Utilizing Machine Learning to Determine Risk Factors for Hospital-Acquired Infections in Cancer Patients Experiencing Pneumonia Related to Immune Checkpoint Inhibitors - Report - MDSpire

Utilizing Machine Learning to Determine Risk Factors for Hospital-Acquired Infections in Cancer Patients Experiencing Pneumonia Related to Immune Checkpoint Inhibitors

  • By

  • Jianzhong Xie

  • Zhuo Zhao

  • Cuiyun Zhou

  • Junxiang Wang

  • Xiufang Lin

  • Lingyu Lai

  • Jinchan Yao

  • Haiyan Lin

  • Zuquan Weng

  • January 29, 2026

  • 0 min

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Clinical Report: Utilizing Machine Learning to Determine Risk Factors for Hospital-Acquired Infections in Cancer Patients Experiencing Pneumonia Related to Immune Checkpoint Inhibitors

Overview

This study evaluates the use of machine learning to identify risk factors for hospital-acquired infections in cancer patients with immune-related pneumonia. The findings indicate a significant incidence of nosocomial infections and highlight the importance of early identification and management strategies.

Background

The use of immune checkpoint inhibitors in cancer therapy has increased the incidence of immune-related adverse events, including pneumonia. Monitoring for hospital-acquired infections in these patients is critical due to their compromised immune systems. Machine learning presents a promising approach to enhance infection risk assessment and management in this vulnerable population.

Data Highlights

FactorValue
Average Age60.47 ± 10.54 years
Infected Patients55
Non-Infected Patients65
Overall Incidence Rate of Nosocomial Infection45.83%
Predominant PathogensFungi and Staphylococci (45.94%)

Key Findings

  • The study included 120 patients with immune-related pneumonia related to PD-1/PD-L1 inhibitors.
  • The overall incidence of nosocomial infections was 45.83%, with pulmonary disease being the most common.
  • Fungi and staphylococci were the predominant pathogens identified in infected patients.
  • Machine learning was utilized to analyze 14 nosocomial infection-related factors to construct an early warning model.
  • Patients in the infection group had a severity rate of 25.45% and a mortality rate of 10.91%.

Clinical Implications

Healthcare providers should implement machine learning tools to enhance the early detection of nosocomial infections in patients receiving immune checkpoint inhibitors. This proactive approach can lead to improved patient outcomes through timely intervention and tailored infection control measures.

Conclusion

The integration of machine learning in infection risk assessment represents a significant advancement in the management of cancer patients undergoing immunotherapy. Continued research is essential to refine these models and improve clinical practices.

References

  1. BMC Infectious Diseases, Springer Nature, 2026 -- Machine learning for identifying risk factors of nosocomial infection in cancer patients with immune checkpoint inhibitor-related pneumonia
  2. The ASCO Post, 2024 -- Can AI Tool Improve Detection of Immune-Related Adverse Events in Patients With Cancer?
  3. The ASCO Post, 2022 -- Machine Learning–Based Scoring of TILs and Outcomes With Immunotherapy in Patients With NSCLC
  4. ScienceDirect, 2025 -- Immune checkpoint inhibitor-related pneumonitis: From guidelines to the front lines
  5. Frontiers, 2025 -- Infectious adverse events associated with immune checkpoint inhibitors: a pharmacovigilance analysis based on FAERS database
  6. The ASCO Post — Can AI Tool Improve Detection of Immune-Related Adverse Events in Patients With Cancer?
  7. The ASCO Post — Machine-Learning Model May Predict Unplanned Hospitalizations After Radiation Therapy for Gastrointestinal Cancer
  8. Immune checkpoint inhibitor-related pneumonitis: From guidelines to the front lines - ScienceDirect
  9. Frontiers | Infectious adverse events associated with immune checkpoint inhibitors: a pharmacovigilance analysis based on FAERS database
  10. Machine learning for identifying risk factors of nosocomial infection in cancer patients with immune checkpoint inhibitor-related pneumonia | BMC Infectious Diseases | Springer Nature Link

Original Source(s)

Utilizing Machine Learning to Determine Risk Factors for Hospital-Acquired Infections in Cancer Patients Experiencing Pneumonia Related to Immune Checkpoint Inhibitors

  • by Jianzhong Xie, Zhuo Zhao, Cuiyun Zhou, Junxiang Wang, Xiufang Lin, Lingyu Lai, Jinchan Yao, Haiyan Lin, Zuquan Weng

  • January 29, 2026

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