010041 (2019).
Achanti, A. & Szerlip, H. M. Acid-base disorders in the critically ill patient. Clin. J. Am. Soc. Nephrol. 18, 102–112. https://doi.org/10.2215/CJN.04500422 (2022).
Zemlin, A. E. et al. The association between acid-base status and clinical outcome in critically ill COVID-19 patients admitted to intensive care unit with an emphasis on high anion gap metabolic acidosis. Ann. Clin. Biochem. 60, 86–91. https://doi.org/10.1177/00045632221134687 (2022).
Huang, Y., Qu, J. & Zhen, P. Non-linear association between anion gap and 28-day mortality in critically ill patients with COVID-19: A cohort study from MIMIC IV database. J. Thorac. Dis. 17, 4662–4671. https://doi.org/10.21037/jtd-2024-1964 (2025).
Allegrini, S., Garcia-Gil, M., Pesi, R., Camici, M. & Tozzi, M. G. The good, the bad and the new about uric acid in cancer. Cancers. 14, 4959. https://doi.org/10.3390/cancers14194959 (2022).
Aydin Bahat, K. The effect of uric acid level on the severity of COVID-19. Acta Clin. Croat. 63, 251–259. https://doi.org/10.20471/acc.2024.63.02.1 (2024).
Pan, C., Chen, Y., Wang, S., Li, M. & Qu, S. The study of routine laboratory factors in children with mycoplasma pneumoniae pneumonia: serum uric acid may have anti-inflammatory effect. J. Clin. Lab. Anal. 35, e24026. https://doi.org/10.1002/jcla.24026 (2021).
Primorac, D. et al. Adaptive immune responses and immunity to SARS-CoV-2. Front. Immunol. 13, 848582. https://doi.org/10.3389/fimmu.2022.848582 (2022).
Ghahramani, S. et al. Laboratory features of severe vs. non-severe COVID-19 patients in Asian populations: a systematic review and meta-analysis. Eur. J. Med. Res. 25, 30. https://doi.org/10.1186/s40001-020-00432-3 (2020).
Papagiouvanni, I. et al. COVID-19 and liver injury: An ongoing challenge. World J. Gastroenterol. 29, 257–271. https://doi.org/10.3748/wjg.v29.i2.257 (2023).
Yu, Y., Ji, R., Xia, Y. & Liu, F. Multicenter analysis of clinical characteristics and risk factors for liver injury in severe Mycoplasma pneumoniae pneumonia. Pediatr. Infect. Dis. J. 45, 236–243. https://doi.org/10.1097/INF.0000000000005037 (2026).
Chaabane, N., Coupez, E., Buscot, M. & Souweine, B. Acute respiratory distress syndrome related to Mycoplasma pneumoniae infection. Respir. Med. Case Rep. 20, 89–91. https://doi.org/10.1016/j.rmcr.2016.11.016 (2016).
Ding, W. et al. Clinical features and risk factors for severe macrolide-resistant Mycoplasma pneumoniae pneumonia induced by 23 S rRNA A2063G mutation: A retrospective observational study. Eur. J. Clin. Microbiol. Infect. Dis. 44, 2475–2486. https://doi.org/10.1007/s10096-025-05215-4 (2025).
Author information
Authors and Affiliations
Contributions
KY, YS, XH, YD and JX conceived and designed the study. XC, BS, QZ, HL and LZ performed the data analyses. KY, YS, XH wrote the manuscript. All authors contributed to the article and approved the submitted version.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Ethics approval and consent to participate
Ethical approval for the study was obtained from the Institutional Review Board of Mindong Hospital Affiliated to Fujian Medical University in Ningde (Approval No. K2026011501). The study was conducted in accordance with the principles of the Declaration of Helsinki.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Ye, K., Su, Y., Hu, X. et al. A retrospective clinical risk prediction model for co‑infection with Mycoplasma pneumoniae in patients with COVID‑19 based on restricted cubic splines. Sci Rep (2026). https://doi.org/10.1038/s41598-026-44539-9
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-44539-9
