Development and Internal Validation of a Retrospective Prediction Model for Fungal Infections in Patients with Chronic Obstructive Pulmonary Disease.
- 1Department of Emergency, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China.
Abstract
Background
Patients with chronic obstructive pulmonary disease (COPD) are at an elevated risk of fungal infections, yet reliable tools for early risk stratification are lacking. This study aimed to analyze the risk factors associated with fungal infections in patients with COPD and to develop a predictive model.Methods
This study retrospectively analyzed patients with COPD who were admitted to Shanghai Pulmonary Hospital, between June 2021 and June 2023. Demographic and clinical data were extracted from electronic medical records. The risk factors were explored by univariate logistic regression and stepwise multivariate logistic regressions. Model performance was assessed using the receiver operating characteristic curve and area under the curve (AUC), along with calibration and decision curve analyses. The internal validation was performed using Bootstrap (n=1000) method.Results
A total of 498 patients with COPD were enrolled, with 78 patients in the fungal infection group. Multivariable logistic regression analysis revealed that PaO2 within the normal range (OR=2.04, 95% CI: 1.12-3.71, P=0.02) and mold mix specific IgE above the normal threshold (OR=2.37, 95% CI: 1.04-5.36, P=0.04) were independently associated with an increased risk of fungal infections, whereas lymphocyte counts within the normal range (OR=0.37, 95% CI: 0.21-0.63, P<0.01) were associated with a reduced risk. Procalcitonin, albumin, and medical history of COPD duration were also included in predictive model based on the regression analysis. The AUC was 0.74 (95% CI: 0.68-0.79), and the Hosmer-Lemeshow goodness-of-fit test and decision curves indicated good differentiation and calibration.Conclusion
This study identifies elevated PaO2, mold mix specific IgE and lymphocyte counts as risk factors for fungal infections. The predictive model incorporating PaO2, mold mix specific IgE, lymphocyte counts, procalcitonin, albumin, and medical history of COPD duration demonstrated good predictive performance, offering a potential tool for early identification of high-risk patients in clinical practice.
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BibTeX
@article{chen2026development,
title = {{Development and Internal Validation of a Retrospective Prediction Model for Fungal Infections in Patients with Chronic Obstructive Pulmonary Disease.}},
author = {Xiaolan Chen and Mingyan Wu and Jingjing Wang and Hongbin Jiang and Shanmei Wang},
journal = {International journal of chronic obstructive pulmonary disease},
year = {2026},
month = oct,
doi = {10.2147/copd.s562056},
url = {https://europepmc.org/article/MED/42898566},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Development and Internal Validation of a Retrospective Prediction Model for Fungal Infections in Patients with Chronic Obstructive Pulmonary Disease.
AU - Xiaolan Chen
AU - Mingyan Wu
AU - Jingjing Wang
AU - Hongbin Jiang
AU - Shanmei Wang
PY - 2026
DA - 2026/10/14
JO - International journal of chronic obstructive pulmonary disease
DO - 10.2147/copd.s562056
UR - https://europepmc.org/article/MED/42898566
AB - Background Patients with chronic obstructive pulmonary disease (COPD) are at an elevated risk of fungal infections, yet reliable tools for early risk stratification are lacking. This study aimed to analyze the risk factors associated with fungal infections in patients with COPD and to develop a predictive model.Methods This study retrospectively analyzed patients with COPD who were admitted to Shanghai Pulmonary Hospital, between June 2021 and June 2023. Demographic and clinical data were extracted from electronic medical records. The risk factors were explored by univariate logistic regression and stepwise multivariate logistic regressions. Model performance was assessed using the receiver operating characteristic curve and area under the curve (AUC), along with calibration and decision curve analyses. The internal validation was performed using Bootstrap (n=1000) method.Results A total of 498 patients with COPD were enrolled, with 78 patients in the fungal infection group. Multivariable logistic regression analysis revealed that PaO2 within the normal range (OR=2.04, 95% CI: 1.12-3.71, P=0.02) and mold mix specific IgE above the normal threshold (OR=2.37, 95% CI: 1.04-5.36, P=0.04) were independently associated with an increased risk of fungal infections, whereas lymphocyte counts within the normal range (OR=0.37, 95% CI: 0.21-0.63, P<0.01) were associated with a reduced risk. Procalcitonin, albumin, and medical history of COPD duration were also included in predictive model based on the regression analysis. The AUC was 0.74 (95% CI: 0.68-0.79), and the Hosmer-Lemeshow goodness-of-fit test and decision curves indicated good differentiation and calibration.Conclusion This study identifies elevated PaO2, mold mix specific IgE and lymphocyte counts as risk factors for fungal infections. The predictive model incorporating PaO2, mold mix specific IgE, lymphocyte counts, procalcitonin, albumin, and medical history of COPD duration demonstrated good predictive performance, offering a potential tool for early identification of high-risk patients in clinical practice.
SN - 1176-9106
SN - 1178-2005
ER -