What question did this study set out to answer?

To develop and validate a diagnostic prediction model for Pneumocystis jirovecii pneumonia (PJP) in HIV-negative patients.

March 18, 2026Open Access

Construction of a Diagnostic Model for Pneumocystis Jirovecii Pneumonia in HIV-Negative Patients Based on Single-Center Patient Data

Key Points

To develop and validate a diagnostic prediction model for Pneumocystis jirovecii pneumonia (PJP) in HIV-negative patients.
Conducted a retrospective observational study on hospitalized patients with suspected PJP.
Included 115 HIV-negative patients, with 37 confirmed PJP cases.
Collected clinical data such as age, gender, comorbidities, and laboratory indicators.
Utilized the LASSO method for variable selection and created a predictive model.
Performed internal validation using Bootstrap resampling and visualized results as a nomogram.
Constructed a 7-variable PJP diagnostic prediction model.
Achieved an area under the ROC curve (AUC) of 0.895, indicating good model performance.
Calibration curve showed high consistency between predicted and actual probabilities.
Decision curve analysis confirmed the model's superior clinical net benefit.
Bootstrap validation confirmed stability of the AUC with consistent calibration.

Abstract

Background: Given the rising incidence, acute onset of Pneumocystis jirovecii pneumonia (PJP) in HIV-negative patients, and the limitations of existing diagnostic methods (poor sensitivity, low accessibility, and difficulty distinguishing infection from colonization), this study aims to develop and validate a diagnostic prediction model for PJP in this population. Methods: A retrospective observational study was conducted on hospitalized patients with suspected PJP at Zhejiang Provincial People’s Hospital from 2017 to 2022. Based on the inclusion and exclusion criteria, 115 HIV-negative patients were enrolled, including 37 with confirmed PJP and 78 with PJP ruled out. Clinical data including age, gender, comorbidities, laboratory indicators, and treatment history were collected. The LASSO method was used to screen predictive variables for constructing a model, which was internally validated via Bootstrap resampling and visualized as a nomogram. Results: A 7-variable PJP diagnostic prediction model was constructed following LASSO variable screening. The model exhibited good performance in distinguishing whether HIV-negative patients were afflicted with PJP, with an area under the ROC curve (AUC) of 0.895 (95% CI: 0.834– 0.956). The calibration curve revealed high consistency between predicted and actual probabilities, and decision curve analysis demonstrated its superior clinical net benefit over all-intervention and no-intervention strategies. Bootstrap internal validation yielded a stable AUC of 0.895 (95% CI: 0.831– 0.947) with consistent calibration. Conclusion: This study established a predictive model for Pneumocystis jirovecii pneumonia (PJP) in HIV-negative patients, and this effort is clinically meaningful. However, due to several limitations of the present study, it should be emphasized that the final nomogram only serves as a supplement to, rather than a replacement for, existing diagnostic approaches. The findings of this study warrant further validation in future multicenter or prospective investigations. Keywords: pneumocystis jirovecii pneumonia, HIV-negative patients, diagnosis, predictive model

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Cite This Study

Liang et al. (Sun,) studied this question.

synapsesocial.com/papers/69ba41e04e9516ffd37a1dd2 https://doi.org/https://doi.org/10.2147/idr.s573344

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