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Development and Validation of an EHR-based Algorithm for Identifying Pneumocystis jirovecii Pneumonia
Y U Kitamura1, Masami Tsuchiya2, Hirotoshi Iihara3,4,5
1Department of Respirology, Graduate School of Medicine, Gifu University, Gifu, Japan.
In Vivo (Athens, Greece)
|April 28, 2026
Summary
A new algorithm using prescription data accurately identifies Pneumocystis pneumonia (PCP) in electronic health records. This improves cancer patient research and prophylaxis strategies.
Area of Science:
- Oncology
- Infectious Diseases
- Health Informatics
Background:
- Pneumocystis jirovecii pneumonia (PCP) is a severe infection in immunocompromised cancer patients.
- Accurate identification of PCP in electronic health records (EHRs) is crucial for research and prophylaxis.
Purpose of the Study:
- To develop and validate a practical EHR-based algorithm for reliable PCP case identification.
Main Methods:
- Retrospective study of EHR data from adult patients with ICD-10 PCP codes.
- Evaluated algorithms combining diagnostic codes, anti-PCP prescriptions, lab tests, and chemotherapy exposure.
- Calculated positive predictive value (PPV) and capture rate against chart-confirmed cases.
Main Results:
- A prescription-enhanced algorithm (A1) achieved 91.7% PPV and 100% capture rate.
- Algorithms with lab tests or chemotherapy achieved 100% PPV but lower capture rates.
- Prescription-based algorithms significantly outperformed diagnostic codes alone.
Conclusions:
- Prescription-based algorithms enhance PCP identification accuracy in EHR data.
- This approach provides a scalable framework for oncology research and prophylaxis strategy development.
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