Construction and validation of a nomogram for predicting chronic obstructive pulmonary disease with bronchiectasis

Introduction

Chronic obstructive pulmonary disease (COPD) is a prevalent chronic respiratory condition defined by persistent airflow limitation, chronic respiratory symptoms, and progressive deterioration in lung function. It primarily arises from exposure to harmful airborne substances, especially cigarette smoke and environmental pollutants. COPD ranks among the major contributors to worldwide mortality and disability (1). Bronchiectasis (BE), defined as an irreversible bronchial dilation, is clinically characterized by chronic productive cough, copious sputum production, recurrent respiratory infections, and imaging manifestations such as bronchial thickening and expansion (2).

BE significantly influences COPD progression and prognosis and is recognized as an important COPD-related comorbidity (3). Previous research has reported varying COPD combined with BE (COPD-BE) prevalence rates, ranging widely between 4.0% and 75.0% (4). The pathogenesis of COPD-BE remains unclear. Compared with COPD alone, COPD-BE has higher exacerbation risk (5), increased mortality (6), poorer prognosis (7), and elevated hospitalization rates and healthcare costs (8). Furthermore, studies indicate that as COPD progresses, patients face an increased risk and severity of developing bronchiectasis, which further exacerbates lung function impairment and elevates mortality rates (9). In recent years, a systematic review has further clarified the clinical risk factors (RFs) closely associated with COPD-BE, such as history of pulmonary tuberculosis, Pseudomonas aeruginosa (P. aeruginosa) infection, and severe airflow limitation (10). This underscores the urgency of early identification of COPD-BE and intervention targeting its RFs. Therefore, this research aimed to identify independent risk determinants for COPD-BE, develop and validate a robust prediction model, and provide clinicians with an evidence-based instrument for early diagnosis and tailored interventions. We present this article in accordance with the TRIPOD reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1286/rc).

Methods

Study design and patient inclusion and exclusion criteria

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This retrospective cohort study was approved by the Ethics Committee of Wuhan Third Hospital (No. KY2025-043). Given the retrospective study design, obtaining informed consent from participants was deemed unnecessary. Clinical information was retrospectively retrieved via electronic medical record review for COPD patients hospitalized at Wuhan Third Hospital from November 2022 to March 2025. Based on BE status, participants were categorized into two groups: COPD alone and COPD-BE.

Inclusion criteria: (I) COPD diagnosed according to the 2024 GOLD guidelines (11). (II) BE diagnosed following the criteria and definitions for the radiological and clinical diagnosis of bronchiectasis in adults for use in clinical trials: International consensus recommendations (12). Exclusion criteria: (I) concurrent other pulmonary diseases, including bronchial asthma, active pulmonary tuberculosis, pulmonary tumors, interstitial lung disease, lung abscess, and pneumothorax; (II) diseases significantly influencing lung function tests; (III) current immunosuppressive therapy; (IV) severe dysfunction or failure of vital organs; and (V) incomplete clinical data, such as pulmonary function tests, chest CT scans, and sputum cultures, were excluded if any of these results were missing. The total number of cases was 3,692. Exclusions based on incomplete clinical data totaled 2,113 individuals, while other exclusion criteria led to 1,197 exclusions, resulting in a total of 3,310 excluded cases. The remaining 382 cases met the inclusion criteria. Eligible patients were randomly assigned to training (n=268) and validation (n=114) sets at a 7:3 ratio (Figure 1).

Figure 1 Flowchart of participant selection. BE, bronchiectasis; COPD, chronic obstructive pulmonary disease.

Data acquisition and related definitions

Medical records were obtained from the electronic medical record system. Collected data included general information, clinical symptoms, medical history, blood test related indicators, treatment status, etc. Measurements for arterial blood gas analysis, complete blood count, and sputum culture were obtained during the hospital admission for the acute exacerbation. Data unavailable in the electronic records were retrieved from paper medical records.

Diagnostic criteria for COPD: persistent airflow limitation with a forced expiratory volume in one second (FEV₁) as a percentage of forced vital capacity (FEV₁/FVC%) <70% after bronchodilator inhalation; predicted forced expiratory volume in one second (FEV₁%) <80% (11). Diagnostic criteria for bronchiectasis: clinical presentation of chronic cough with purulent sputum production and history of acute exacerbations; radiographic demonstration of bronchial diameter/adjacent pulmonary artery diameter (B/A) ≥1.0, absence of progressive narrowing of airways, and visible peripheral airways (12). Pulmonary function testing performed using: German Jaeger MasterScreen. Definition of hemoptysis: bleeding originating from the lower respiratory tract (below the vocal cords, including the trachea, bronchi, and lung parenchyma), presenting as sputum containing blood or pure blood, excluding bleeding from the upper respiratory tract or gastrointestinal tract. Definition of P. aeruginosa episodes: as sputum culture-confirmed P. aeruginosa infection occurring multiple times during a single hospitalization or across multiple hospitalizations.

Statistical analysis

Statistical analyses and data visualization were performed using SPSS (version 27.0) and R (version 4.5.0). A two-tailed P value threshold of <0.05 was set for statistical significance. Continuous variables were first assessed for normality using the Shapiro-Wilk test. Those conforming to a normal distribution were reported as mean ± standard deviation (SD) and compared using independent-samples t-tests. Non-normally distributed variables are presented as median with interquartile range (IQR) and analyzed with the Wilcoxon rank-sum test. Categorical variables are expressed as counts and percentages. Unordered categorical variables were assessed using χ² tests or Fisher’s exact tests, as appropriate. For ordered categorical variables, the Wilcoxon rank-sum test was employed.

Patients were grouped according to their BE status, which served as the dependent outcome. Predictive factors preliminarily identified through univariate analysis (with a significance threshold of P<0.05) were subsequently subjected to Benjamini-Hochberg (BH) false discovery rate (FDR) correction to control the risk of false positives resulting from multiple comparisons. Factors that remained statistically significant after FDR correction (adjusted P<0.05) were further screened using least absolute shrinkage and selection operator (LASSO) regression. To simplify and optimize the regression model, the regularization parameter (λ) was manually adjusted to 0.1, exceeding the default λ.1se (standard deviation), thus effectively eliminating insignificant variables. Factors selected through LASSO were subsequently subjected to multivariate logistic regression modeling. Finally, a nomogram based on these significant variables was constructed, and its predictive efficacy and clinical value were assessed by evaluating the areas under the curve (AUC), calibration curve alignment, and decision curve analysis (DCA) in both training and validation cohorts.

Results

Clinical characteristics

Comparison of clinical attributes among the 382 patients in the training and validation cohorts revealed no significant differences (P>0.05, Table 1).

Analysis of RFs for COPD-BE

Univariate logistic regression analysis initially pinpointed potential predictors (Table 2), and variables demonstrating statistical significance (P<0.05) underwent further refinement by LASSO regression. Six factors emerged with nonzero regression coefficients (Figure 2): female sex, hemoptysis, previous pulmonary tuberculosis, P. aeruginosa infection, globulin levels, and mechanical ventilation duration. These factors were subsequently confirmed as independent predictors of COPD-BE through multivariate regression analysis (Table 3).

Figure 2 LASSO regression analysis screening variables. (A) LASSO coefficient path diagram (a total of 23 variables that were significant in the one-way logistic regression were included in the analysis); (B) LASSO regularization path diagram, when Lambda =0.1, a total of 6 variables were selected. LASSO, least absolute shrinkage and selection operator.

Construction, validation, and clinical application of nomogram prediction models

Based on multivariate regression results, a nomogram predicting COPD-BE was constructed (Figure 3). Independent RFs (female sex, history of pulmonary tuberculosis, hemoptysis, P. aeruginosa infection, globulin levels, and mechanical ventilation duration) were included in the model. To estimate the probability of bronchiectasis occurring concurrently, the scores for each selected factor from the nomogram were combined into an overall sum. Receiver operating characteristic (ROC) curves were utilized to evaluate the accuracy of prediction provided by this nomogram model in both validation and training datasets (Figure 4). The AUC was calculated as 0.840 [95% confidence interval (CI): 0.790–0.890] for the training cohort and 0.829 [95% CI: 0.749–0.909] for the validation cohort. Additionally, calibration assessments (Figure 5) indicated a good match between observed and predicted probabilities (P>0.05). The DCA presented in (Figure 6) further confirmed the clinical effectiveness and utility of the developed nomogram, demonstrating substantial potential to guide clinical decision-making.

Figure 3 Nomogram predicting COPD-BE probability. Pseudomonas aeruginosa infection corresponds to 45 points, history of pulmonary tuberculosis to 50 points, hemoptysis to 53 points, and female sex to 28 points. Points for globulin and mechanical ventilation duration vary according to actual measured values. COPD-BE, chronic obstructive pulmonary disease combined with bronchiectasis.

Figure 4 ROC curves for COPD-BE prediction using the nomogram. The AUC was 0.840 (95% CI: 0.790–0.890) in the training set and 0.829 (95% CI: 0.749–0.909) in the validation set. AUC, area under the curve; CI, confidence interval; COPD-BE, chronic obstructive pulmonary disease combined with bronchiectasis; ROC, receiver operating characteristic.

Figure 5 Calibration curves for nomogram-predicted COPD-BE probability. P=0.63 (training set) and P=0.61 (validation set). COPD-BE, chronic obstructive pulmonary disease combined with bronchiectasis.

Figure 6 DCA of the nomogram for predicting COPD-BE in the training and validation sets. The training and test models are plotted based on data from the training set and validation set, respectively. COPD-BE, chronic obstructive pulmonary disease combined with bronchiectasis; DCA, decision curve analysis.

Discussion

The diagnosis of COPD is based on persistent airflow limitation (1), while BE is diagnosed through computed tomography (CT) imaging (12). It has been suggested that COPD and BE coexist as COPD-BE overlap syndrome (BCOS) (13). The pathogenesis of BCOS remains unclear but may involve oxidative stress from inflammation, immune dysregulation due to microbial colonization, and α1-antitrypsin imbalance (14). Studies have indicated that patients diagnosed with COPD-BE experience worse pulmonary function, heightened dyspnea, diminished life quality, reduced physical activity tolerance, and impaired psychological well-being compared to those diagnosed solely with COPD (15-17). Moreover, patients with COPD-BE face significantly increased risks of acute exacerbations (3.88-fold), acute respiratory failure (1.74-fold), pneumonia (2.20-fold), and cardiac arrest (1.99-fold) compared to their COPD-only counterparts (18). Consequently, COPD-BE patients have worse prognoses and higher mortality risks (7). Crucially, a multicenter longitudinal study provides crucial prognostic evidence: it confirms that in COPD patients, the presence of structural lung diseases such as bronchiectasis is closely associated with accelerated lung function decline (19). This finding profoundly illuminates the pathophysiological basis for the poor prognosis in COPD-BE patients. COPD treatment primarily relies on long-acting bronchodilators, whereas COPD-BE requires additional interventions. Treatment strategies differ significantly regarding antibiotics, inhaled antibiotics, glucocorticoids, and other medications (7). Lee’s research team directly compared the efficacy of different inhaled combination therapies in patients with bronchial dilation and airflow limitation. Their findings indicate that treatment outcomes correlate with blood eosinophil count (BEC). ICS-containing regimens reduce the risk of exacerbations in patients with elevated blood eosinophil counts (BEC ≥300/µL), while potentially accelerating lung function decline in those with low counts (BEC <200/µL). This further confirms that therapeutic strategies can significantly modulate clinical outcomes in this population (20). Therefore, this research aimed to identify factors independently associated with COPD-BE and to establish a practical clinical nomogram to facilitate early identification of the presence of BE in patients with COPD. The model’s application may help clinicians deliver timely interventions, decrease mortality, and improve overall patient prognosis.

P. aeruginosa infection as an independent RF for COPD-BE

Multivariate regression analysis indicated that P. aeruginosa infection was an independent RF for COPD-BE (OR =9.275), consistent with previous research results (9). P. aeruginosa promotes neutrophil infiltration and macrophage activation. Neutrophils release myeloperoxidase (MPO) and elastase, damaging airway elastic fibers and collagen, exacerbating structural airway damage and alveolar injury. Furthermore, activated fibroblasts and myofibroblasts increase collagen and extracellular matrix deposition, thickening airway walls, narrowing the lumen, and aggravating airflow limitation. Macrophages secrete matrix metalloproteinases, contributing to airway remodeling and promoting BE development (14,21-23). BE features irreversible airway dilation, wall thickening, and impaired ciliary function. COPD exacerbates airway obstruction through chronic inflammation and mucus hypersecretion, leading to mucus retention and hypoxic conditions favorable for colonization by pathogens such as P. aeruginosa. Additionally, P. aeruginosa can adhere to damaged airway epithelial cells, forming biofilms (alginate, etc.) that resist immune clearance and antibiotic treatment (24). Thus, an inflammation-injury cycle ensues. Studies have demonstrated the pivotal role of P. aeruginosa infection in COPD-BE pathogenesis, contributing to airway remodeling, systemic inflammation, decreased lung function, and significantly increased risk of death (25,26). Furthermore, evidence indicates that P. aeruginosa colonization is not only a marker of disease severity but is also independently associated with accelerated disease progression and poorer prognosis in COPD-BE patients (27). This underscores the need for vigilant microbiological surveillance and targeted antimicrobial strategies in this high-risk phenotype.

History of pulmonary tuberculosis as an independent RF for COPD-BE

Pulmonary tuberculosis, primarily caused by Mycobacterium tuberculosis, constitutes the predominant tuberculosis form. COPD patients previously infected with pulmonary tuberculosis show a higher prevalence of bronchiectasis compared to COPD patients without such a medical history. Furthermore, these individuals commonly experience prolonged and intensified dyspnea, frequent acute exacerbation episodes, and increased positivity rates for P. aeruginosa cultures (28). Mycobacterium tuberculosis infection triggers granuloma formation, fibrosis, and cavitation. These processes damage lung parenchyma and airway structures, leading to BE, small airway stenosis, and emphysema. Additionally, immune imbalances, particularly T helper cell 1 (Th1)/Th2 dysregulation, exacerbate fibrosis. Smoking and other factors impair immune cells, such as macrophages and natural killer (NK) cells, prolonging chronic inflammation and contributing to COPD-BE formation (29). Consistent with findings from earlier studies (30), the present research identified previous pulmonary tuberculosis infection as a crucial independent RF for the occurrence of COPD-BE. Thus, it is recommended that clinicians carefully monitor COPD patients who have experienced tuberculosis infection previously, initiating timely therapeutic strategies to reduce potential negative outcomes.

Hemoptysis as an independent RF for COPD-BE

Hemoptysis, a common symptom in BE, correlates closely with airway structural damage and abnormal angiogenesis. Repeated airway infections in BE cause bronchial wall damage and dilation, leading to compensatory hyperplasia and tortuosity of bronchial arteries. Chronic inflammation promotes anomalous anastomotic branches and abundant collateral circulation. These new blood vessels, lacking smooth muscle and elastic fiber support, rupture easily under pressure changes or inflammation, triggering hemoptysis (31). Thus, clinicians should consider COPD-BE in COPD patients presenting with hemoptysis, particularly if recurrent or massive.

Female sex as an independent RFs for COPD-BE

In our study, female sex is an independent RF for COPD-BE, with a 4.056-fold higher risk compared to males. This difference may relate to anatomical and physiological airway differences and hormonal influences. Women generally have smaller airways, a higher density of goblet cells, greater mucus secretion, and hormone-regulated ciliary function, leading to impaired mucus clearance. Estrogen also promotes mucus secretion (MUC5B gene expression), inhibits immune responses (interleukin-8 release), and facilitates P. aeruginosa biofilm formation, accelerating lung function decline (32,33). However, previous studies identified male sex as a RF (30). These discrepancies may stem from ethnic, geographic, and environmental exposure variations. Additionally, men’s higher prevalence of smoking and other harmful behaviors, versus hormonal and anatomical susceptibility in women, may explain these differences. Future multicenter studies with larger samples are needed to explore this issue further.

Globulin levels as an independent RF for COPD-BE

Globulins are functionally diverse proteins, including immunoglobulins and non-immunoglobulins, crucial for immunity, metabolism, and defense. This study showed significantly higher globulin levels in COPD-BE patients compared to COPD patients, establishing globulin as an independent RF for COPD-BE. Elevated globulin may reflect chronic airway infection-driven immune activation, inflammation, and mucosal repair. Chronic antigenic stimulation continuously activates B lymphocytes and plasma cells, elevating immunoglobulin production. Additionally, airway epithelial injury and chronic infection (e.g., via IL-17 pathways) increase immunoglobulin A (IgA) secretion and enhance polymeric immunoglobulin receptor (pIgR) expression, promoting immunoglobulin transport and inflammatory repair (34). Notably, P. aeruginosa infection significantly elevates IgG, correlating with exacerbation frequency and BE severity (35). Jin et al. (30) also reported elevated IgE levels correlating positively with BE severity. Thus, increased globulin levels serve as a marker of airway inflammation and immune activation in COPD-BE patients (except BE due to immunodeficiency). Monitoring the levels of globulin may provide a basis for early identification of COPD-BE, early warning of infection and immune-targeted therapy.

Mechanical ventilation duration as an independent RF for COPD-BE

The current study also revealed that COPD-BE patients had notably prolonged mechanical ventilation durations compared with those having only COPD. This difference may stem from more severe clinical presentations, worse pulmonary function, complicated infections, and longer hospital admissions typically observed in COPD-BE patients (8,31). From a pathophysiological perspective, in addition to the aforementioned factors, particular attention should be paid to the impact of ventilator-associated mucus clearance impairment on disease progression (36). Additionally, COPD patients are particularly susceptible to ventilator-induced lung injury due to pre-existing chronic inflammation, heterogeneous emphysematous destruction, and abnormal mechanical properties. Prolonged exposure to excessive tidal volumes, high driving pressures, or inadequate positive end-expiratory pressure induces mechanical stress through alveolar overdistension (volume injury) and cyclic airway opening/closing (collapse injury), thereby activating intense local inflammatory responses (biological injury). This response disrupts the alveolar-capillary barrier and initiates a pro-fibrotic process. Ultimately, fibrous scar tissue deposits and contracts, exerting persistent radial traction on adjacent bronchioles. This leads to mechanical dilation and distortion of the airways, resulting in irreversible structural damage (37,38). Therefore, when COPD patients use mechanical ventilation duration is prolonged, the occurrence of COPD-BE needs to be considered, and the clinic needs to comprehensively consider the patient’s situation while improving the patient’s ventilation to take off the machine as early as possible, reduce the infection and mechanical damage caused by mechanical ventilation, and reduce the risk of BE and other complications through individualized ventilation strategies and refined management.

Nomogram prediction model

The nomogram serves as a practical tool for translating multivariate models into clinically accessible formats. In our internal validation, the proposed nomogram demonstrated favorable predictive performance, with AUCs of 0.840 in the training set and 0.829 in the validation set. The calibration curves suggested good agreement between predictions and observations, and the DCA indicated potential clinical utility across a range of threshold probabilities. More importantly, this model facilitates a shift from qualitative judgments based on “single warning signs” to quantitative assessments integrating multiple factors. It provides clinicians with a standardized risk identification method, enabling more efficient screening of high-risk COPD-BE patients under resource-constrained conditions. This approach helps avoid unnecessary imaging examinations while enhancing the efficiency of early identification and management.

There are several limitations in this study. First, its retrospective design resulted in the exclusion of a substantial number of patients due to missing key clinical data, including pulmonary function tests, chest CT scans, and sputum cultures. This issue, combined with the inability of some severely ill patients to complete pulmonary function tests, may have introduced selection bias, potentially limiting the generalizability of our nomogram. Second, we could not systematically adjust for specific treatments, which are influence airway microbiology and disease progression. Future research should systematically incorporate treatment variables to better understand their impact on disease trajectories and model performance. Third, due to data limitations, we were unable to employ specific immunological criteria for systematic screening of conditions such as allergic bronchopulmonary aspergillosis to assess the influence of allergic factors, nor could we integrate a multidimensional bronchial dilation scoring system to further enhance overall clinical utility and accuracy.

Future prospective studies incorporating detailed treatment records, specific immunoglobulin testing, and comprehensive severity scoring are needed to validate and refine our model. To this end, we plan to initiate a multi-regional, multi-center study to further optimize the variable weights in the nomogram and attempt to establish a simplified risk scoring system to validate its clinical feasibility. This initiative will achieve external validation and iterative refinement of the predictive model by integrating these multidimensional assessments. The ultimate goal is to establish a comprehensive management pathway from initial suspicion to precise risk stratification.

Conclusions

Female sex, history of pulmonary tuberculosis, hemoptysis, P. aeruginosa infection, globulin levels, and mechanical ventilation duration were identified as independent RFs for COPD-BE. The nomogram constructed in this study demonstrated promising diagnostic capability and potential clinical utility upon internal validation. Pending future validation, this model could serve as a potential tool to aid in identifying BE in COPD patients, which may help guide early targeted interventions with the ultimate goal of improving patient prognosis.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1286/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1286/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1286/prf

Funding: This study was supported by the Development Center for Medical Science & Technology, National Health Commission of the People’s Republic of China (No. WKZX2024HK0123).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1286/coif). The authors have no conflicts of interest to declare.

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. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Wuhan Third Hospital (No. KY2025-043), and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Christenson SA, Smith BM, Bafadhel M, et al. Chronic obstructive pulmonary disease. Lancet 2022;399:2227-42. [Crossref] [PubMed]
2. Choi H, McShane PJ, Aliberti S, et al. Bronchiectasis management in adults: state of the art and future directions. Eur Respir J 2024;63:2400518. [Crossref] [PubMed]
3. Agustí A, Celli BR, Criner GJ, et al. Global Initiative for Chronic Obstructive Lung Disease 2023 Report: GOLD Executive Summary. Eur Respir J 2023;61:2300239. [Crossref] [PubMed]
4. Traversi L, Miravitlles M, Martinez-Garcia MA, et al. ROSE: radiology, obstruction, symptoms and exposure - a Delphi consensus definition of the association of COPD and bronchiectasis by the EMBARC Airways Working Group. ERJ Open Res 2021;7:00399-2021. [Crossref] [PubMed]
5. Shi L, Wei F, Ma T, et al. Impact of Radiographic Bronchiectasis in COPD. Respir Care 2020;65:1561-73. [Crossref] [PubMed]
6. Polverino E, De Soyza A, Dimakou K, et al. The Association between Bronchiectasis and Chronic Obstructive Pulmonary Disease: Data from the European Bronchiectasis Registry (EMBARC). Am J Respir Crit Care Med 2024;210:119-27. [Crossref] [PubMed]
7. Polverino E, Dimakou K, Hurst J, et al. The overlap between bronchiectasis and chronic airway diseases: state of the art and future directions. Eur Respir J 2018;52:1800328. [Crossref] [PubMed]
8. Kim Y, Kim K, Rhee CK, et al. Increased hospitalizations and economic burden in COPD with bronchiectasis: a nationwide representative study. Sci Rep 2022;12:3829. [Crossref] [PubMed]
9. Martínez-García MÁ, de la Rosa-Carrillo D, Soler-Cataluña JJ, et al. Bronchial Infection and Temporal Evolution of Bronchiectasis in Patients With Chronic Obstructive Pulmonary Disease. Clin Infect Dis 2021;72:403-10. [Crossref] [PubMed]
10. Zhang X, Pang L, Lv X, et al. Risk factors for bronchiectasis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis. Clinics (Sao Paulo) 2021;76:e2420. [Crossref] [PubMed]
11. Venkatesan P. GOLD COPD report: 2024 update. Lancet Respir Med 2024;12:15-6. [Crossref] [PubMed]
12. Aliberti S, Goeminne PC, O'Donnell AE, et al. Criteria and definitions for the radiological and clinical diagnosis of bronchiectasis in adults for use in clinical trials: international consensus recommendations. Lancet Respir Med 2022;10:298-306. [Crossref] [PubMed]
13. Hurst JR, Elborn JS, De Soyza A, et al. COPD-bronchiectasis overlap syndrome. Eur Respir J 2015;45:310-3. [Crossref] [PubMed]
14. Alam MA, Mangapuram P, Fredrick FC, et al. Bronchiectasis-COPD Overlap Syndrome: A Comprehensive Review of its Pathophysiology and Potential Cardiovascular Implications. Ther Adv Pulm Crit Care Med 2024;19:29768675241300808. [Crossref] [PubMed]
15. Sahin H, Naz I, Susam S, et al. The effect of the presence and severity of bronchiectasis on the respiratory functions, exercise capacity, dyspnea perception, and quality of life in patients with chronic obstructive pulmonary disease. Ann Thorac Med 2020;15:26-32. [Crossref] [PubMed]
16. Kim SH, Kim C, Jeong I, et al. Chronic Obstructive Pulmonary Disease Is Associated With Decreased Quality of Life in Bronchiectasis Patients: Findings From the KMBARC Registry. Front Med (Lausanne) 2021;8:722124. [Crossref] [PubMed]
17. Sobala R, De Soyza A. Bronchiectasis and Chronic Obstructive Pulmonary Disease Overlap Syndrome. Clin Chest Med 2022;43:61-70. [Crossref] [PubMed]
18. Chung WS, Lin CL. Acute respiratory events in patients with bronchiectasis-COPD overlap syndrome: A population-based cohort study. Respir Med 2018;140:6-10. [Crossref] [PubMed]
19. Lee HW, Lee JK, Kim Y, et al. Differential decline of lung function in COPD patients according to structural abnormality in chest CT. Heliyon 2024;10:e27683. [Crossref] [PubMed]
20. Lee HJ, Lee JK, Park TY, et al. Clinical outcomes of long-term inhaled combination therapies in patients with bronchiectasis and airflow obstruction. BMC Pulm Med 2024;24:49. [Crossref] [PubMed]
21. Qi Y, Yan Y, Tang D, et al. Inflammatory and Immune Mechanisms in COPD: Current Status and Therapeutic Prospects. J Inflamm Res 2024;17:6603-18. [Crossref] [PubMed]
22. Vidaillac C, Chotirmall SH. Pseudomonas aeruginosa in bronchiectasis: infection, inflammation, and therapies. Expert Rev Respir Med 2021;15:649-62. [Crossref] [PubMed]
23. Perea L, Faner R, Chalmers JD, et al. Pathophysiology and genomics of bronchiectasis. Eur Respir Rev 2024;33:240055. [Crossref] [PubMed]
24. Augustyniak D, Olszak T, Drulis-Kawa Z. Outer Membrane Vesicles (OMVs) of Pseudomonas aeruginosa Provide Passive Resistance but Not Sensitization to LPS-Specific Phages. Viruses 2022;14:121. [Crossref] [PubMed]
25. Dai Z, Zhong Y, Cui Y, et al. Analysis of clinical characteristics, prognosis and influencing factors in patients with bronchiectasis-chronic obstructive pulmonary disease overlap syndrome: A prospective study for more than five years. J Glob Health 2024;14:04129. [Crossref] [PubMed]
26. Kobayashi S, Yamada M, Ishida M, et al. Bronchiectasis in Japanese patients with chronic obstructive pulmonary disease: A prospective cohort study. Respir Investig 2025;63:942-8. [Crossref] [PubMed]
27. Huang JT, Cant E, Keir HR, et al. Endotyping Chronic Obstructive Pulmonary Disease, Bronchiectasis, and the "Chronic Obstructive Pulmonary Disease-Bronchiectasis Association". Am J Respir Crit Care Med 2022;206:417-26. [Crossref] [PubMed]
28. Seo H, Sim YS, Min KH, et al. The Relationship Between Comorbidities and Microbiologic Findings in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease. Int J Chron Obstruct Pulmon Dis 2022;17:855-67. [Crossref] [PubMed]
29. Gai X, Allwood B, Sun Y. Post-tuberculosis lung disease and chronic obstructive pulmonary disease. Chin Med J (Engl) 2023;136:1923-8. [Crossref] [PubMed]
30. Jin J, Yu W, Li S, et al. Factors associated with bronchiectasis in patients with moderate-severe chronic obstructive pulmonary disease. Medicine (Baltimore) 2016;95:e4219. [Crossref] [PubMed]
31. Lu GD, Yan HT, Zhang JX, et al. Bronchial artery embolization for the management of frequent hemoptysis caused by bronchiectasis. BMC Pulm Med 2022;22:394. [Crossref] [PubMed]
32. Silveyra P, Babayev M, Ekpruke CD. Sex, hormones, and lung health. Physiol Rev 2026;106:53-86. [Crossref] [PubMed]
33. Somayaji R, Chalmers JD. Just breathe: a review of sex and gender in chronic lung disease. Eur Respir Rev 2022;31:210111. [Crossref] [PubMed]
34. de Fays C, Carlier FM, Gohy S, et al. Secretory Immunoglobulin A Immunity in Chronic Obstructive Respiratory Diseases. Cells 2022;11:1324. [Crossref] [PubMed]
35. Suarez-Cuartin G, Smith A, Abo-Leyah H, et al. Anti-Pseudomonas aeruginosa IgG antibodies and chronic airway infection in bronchiectasis. Respir Med 2017;128:1-6. [Crossref] [PubMed]
36. Goetz RL, Vijaykumar K, Solomon GM. Mucus Clearance Strategies in Mechanically Ventilated Patients. Front Physiol 2022;13:834716. [Crossref] [PubMed]
37. Silva PL, Scharffenberg M, Rocco PRM. Understanding the mechanisms of ventilator-induced lung injury using animal models. Intensive Care Med Exp 2023;11:82. [Crossref] [PubMed]
38. Hata A, Hino T, Putman RK, et al. Traction Bronchiectasis/Bronchiolectasis on CT Scans in Relationship to Clinical Outcomes and Mortality: The COPDGene Study. Radiology 2022;304:694-701. [Crossref] [PubMed]