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Войти в исследования International journal of chronic obstructive pulmonary disease · 2026
Development and Internal Validation of a Retrospective Prediction Model for Fungal Infections in Patients with Chronic Obstructive Pulmonary Disease.
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Chen Xiaolan, Wu Mingyan, Wang Jingjing, Jiang Hongbin, Wang Shanmei
Аннотация
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.
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.