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A diagnostic model for discrimination between Pneumocystis jirovecii pneumonia and colonization based on multiple parameters
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  • Published: 25 April 2026

A diagnostic model for discrimination between Pneumocystis jirovecii pneumonia and colonization based on multiple parameters

  • Qianyu Ye1 na1,
  • Bo Xiang3 na1,
  • Jufeng Pan4,
  • Xiaoqi Luo5,
  • Gang Huang6,
  • Peisong Chen1,2,
  • Wenbin Lin1 &
  • …
  • Yili Chen1,7 

Scientific Reports (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

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  • Diseases
  • Medical research

Abstract

Distinguishing Pneumocystis jirovecii pneumonia (PJP) from colonization (PJC) is crucial due to overlapping symptoms but different treatments. This study aims to evaluate whether peripheral blood parameters can serve as a non-invasive tool for distinguishing PJP from PJC. We retrospectively enrolled 174 patients with PJP and 61 with PJC from the First Affiliated Hospital of Sun Yat-sen University (April 2022–March 2024). Peripheral blood parameters were analyzed and compared between groups. Normally distributed variables were assessed using Student’s t-test, while non-parametric data were analyzed with the Wilcoxon rank-sum test. A diagnostic model was subsequently developed based on significant hematological indicators. Utilizing a significance threshold of p < 0.05, red blood cell (RBC) and lymphocyte percentage (Lym%), while excluding neutrophil percentage (Neu%), procalcitonin (PCT), and lactic dehydrogenase (LDH) were used to build a random forest diagnostic model. The optimal XGBoost model achieved an AUC of 0.9991 internally and 0.787 in external validation. A web-based tool was developed to assist diagnosis. The findings of this study offer an effective tool for clinical practice, enabling physicians to accurately diagnose and differentiate between PJP and PJC, guiding appropriate treatment for patients.

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Acknowledgements

Not applicable.

Funding

This work is funded by Guangdong Provincial Center for Disease Control and Prevention Supports Talent Projects (0720240122); Noncommunicable Chronic Diseases-National Science and Technology Major Project (2024ZD0533100 & 2024ZD0533106); Basic and Applied Basic Research Foundation of Guangdong Province (2023A1515012526 & 2024A1515012332).

Author information

Author notes
  1. Qianyu Ye and Bo Xiang contributed equally to this work.

Authors and Affiliations

  1. Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat- sen University, No. 58, Zhong Shan 2nd Street, Guangzhou, 510080, China

    Qianyu Ye, Peisong Chen, Wenbin Lin & Yili Chen

  2. People’s Hospital of Boluo County, Huizhou, China

    Peisong Chen

  3. Department of Laboratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China

    Bo Xiang

  4. Department of Laboratory Medicine, The Fourth People’s Hospital of Nanhai, Foshan, China

    Jufeng Pan

  5. Guangzhou Medical University, Guangzhou, China

    Xiaoqi Luo

  6. Organ Transplantation Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Gang Huang

  7. GuiZhou Hospital of the First Affiliated Hospital, Sun Yat-sen University, Guizhou, China

    Yili Chen

Authors
  1. Qianyu Ye
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  2. Bo Xiang
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  3. Jufeng Pan
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  4. Xiaoqi Luo
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  6. Peisong Chen
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  7. Wenbin Lin
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  8. Yili Chen
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Corresponding authors

Correspondence to Peisong Chen, Wenbin Lin or Yili Chen.

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Cite this article

Ye, Q., Xiang, B., Pan, J. et al. A diagnostic model for discrimination between Pneumocystis jirovecii pneumonia and colonization based on multiple parameters. Sci Rep (2026). https://doi.org/10.1038/s41598-026-48520-4

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  • Received: 01 December 2025

  • Accepted: 08 April 2026

  • Published: 25 April 2026

  • DOI: https://doi.org/10.1038/s41598-026-48520-4

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Keywords

  • Pneumocystis jirovecii pneumonia
  • Colonization
  • Random forest
  • Diagnosis model
  • Peripheral blood parameters
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