In vivo (Athens, Greece) · 2026
Development and Validation of an EHR-based Algorithm for Identifying Pneumocystis jirovecii Pneumonia.
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Kitamura Y U, Tsuchiya Masami, Iihara Hirotoshi, Sakurai Shuji, Endo Junki, Kumada Keisuke, Tsubata Yukari
Аннотация
BACKGROUND/AIM: Pneumocystis jirovecii pneumonia (PCP) remains a life-threatening opportunistic infection in patients receiving chemotherapy and other immunosuppressive cancer treatments. Accurate identification of true PCP cases within real-world electronic health record (EHR) databases is essential for epidemiological research and optimization of prophylactic strategies in oncology practice. The aim of this study was to develop and validate a practical, EHR-based algorithm for reliably identifying PCP cases.
PATIENTS AND METHODS: This retrospective, single-center validation study used EHR data from a Japanese university hospital between April 2022 and March 2024. Adult patients (≧20 years) who were assigned an ICD-10 code for PCP were extracted, and true cases were confirmed by a detailed review of the patient records. Seven candidate algorithms combining diagnostic codes, therapeutic-dose anti-PCP prescriptions, laboratory testing, chemotherapy exposure, and prescription duration were evaluated. The positive predictive value (PPV) and capture rate were then calculated using chart-confirmed PCP as the reference standard.
RESULTS: Among 617 ICD-coded patients, 11 (1.8%) were confirmed as true PCP cases. The PPV of diagnostic codes alone was 1.8%. A prescription-enhanced algorithm (A1) identified 12 patients, including 11 true cases (PPV=91.7%; capture rate 100%). Algorithms incorporating β-D-glucan or PCR testing achieved PPVs of 100% with lower capture rates (63.6-81.8%). Incorporation of concurrent chemotherapy also resulted in a PPV of 100% with reduced capture. An algorithm requiring therapeutic-dose prescription for ≥21 days showed equivalent performance to A1.
CONCLUSION: Prescription-based algorithms substantially improve the accuracy of PCP case identification in EHR data compared with diagnostic codes alone. This straightforward, scalable approach offers a robust framework for real-world oncology research, enabling a more reliable evaluation of PCP incidence and informing future prophylaxis strategies for patients receiving anticancer treatment.
PATIENTS AND METHODS: This retrospective, single-center validation study used EHR data from a Japanese university hospital between April 2022 and March 2024. Adult patients (≧20 years) who were assigned an ICD-10 code for PCP were extracted, and true cases were confirmed by a detailed review of the patient records. Seven candidate algorithms combining diagnostic codes, therapeutic-dose anti-PCP prescriptions, laboratory testing, chemotherapy exposure, and prescription duration were evaluated. The positive predictive value (PPV) and capture rate were then calculated using chart-confirmed PCP as the reference standard.
RESULTS: Among 617 ICD-coded patients, 11 (1.8%) were confirmed as true PCP cases. The PPV of diagnostic codes alone was 1.8%. A prescription-enhanced algorithm (A1) identified 12 patients, including 11 true cases (PPV=91.7%; capture rate 100%). Algorithms incorporating β-D-glucan or PCR testing achieved PPVs of 100% with lower capture rates (63.6-81.8%). Incorporation of concurrent chemotherapy also resulted in a PPV of 100% with reduced capture. An algorithm requiring therapeutic-dose prescription for ≥21 days showed equivalent performance to A1.
CONCLUSION: Prescription-based algorithms substantially improve the accuracy of PCP case identification in EHR data compared with diagnostic codes alone. This straightforward, scalable approach offers a robust framework for real-world oncology research, enabling a more reliable evaluation of PCP incidence and informing future prophylaxis strategies for patients receiving anticancer treatment.