Risk prediction for secondary pulmonary fungal infection during acute exacerbation of chronic obstructive pulmonary disease

Abstract

Background

Secondary fungal infections significantly affect the outcomes of patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This study aimed to develop and validate a clinically applicable prediction model for this complication.

Methods

In this retrospective cohort study, we analyzed 225 consecutive patients with AECOPD who were admitted to The Fourth Affiliated Hospital of Soochow University (July 2022–July 2024). Patients were randomly allocated to the training (n=177) and validation (n=48) sets. Through multivariable logistic regression analysis, we identified independent risk factors and constructed a nomogram. Model performance was assessed using the area under the curve (AUC), calibration plots with the Hosmer-Lemeshow test, and decision curve analysis (DCA).

Results

Three independent predictors were identified: the use of systemic glucocorticoids within 3 months before admission [odds ratio (OR) 2.943], admission to the hospital due to disease aggravation within the past year (OR 2.679), and the use of antibiotics for ≥14 days (OR 3.739). The nomogram demonstrated excellent discrimination {AUC 0.82 [95% confidence interval (CI): 0.75–0.88] in the training set; 0.80 (0.65–0.95) in the validation set} and good calibration (Hosmer-Lemeshow P>0.05). DCA confirmed the clinical utility across 10–80% risk thresholds.

Conclusions

This validated nomogram, which incorporates three easily obtainable clinical parameters, provides reliable, individualized risk predictions for secondary pulmonary fungal infections in patients with AECOPD, facilitating early targeted interventions.

Highlight box.

Key findings

• Developed a nomogram predicting secondary pulmonary fungal infection risk in acute exacerbation of chronic obstructive pulmonary disease (AECOPD) patients, incorporating three readily available clinical factors: recent systemic glucocorticoid use, prior hospitalization for exacerbation, and prolonged antibiotic therapy (≥14 days).

• The model showed excellent discrimination (area under the curve 0.82 in training, 0.80 in validation) and good calibration, with clinical utility across a wide risk threshold range.

• Fungal infection incidence was comparable across chronic obstructive pulmonary disease (COPD) severity stages, suggesting acute iatrogenic factors may outweigh baseline lung function in driving infection risk during exacerbations.

What is known and what is new?

• AECOPD patients are at risk for fungal infections due to immunosuppression and antibiotic use.

• This study integrates three easily obtainable clinical variables into a validated predictive tool, highlighting prior hospitalization as a novel and accessible risk predictor, and proposes that acute management factors may transiently elevate infection risk across all COPD stages.

What is the implication, and what should change now?

• Clinicians can use this nomogram for early, individualized risk assessment to guide targeted monitoring and preemptive strategies in high-risk AECOPD patients.

• Emphasis should be placed on minimizing unnecessary steroid/antibiotic exposure and enhancing surveillance in those with recent hospitalizations or prolonged therapy.

Introduction

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory airway disease characterized by lung parenchymal damage and small airway lesions of which its clinical manifestation is a progressive decline in lung ventilatory function. COPD causes more than 3 million deaths annually worldwide, making it the third leading cause of death globally and imposing significant economic and social burdens (1-3). Acute exacerbation of COPD (AECOPD) refers to clinical symptoms such as dyspnea, purulent sputum production, and increased sputum volume. It is the primary cause of mortality and hospitalization in patients and significantly affects their daily activities and quality of life (4). Patients with AECOPD are typically treated with corticosteroids and antibiotics. In cases complicated by respiratory acidosis, non-invasive mechanical ventilation (NIV) may be required. These interventions, while necessary, however, can impair immune function and increase the risk of secondary pulmonary fungal infections (5,6). Pulmonary fungal infections are the most commonly occurring type of infection during AECOPD treatment; they exacerbate the condition, increase medical costs, and are associated with high mortality rates, making them key factors influencing patient outcomes (7). The risk factors for fungal infections are complex, and identifying their risk factors and pathological characteristics is crucial for reducing infection risk, developing rational antimicrobial treatment strategies, and improving outcomes. Although extensive research related to these issues has been conducted in recent years, most studies have focused on bacterial infections and antibiotic resistance, with inconsistent conclusions regarding the risk factors for secondary fungal infections in AECOPD patients. Additionally, research on predictive models for fungal infections remains limited and is still in its exploratory phase (5,8,9). This study identified risk factors for secondary pulmonary fungal infections in patients with AECOPD and constructed a nomogram prediction model to provide a theoretical basis and practical tools for clinical prevention and treatment. We present this article in accordance with the TRIPOD reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-aw-2006/rc).

Methods

Clinical data

We performed a single-center, retrospective cohort study at The Fourth Affiliated Hospital of Soochow University in Suzhou, China. This tertiary care hospital serves as a regional referral center. We conducted a retrospective cohort study using data from 225 consecutive patients with AECOPD admitted to The Fourth Affiliated Hospital of Soochow University between July 2022 and July 2024. The entire cohort was randomly allocated into a training set (n=177, 78.7%) for model development and a validation set (n=48, 21.3%) for internal validation.

The inclusion criteria were as follows: (I) meeting the diagnostic criteria outlined in the “Guidelines for the Diagnosis and Treatment of Chronic Obstructive Pulmonary Disease (2021 Revised Edition)” established by the Chinese Medical Association in 2021 (10); (II) undergoing sputum fungal culture and routine sputum culture; and (III) having complete clinical data. Exclusion criteria: (I) lactating or pregnant women; (II) patients with concomitant respiratory diseases such as tuberculosis, bronchial asthma, or pulmonary tuberculosis; (III) patients who died before the diagnosis of fungal infection; (IV) patients with malignant tumors; (V) patients with severe immune deficiency; (VI) patients with other infections; and (VII) patients with insufficient information. This study was approved by the Medical Ethics Committee of The Fourth Affiliated Hospital of Soochow University (Suzhou Dushu Lake Hospital) (No. 251243). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Due to the retrospective nature of the study, the requirement for informed consent was waived by the Ethics Committee.

Research methods

Clinical data collection

The collected data included age, sex, smoking status, COPD duration, history of long-term home oxygen therapy, presence of comorbidities, respiratory failure, number of AECOPD episodes in the past year (≥2 episodes), hospitalization due to worsening condition within the past year, COPD severity grading, antibiotic use within the 3 months prior to admission, systemic corticosteroid use within the 3 months prior to admission, length of current hospitalization, invasive procedures during hospitalization, duration of mechanical ventilation [cumulative non-invasive ventilation (NIV) use >14 days during the index hospitalization, excluding pre-admission chronic use], presence of hypoalbuminemia (serum albumin concentration <30 g/L), use of carbapenem antibiotics during hospitalization, duration of antimicrobial therapy, types of antimicrobial agents used, duration of systemic steroid use, serum procalcitonin (PCT) and C-reactive protein (CRP) levels at diagnosis of fungal infection, normal white blood cell count and neutrophil count, and presence of hepatic or renal dysfunction. To minimize assessment bias, the researchers who adjudicated the patient outcomes were different from and blinded to the identities of the data collectors responsible for extracting clinical predictor variables.

Specimen collection, pathogen culture, and identification

The specimen collection methods included the following: (I) the patient rinsed their mouth with clean water at least three times in the morning and collected deep sputum; (II) a fiberoptic bronchoscope was used to reach the bronchi and collect sputum samples from the area with the most secretions; and (III) a disposable sputum collection device was used to connect the bedside suction equipment and connecting tube. Sterile forceps were used to insert the suction tube into the deep trachea, and the suction device was activated to collect sputum into a sterile collection container. The samples were immediately sent for testing. Prior to culture, a Gram staining smear examination was performed. A qualified sputum sample was defined as having >25 white blood cells and <10 epithelial cells per low-power field or a squamous epithelial cell-to-white blood cell ratio <1:2.5. Qualified samples were subjected to microscopic examination and culture identification. The samples were inoculated onto blood agar medium for culture. After culture, the fungi were isolated, and the isolated strains were identified using an automated microbial identification system (BD Phoenix M50, Franklin Lakes, NJ, USA). All testing procedures were conducted in accordance with the standards of the “National Clinical Laboratory Operating Procedures (4th Edition)”.

Diagnostic criteria for pulmonary fungal infections

The primary outcome of this study was the occurrence of a secondary pulmonary fungal infection during the patient’s index hospitalization for AECOPD. Diagnosis was based on the Chinese Medical Association’s ‘Expert Consensus on the Diagnosis and Treatment of Pulmonary Mycoses’ (11), which includes patients with confirmed, clinical, and presumptive diagnoses. To ensure that the infections were truly secondary and acquired during hospitalization, we included only infections that were newly diagnosed more than 48 h after admission. Patients with unsuitable specimens, no clinical manifestations of fungal infection, or atypical clinical manifestations, and those who had not received clinical antifungal treatment but had positive sputum cultures were classified as colonized and were not included in the outcome group. Owing to the retrospective nature of this study, to ensure objectivity and consistency, the outcome was strictly determined based on predefined objective criteria from the expert consensus (11). The outcome for each patient was assessed from admission until discharge, death, or the initiation of antifungal therapy.

Statistical analysis

The study included data from 225 consecutive, eligible AECOPD patients admitted between July 2022 and July 2024. After those with incomplete data were excluded, the cohort was randomly split into a training set (n=177) and a validation set (n=48). The training set contained 48 outcome events, resulting in an events-per-variable (EPV) ratio of 16, which exceeds the recommended minimum EPV ≥10, indicating a sufficient sample size to minimize overfitting. The validation set included 12 outcome events, providing a reasonable basis for initial internal performance assessment.

Statistical data analysis and graphing were performed using SPSS 27 and R software (version 4.3.1). A normality test was first conducted for continuous data. If the data followed a normal distribution, they are expressed as the means ± standard deviations ($\overline{x}\pm s$); otherwise, they are expressed as the medians and interquartile ranges [M (Q1, Q3)]. Categorical data are expressed as frequencies (percentages). For comparisons of differences between the training and validation sets, as well as between the infected and noninfected groups within the training set, continuous variables were analyzed using t-tests if they met the assumptions of a normal distribution and homogeneity of variance; otherwise, nonparametric Mann-Whitney U tests were used. Chi-squared or Fisher’s exact tests were used for categorical variables. Statistical significance was set at P<0.05.

Factors with P<0.05 in the comparison between the infected and noninfected groups in the training set were included in a multivariate logistic regression analysis to identify independent risk factors for pulmonary fungal infection and to construct a nomogram prediction model. The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the discriminative ability of the model in both the training and validation sets. Calibration curves and the Hosmer-Lemeshow test were used to validate the calibration. Decision curve analysis (DCA) was used to evaluate the clinical utility of the model. This validation is an internal validation performed by randomly splitting the original cohort. As a result, there were no differences between the development and validation datasets regarding the study setting, eligibility criteria, or definition and assessment of the outcome and predictors.

Results

Clinical characteristics of patients with AECOPD

A total of 244 patients with AECOPD were enrolled in this study. After 19 cases were excluded according to the exclusion criteria, 225 cases were included and randomly allocated into a training set (n=177) and a validation set (n=48), as illustrated in Figure 1. The primary outcome of secondary pulmonary fungal infection occurred in 48 patients (27.1%) in the training set and 12 patients (25.0%) in the validation set. The mean age was 73.7±8.0 years, and 48 (27.1%) patients had secondary fungal infections. The validation set included 48 patients with an average age of 76.3±7.4 years, 12 (25%) of whom developed pulmonary fungal infections. The specific clinical characteristics of the two groups are presented in Table 1. The comparison of fungal infection rates between the two groups yielded a P value of 0.77, indicating that there was no statistically significant difference between the two groups.

Figure 1.

Flow of participants through the study. A total of 244 patients were initially screened. Nineteen patients were excluded: 14 based on predefined exclusion criteria (immunosuppression, malignant tumors) and 5 due to missing critical data (smoking index, n=4; grade of COPD, n=1). The remaining 225 patients constituted the final cohort and were randomly split into training and validation sets. The numbers of patients with and without the outcome (secondary pulmonary fungal infection) in each set are shown. AECOPD, acute exacerbation of chronic obstructive pulmonary disease; COPD, chronic obstructive pulmonary disease.

Table 1. Clinical characteristics of the inpatients with AECOPD in the training set and validation set.

Characteristic	Training set (n=177)	Validation set (n=48)	P
Pulmonary fungal infection	48 (27.1)	12 (25.0)	0.77
Age, years	73.7±8.0	76.3±7.4	0.05
Gender male	151 (85.3)	41 (85.4)	0.99
Smoking index (>400)	44 (24.9)	14 (29.2)	0.55
Course of COPD (years)	10 [3, 12.5]	10 [4, 10]	0.51
Long-term home oxygen therapy	12 (6.8)	9 (18.8)	0.02
Underlying diseases complicated
Hypertension	76 (42.9)	21 (44.7)	0.83
Diabetes	25 (14.1)	7 (14.6)	0.94
Coronary heart disease	11 (6.2)	3 (6.3)	>0.99
Pulmonary heart disease	5 (2.8)	2 (4.2)	0.64
Respiratory failure	42 (23.7)	12 (25.0)	0.86
AECOPD occurred ≥2 times in the past year	81 (45.8)	18 (37.5)	0.31
Admission to the hospital due to aggravation of disease within the past year	60 (33.9)	15 (31.3)	0.73
Grade of COPD*			0.86
1–2	157 (88.7)	43 (89.6)
3–4	20 (11.3)	5 (10.4)
Use of antibiotics within 3 months before admission	113 (63.8)	25 (52.1)	0.14
Duration of hospitalization ≥15 days	32 (18.1)	4 (8.3)	0.10
Invasive operation	13 (7.3)	4 (8.3)	0.76
Duration of assisted ventilation ≥14 days	30 (16.9)	6 (12.5)	0.46
Hypoalbuminemia	5 (2.8)	1 (2.1)	>0.99
Type of antibiotics used ≥2	66 (37.3)	13 (27.1)	0.19
Use time of antibiotics ≥14 days	38 (21.5)	5 (10.4)	0.08
Use of carbapenem antibiotics	14 (7.9)	3 (6.3)	>0.99
Normal white blood cell count	74 (41.8)	28 (58.3)	0.041
Normal neutrophil count	45 (25.4)	15 (31.3)	0.42
Duration of systemic corticosteroid treatment ≥7 days	129 (72.9)	34 (70.8)	0.78
Use of systemic glucocorticoids within 3 months before admission	78 (44.1)	18 (37.5)	0.42
PCT ≥0.5 ng/mL when diagnosed with fungal infection	11 (6.2)	2 (4.2)	0.59
CRP ≥10 mg/L when diagnosed with fungal infection	85 (48.0)	21 (43.8)	0.60
Hepatic/renal insufficiency	23 (13.0)	6 (12.5)	0.93

Data are presented as n (%), mean ± standard deviation, or median [interquartile range]. *, the GOLD stage was obtained from the patient’s latest pulmonary function test within the past year. This analysis includes only patients whose complete data for all the variables are presented. Consequently, there are no missing data in this table. AECOPD, acute exacerbation of chronic obstructive pulmonary disease; COPD, chronic obstructive pulmonary disease; CRP, C-reactive protein; GOLD, Global Initiative for Chronic Obstructive Lung Disease; PCT, procalcitonin.

Analysis of risk factors for secondary pulmonary fungal infections in patients with AECOPD

The clinical characteristics of the infection and noninfection groups in the training set are shown in Table 2. The results of the univariate analysis indicated that the clinical characteristics of the two groups of patients were similar, including the number of AECOPD episodes in the past year (≥2), hospitalization due to disease exacerbation in the past year, antibiotic use in the three months prior to hospitalization, length of stay during the current hospitalization (≥15 days), duration of mechanical ventilation (≥14 days), number of antimicrobial agents used (≥2 types), duration of antimicrobial therapy (≥14 days), duration of systemic steroid use (≥7 days), and use of systemic corticosteroids within the three months prior to admission (P<0.05).

Table 2. Comparison of clinical characteristics between the infected and noninfected groups in the training set.

Characteristic	Infection group (n=48)	Non-infection group (n=129)	P
Age, years	73.65±8.42	73.78±7.90	0.92
Gender male	41 (85.4)	110 (85.3)	0.98
Smoking index (>400)	13 (27.1)	31 (24.0)	0.68
Course of COPD (years)	10 [4.25, 20]	8 [3, 10]	0.51
Long-term home oxygen therapy	2 (4.2)	10 (7.8)	0.52
Underlying diseases complicated
Hypertension	20 (62.5)	56 (43.4)	0.84
Diabetes	7 (14.6)	18 (14.0)	0.92
Coronary heart disease	3 (6.2)	8 (6.2)	>0.99
Pulmonary heart disease	3 (6.3)	2 (1.6)	0.12
Respiratory failure	13 (27.1)	29 (22.5)	0.52
AECOPD occurred ≥2 times in the past year	30 (62.5)	51 (39.5)	0.006
Admission to the hospital due to aggravation of disease within the past year	27 (56.2)	33 (25.6)	<0.001
Grade of COPD*			0.76
1–2	42 (87.5)	115 (89.1)
3–4	6 (12.5)	14 (10.9)
Use of antibiotics within 3 months before admission	41 (85.4)	72 (55.8)	<0.001
Duration of hospitalization ≥15 days	18 (37.5)	14 (10.9)	<0.001
Invasive operation	5 (10.4)	8 (6.2)	0.34
Duration of assisted ventilation ≥14 days	14 (29.2)	16 (12.4)	0.008
Hypoalbuminemia	3 (6.3)	2 (1.6)	0.12
Type of antibiotics used ≥2	27 (56.2)	39 (30.2)	0.001
Use time of antibiotics ≥14 days	23 (47.9)	15 (11.6)	<0.001
Use of carbapenem antibiotics	7 (14.6)	7 (5.4)	0.06
Normal white blood cell count	15 (31.3)	59 (45.7)	0.08
Normal neutrophil count	11 (22.9)	34 (26.4)	0.64
Duration of systemic corticosteroid treatment ≥7 days	43 (89.6)	86 (66.7)	0.002
Use of systemic glucocorticoids within 3 months before admission	36 (75.0)	42 (32.6)	<0.001
PCT ≥0.5 ng/mL when diagnosed with fungal infection	3 (6.3)	8 (6.2)	0.52
CRP ≥10 mg/L when diagnosed with fungal infection	22 (45.8)	63 (48.8)	0.72
Hepatic/renal insufficiency	7 (14.6)	16 (12.4)	0.70

Data are presented as n (%), mean ± standard deviation, or median [interquartile range]. *, the GOLD stage was obtained from the patient’s latest pulmonary function test within the past year. AECOPD, acute exacerbation of chronic obstructive pulmonary disease; COPD, chronic obstructive pulmonary disease; CRP, C-reactive protein; GOLD, Global Initiative for Chronic Obstructive Lung Disease; PCT, procalcitonin.

The results of the analysis revealed that the use of systemic corticosteroids within three months prior to admission, hospitalization due to disease exacerbation within the past year, and duration of antimicrobial drug use (≥14 days) were independent risk factors for secondary pulmonary fungal infection in AECOPD patients, as detailed in Table 3 and Figure 2. For the validation set, the predicted probability for each patient was calculated by applying the final logistic regression model derived from the training set. The calculation was based on the equation ln [P/(1 − P)] = −3.478 + 1.319 × (use of antibiotics ≥14 days) + 0.986 × (hospitalization within past year) + 1.079 × (systemic glucocorticoid use), where P is the probability of secondary pulmonary fungal infection, and all the predictors are coded as 1 for ‘yes’ or 0 for ‘no’. The predicted probabilities for the validation cohort, derived from this model, were used for all subsequent performance assessments.

Table 3. Results of the multivariate logistic regression analysis.

Variable	β	Wald	OR (95% CI)	P
Use time of antibiotics ≥14 days	1.319	3.899	3.739 (1.010–13.841)	0.048
Admission to the hospital due to aggravation of disease within the past year	0.986	4.842	2.679 (1.114–6.445)	0.03
Use of systemic glucocorticoids within 3 months before admission	1.079	4.122	2.943 (1.038–8.342)	0.042

CI, confidence interval; OR, odds ratio.

Figure 2.

Forest plot of multivariate analysis results for influencing factors of secondary pulmonary fungal infection in AECOPD patients. AECOPD, acute exacerbation of chronic obstructive pulmonary disease.

Establishment of a nomogram prediction model

The final multivariable logistic regression model was developed using complete-case data from all 177 patients in the training set. Among these, 48 patients (27.1%) experienced an outcome event (secondary pulmonary fungal infection), resulting in a total of 48 outcome events available for the model development analysis.

The three independent risk factors identified through multiple regression analysis of the training set—use of systemic glucocorticoids within three months prior to admission, hospitalization due to disease exacerbation within the past year, and duration of antimicrobial use (≥14 days)—were incorporated into the prediction model. Based on the results of the multiple logistic regression analysis, a nomogram prediction model was established for secondary lower respiratory fungal infections in patients with AECOPD (Figure 3).

Figure 3.

Nomogram prediction model for secondary pulmonary fungal infection in patients with AECOPD. 0, no fungal infection; 1, secondary pulmonary fungal infection. AECOPD, acute exacerbation of chronic obstructive pulmonary disease.

To use the nomogram, locate the patient’s status for each predictor on the corresponding axis, draw a line upward to the ‘Points’ line to determine the score for each variable, and sum these scores to obtain the ‘Total Points’. Finally, draw a line downward from the ‘Total Points’ axis to the ‘Predicted Probability’ axis to estimate the individual risk of secondary pulmonary fungal infection.

Model discrimination and calibration

ROC curves for the predictive model were plotted separately for the training and validation sets. The AUC of the training set ROC curve was 0.82 [95% confidence interval (CI): 0.752–0.8845], and the AUC of the validation set ROC curve was 0.80 (95% CI: 0.6476–0.952), indicating that the model had high predictive ability, as shown in Figure 4.

Figure 4.

ROC curve of the nomogram for predicting secondary pulmonary fungal infection in AECOPD patients. AECOPD, acute exacerbation of chronic obstructive pulmonary disease; AUC, area under the curve; CI, confidence interval; ROC, receiver operating characteristic.

The calibration curves for the prediction model were plotted separately for the training and validation sets, as shown in Figure 5. The Hosmer-Lemeshow test P values for the training and validation sets were 0.40 and 0.74, respectively, both greater than 0.05, indicating that there was no significant difference between the predicted and actual probabilities and that the model fit was good, as shown in Figure 5.

Figure 5.

Calibration curves of the nomogram for predicting secondary pulmonary fungal infection in patients with AECOPD. (A) Calibration curve of the training set; (B) calibration curve of the validation set. AECOPD, acute exacerbation of chronic obstructive pulmonary disease.

DCA

DCA was performed on the training and validation sets for this predictive model, as shown in Figure 6, where the horizontal line (none) represents a net benefit of 0; that is, we assumed that no patients received treatment, and the sloping line (all) represents the assumption that all patients received treatment. A comparison of the decision curves of the training and validation sets revealed that the column chart prediction model yielded positive net benefits for predicting risk thresholds between 10% and 80%.

Figure 6.

Decision curve analysis of the nomogram for predicting secondary pulmonary fungal infection in AECOPD patients. (A) Decision curve analysis of the training set; (B) decision curve analysis of the validation set. AECOPD, acute exacerbation of chronic obstructive pulmonary disease.

Discussion

COPD is a common chronic disease whose prevalence rate increases each year (12,13). Treatment strategies must be adjusted during the acute exacerbation phase of COPD. Recurrent AECOPD episodes, frequent hospitalizations, and the use of corticosteroids and antibiotics can alter lung structure, impair lung function, and increase the risk of fungal infections, thereby increasing patient mortality rates. Fungal infections are challenging to diagnose because of their unclear clinical and imaging manifestations, leading to worsening disease outcomes and increased mortality. Studies have shown that early detection and intervention of invasive fungal infections are crucial for improving patient outcomes (14,15). In this study, a retrospective analysis of independent risk factors for secondary pulmonary fungal infections in patients with AECOPD was conducted, a clinical-based nomogram prediction model was developed, and internal validation was performed to assist clinicians in the early prediction of the likelihood of secondary fungal infections, aid in determining treatment strategies, and improve patient outcomes.

This retrospective study included a large number of patients with AECOPD, including those with secondary pulmonary fungal infections. The probability of fungal infection among patients was 26.67%, with 60 fungal cases detected, including 31 Candida cases (51.67%) and 29 mold cases (48.33%), comprising 26 Aspergillus spp. (89.7%), 1 Penicillium spp. (3.4%), 1 Rhizomucor spp. (3.4%), and 1 Cladosporium spp. (3.4%). Multivariate logistic regression analysis revealed that the use of systemic corticosteroids within three months prior to admission, hospitalization due to disease exacerbation within the past year, and duration of antimicrobial drug use (≥14 days) were independent risk factors for secondary pulmonary fungal infections in patients with AECOPD.

Patients with AECOPD often receive glucocorticoid therapy, and the use of systemic glucocorticoids can impair the immune system, increasing the risk of fungal infection. This study revealed that the use of systemic glucocorticoids within the previous three months was an independent risk factor for secondary pulmonary fungal infection in AECOPD patients, which is consistent with previous findings (16).

Patients with COPD frequently experience recurrent respiratory tract infections, leading to repeated use of antibiotics to control the disease, particularly during acute exacerbations. However, while antibiotics eliminate pathogenic bacteria, they also disrupt the body’s normal microbial flora, causing damage to normal microbial structure and leading to the proliferation of opportunistic pathogens, which can easily result in pulmonary fungal infections (17). Recent studies have shown that long-term antibiotic use can increase patient susceptibility to invasive Candida albicans infection (18). In this study, the proportion of patients in the infection group who used antibiotics for ≥14 days was significantly greater than that in the noninfection group, and antibiotic use for ≥14 days was an independent risk factor for pulmonary fungal infection in AECOPD patients. It should be noted that prolonged antibiotic use (>14 days) often reflects a complicated clinical course rather than routine management of AECOPD. In our cohort, this likely identified patients with poor treatment response, serious complications (e.g., cor pulmonale, respiratory failure), or significant comorbidities, all of which can necessitate extended therapy and independently increase the risk of fungal superinfection.

Notably, the incidence of secondary pulmonary fungal infection was comparable between patients with mild-to-moderate [Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1&2] and severe-to-very severe (GOLD 3&4) airflow limitation in our cohort. This observation suggests that during an acute exacerbation, the risk of secondary pulmonary fungal infection may be driven more strongly by the intensity of acute iatrogenic immunosuppression (e.g., recent systemic corticosteroid use, broad-spectrum antibiotics) and the associated physiological stress, which can transiently elevate infection susceptibility across all COPD stages, potentially overriding the influence of baseline lung function severity alone. This hypothesis underscores the critical role of acute clinical management in modulating infection risk and warrants further investigation.

In this study, the results of multivariate regression analysis revealed that hospitalization due to exacerbation of the condition during the past year was an independent risk factor for secondary pulmonary fungal infection in AECOPD patients. Patients who were hospitalized because of exacerbation of the condition during the past year were considered high-risk AECOPD patients. Hospitalization due to acute exacerbations may reflect poor baseline lung function, severe systemic inflammation, and immunosuppression, increasing the susceptibility of patients to secondary pulmonary infections. However, frequent use of antibiotics and systemic steroids during exacerbations in high-risk patients with AECOPD also increases the risk of fungal infection. To date, studies on risk factors for secondary fungal infections in patients with AECOPD have not focused on hospitalization due to acute exacerbations during the past year. Clinicians can conduct G and GM tests on patients who have been hospitalized due to acute exacerbations during the past year to monitor the possibility of subsequent fungal infections. Emphasizing regular medication use during stable periods and engaging in respiratory rehabilitation exercises can reduce the risk of acute exacerbations in patients with COPD. This risk factor is easily obtainable and can be used by clinicians to assess the risk of fungal infection using a nomogram.

A nomogram is a simple, visual predictive model commonly used to assist clinical decision-making and provide evidence for developing personalized treatment plans (19-21). The nomogram model established in this study, which is based on independent risk factors identified through screening and regression analysis, can be used to predict the risk of secondary pulmonary fungal infection in patients with AECOPD. Internal validation demonstrated the consistent and robust performance of the nomogram. The model maintained high discriminatory ability in the validation set (AUC =0.80; 95% CI: 0.65–0.95), closely mirroring its performance in the development set (AUC =0.82; 95% CI: 0.75–0.88). Furthermore, the model exhibited good calibration in both cohorts, with nonsignificant Hosmer-Lemeshow test results (p=0.74 and p=0.40 for the validation and training sets, respectively). This consistency across the training and validation datasets underscores the model’s reliability and suggests that it is not overfitted to the development data.

There are limitations in this study. First, the single-center, retrospective design may restrict generalizability and introduce selection bias. Second, the relatively small sample size, particularly the limited number of patients with severe-to-very severe COPD (GOLD 3&4), may constrain the statistical power for robust subgroup analyses and could affect the stability of risk estimates specifically within this population. This limits the interpretability of the comparable infection rates across GOLD stages and indicates that our model’s performance in predicting risk for patients with advanced disease requires validation in larger, dedicated cohorts. Third, while internal validation was performed, the smaller size of the validation cohort resulted in wider confidence intervals for performance metrics. External validation in larger, prospective, multicenter studies is necessary to confirm the model’s utility. Finally, although missing data were minimal, the complete-case analysis assumes data were missing at random.

Conclusions

This study identified three readily obtainable clinical factors—recent systemic corticosteroid use, prior hospitalization for exacerbation, and prolonged antimicrobial therapy (≥14 days)—as independent predictors of secondary pulmonary fungal infection in patients with AECOPD. These factors were integrated into a nomogram that demonstrated consistent and robust performance upon internal validation, with high discrimination and good calibration. While the model’s generalizability may be limited by its single-center retrospective design, its strong internal validity supports its potential as a practical tool for early and individualized risk estimation. External validation in prospective, multicenter cohorts is warranted to confirm its clinical utility and broader impact.

Supplementary

The article’s supplementary files as

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was approved by the Medical Ethics Committee of The Fourth Affiliated Hospital of Soochow University (Suzhou Dushu Lake Hospital) (No. 251243). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Due to the retrospective nature of the study, the requirement for informed consent was waived by the Ethics Committee.

Data Sharing Statement

Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-aw-2006/dss

jtd-18-02-84-dss.pdf ^{(66.2KB, pdf)}

DOI: 10.21037/jtd-2025-aw-2006