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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Inflammation
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1441838
This article is part of the Research Topic Community Series in Inflammation in Respiratory and Neurological Diseases and the immune-interaction of the lung-brain axis: Volume II View all 7 articles

Machine learning-based derivation and validation of three immune phenotypes for risk stratification and prognosis in community-acquired pneumonia: a retrospective cohort study

Provisionally accepted
Qiangqiang Qin Qiangqiang Qin 1Haiyang Yu Haiyang Yu 1Jie Zhao Jie Zhao 1Xue Xu Xue Xu 1Qingxuan Li Qingxuan Li 2Wen Gu Wen Gu 1*Xuejun Guo Xuejun Guo 1*
  • 1 Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
  • 2 Department of Respiratory and Critical Care Medicine, The Second Hospital of Jilin University, Jilin, China

The final, formatted version of the article will be published soon.

    Background: The clinical presentation of Community-acquired pneumonia (CAP) in hospitalized patients exhibits heterogeneity. Inflammation and immune responses play significant roles in CAP development. However, research on immunophenotypes in CAP patients is limited, with few machine learning (ML) models analyzing immune indicators. Methods: A retrospective cohort study was conducted at Xinhua Hospital, affiliated with Shanghai Jiaotong University. Patients meeting predefined criteria were included and unsupervised clustering was used to identify phenotypes. Patients with distinct phenotypes were also compared in different outcomes. By machine learning methods, we comprehensively assess the disease severity of CAP patients. Results: A total of 1156 CAP patients were included in this research. In the training cohort (n=809), we identified three immune phenotypes among patients: Phenotype A (42.0%), Phenotype B (40.2%), and Phenotype C (17.8%), with Phenotype C corresponding to more severe disease. Similar results can be observed in the validation cohort. The optimal prognostic model, SuperPC, achieved the highest average C-index of 0.859. For predicting CAP severity, the random forest model was highly accurate, with C-index of 0.998 and 0.794 in training and validation cohorts, respectively. Conclusion: CAP patients can be categorized into three distinct immune phenotypes, each with prognostic relevance. Machine learning exhibits potential in predicting 3 mortality and disease severity in CAP patients by leveraging clinical immunological data. Further external validation studies are crucial to confirm applicability.

    Keywords: Community-acquired pneumonia, Immune phenotype, machine learning, unsupervised clustering, risk stratification

    Received: 31 May 2024; Accepted: 05 Jul 2024.

    Copyright: © 2024 Qin, Yu, Zhao, Xu, Li, Gu and Guo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Wen Gu, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
    Xuejun Guo, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.