<?xml version='1.0' encoding='UTF-8'?><xml><records><record><source-app name="HighWire" version="7.x">Drupal-HighWire</source-app><ref-type name="Journal Article">17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">KITAMURA, YU</style></author><author><style face="normal" font="default" size="100%">TSUCHIYA, MASAMI</style></author><author><style face="normal" font="default" size="100%">IIHARA, HIROTOSHI</style></author><author><style face="normal" font="default" size="100%">SAKURAI, SHUJI</style></author><author><style face="normal" font="default" size="100%">ENDO, JUNKI</style></author><author><style face="normal" font="default" size="100%">KUMADA, KEISUKE</style></author><author><style face="normal" font="default" size="100%">TSUBATA, YUKARI</style></author></authors><secondary-authors></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Development and Validation of an EHR-based Algorithm for Identifying &lt;em&gt;Pneumocystis jirovecii&lt;/em&gt; Pneumonia</style></title><secondary-title><style face="normal" font="default" size="100%">In Vivo</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2026</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2026-05-01 00:00:00</style></date></pub-dates></dates><pages><style  face="normal" font="default" size="100%">1715-1721</style></pages><doi><style  face="normal" font="default" size="100%">10.21873/invivo.14322</style></doi><volume><style face="normal" font="default" size="100%">40</style></volume><issue><style face="normal" font="default" size="100%">3</style></issue><abstract><style  face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>