Intensive versus usual rehabilitation programs in stable chronic obstructive pulmonary disease (COPD) patients.

Achieving long-term behavioural change in chronic disease management, particularly in chronic obstructive pulmonary disease (COPD), remains a significant challenge. Although maintenance programmes have been developed to extend the benefits of pulmonary rehabilitation, patient adherence is often comperomised by persistent symptoms, low motivation, and fragmented care. Research highlights the importance of therapeutic alliance, social support, and personalised follow-up to encourage long-term healthy behaviour. Care managers (CMs) may help facilitate these key elements by providing individualised support and coordination. The aim of this study is to identify key elements that support the sustainable implementation of the CM role in chronic care pathways, by exploring the shared experiences of COPD patients and CM involved in an 18-month remote follow-up post-rehabilitation programme. A qualitative descriptive study was conducted using semi-structured interviews with COPD patients and care managers who participated in the INSPIR'ACTION national experiment. This programme included an initial pulmonary rehabilitation phase followed by an 18-month remote follow-up. Interviews focused on the follow-up phase and were analysed using inductive thematic analysis. Data saturation was reached with a final sample of 9 patients and 7 CMs. Patients described CMs as supportive professionals who helped sustain motivation, adherence to healthy behaviours, and continuity of care. The relationship was perceived as trustful and personalised, even in a remote format. CMs expressed pride in their role but also reported organisational challenges, including lack of recognition and insufficient time allocation. Both groups emphasised the importance of relational continuity and individualised support. Patients and care managers described the CM as a key supportive figure offering personalised follow-up perceived as helping promote therapeutic engagement and behavioural change. Relational continuity throughout the remote follow-up was seen as fostering a trusting relationship that shaped participants' experience of the programme. By highlighting organisational elements that could influence implementation, our study may help inform future strategies to enhance the sustainable integration of care management in COPD care pathway. COPD patients and CMs involved in the INSPIR'ACTION programme shared their experiences through interviews, helping to identify key factors for improving care manager support and long-term follow-up. Their input directly informed the study's findings and recommendations.

To evaluate the effects of tiotropium/olodaterol (T/O) on phase-resolved functional lung (PREFUL) MRI parameters in hyperinflated chronic obstructive pulmonary disease (COPD) patients and examine correlations with conventional cardiopulmonary and hyperpolarized 129Xe MRI measures. Retrospective subanalysis of a prospective, randomized, placebo-controlled, crossover trial with open-label extension. Thirty-two patients with moderate-to-severe COPD (61.5 ± 7.7 years; 17 men); 30 completed the MRI extension at 1.5 T. PREFUL analysis yielded regional ventilation (RVent), flow-volume loop correlation metric (FVL-CM), normalized perfusion (QN), ventilation defect percentage (VDP), perfusion defect percentage (QDP), V/Q match metrics (VQM), and pulmonary pulse wave velocity (PWV; post-hoc parameter). Linear mixed-effects models tested treatment effects; correlations were evaluated with Spearman's rank and bootstrap 95% confidence intervals (95% CIs). PREFUL parameters improved after T/O single dose (SD) versus placebo, including improvements in FVL-CM by 4.1 percentage points (pp; 95% CI: 1.0 to 7.3 pp) and QN by 0.4 pp (95% CI: 0.2 to 0.6 pp) and reductions in VDP and QDP, with parallel gains in VQM(Non-Defect) (p < 0.05). PWV decreased after multiple doses (-0.87 m/s, 95% CI: -1.26 to -0.48 m/s). PREFUL MRI baseline values showed significant correlations with pulmonary function tests, cardiac, dynamic contrast-enhanced and 129Xe MRI. SD treatment-induced absolute changes in VDP(FVL-CM) correlated with reductions in residual volume (ρ = 0.41, 95% CI: 0.02 to 0.64). Further correlations were observed between PREFUL MRI and ¹²⁹Xe-derived VDP, apparent diffusion coefficient, and compartment ratios. PREFUL MRI sensitively captured immediate SD T/O-induced improvements in V/Q parameters and dose-dependent PWV responses after sustained bronchodilation. Question Can phase-resolved functional lung (PREFUL) MRI sensitively capture immediate single-dose and sustain multi-dose effects of tiotropium/olodaterol on ventilation-perfusion and vascular function in COPD patients? Findings Tiotropium/olodaterol improved PREFUL MRI-derived ventilation, perfusion, and V/Q matching parameters after a single dose, with sustained pulmonary vascular improvements after repeated dosing. Clinical relevance PREFUL MRI detected immediate and sustained functional improvements after tiotropium/olodaterol and showed significant correlations with cardiopulmonary tests and hyperpolarized ¹²⁹Xe MRI, supporting its role as a sensitive, radiation-free tool for monitoring COPD treatment response.

The role of statins in reduction of acute exacerbations in chronic obstructive pulmonary disease (COPD) remains unclear. This systematic review and meta-analysis aimed to determine the preventive effect of statins on exacerbations in COPD patients. A systematic search was conducted in PubMed, Embase, Cochrane Library, and clinicaltrials.gov, without language restrictions. Randomized controlled trials (RCTs) on treatment of COPD with statins, compared with placebo, were reviewed. Estimated effects of included studies were pooled as risk ratios (RRs) and weighted mean differences (WMDs), with 95% confidence intervals (CIs). The certainty of evidence was assessed using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) framework. Three RCTs (enrolling 1190 patients) met the inclusion criteria. Compared with placebo, the exacerbation related hospitalization rate (RR 0.87, 95% CI 0.77 to 0.97; GRADE = low) and severe exacerbation rate (RR 0.88, 95% CI 0.78 to 0.99; low) were significantly lower in statins groups. There was no statistically significant difference in moderate exacerbation rate (RR 0.71, 95% CI 0.48 to 1.04; very low), annualized exacerbation rate (WMD -0.21 per person-year, 95% CI -0.61 to 0.20; very low), and annualized moderate-to-severe exacerbation rate (WMD -0.21 per person-year, 95% CI -0.57 to 0.15; very low) between the two groups. In the absence of single-inhaler triple therapy (SITT), statins were associated with a reduction in severe exacerbations and hospitalization rates of COPD. Given the limitations of the evidence we found, further large-scale, high-quality studies are needed to clarify the impact of statins on prognosis and the beneficiary population. PROSPERO CRD42025644971.

This study aimed to evaluate changes in the oral, nasal, pulmonary, and gut microbiota in patients with chronic obstructive pulmonary disease (COPD) and to explore their interrelationships compared with the healthy group. This study included 33 COPD patients and 29 healthy individuals. A total of 162 oral, nasal, sputum, and fecal samples were obtained. The microbiota was determined using full-length 16S rRNA gene sequencing on the PacBio platform. Alpha diversity was significantly reduced in sputum and fecal samples of COPD patients, while oral and nasal microbiota showed no significant differences. Beta diversity revealed substantial overlap between oral and sputum microbiota in both groups, while nasal and fecal communities were clearly distinct. Linear discriminant analysis effect size analysis identified Haemophilus parahaemolyticus as a sputum biomarker. Source tracking confirmed that the majority of lung microbiota originated from the oral cavity. Interleukin-6 was inversely correlated with short-chain fatty acids (SCFAs)-producing microbiota in fecal samples, suggesting that depletion of these bacteria may contribute to systemic inflammation. Co-occurrence network analysis revealed that the sputum microbial network in COPD patients exhibited reduced robustness and lacked prominent hub nodes. Lung microbiota largely originates from the oral cavity but is changed in COPD. The lung microbiome is still more sensitive and accurate than the oral, nasal, and fecal microbiomes for COPD diagnosis. Fragmented networks in COPD indicate reduced community resilience.IMPORTANCELung and gut microbial diversity is significantly reduced in COPD patients. Oral microbiota is the primary source of lung microbes, but poorly predicts COPD status. Haemophilus parahaemolyticus was identified as a novel sputum biomarker in COPD. The bacterial network in COPD lungs is fragmented, lacking the resilience seen in healthy individuals.

To compare outcomes of single(SLT) versus bilateral(BLT) lung transplantation among dually listed recipients hypothetically eligible for either treatment. Considerable debate remains regarding the added benefit of BLT versus SLT. Prior analyses were confounded by significant selection bias or limited in generalizability to the contemporary era; a randomized trial remains unlikely due to ethical concerns. Within the Organ Procurement and Transplantation Network, we considered all adults who were dually listed for SLT and BLT and underwent first-time lung allograft transplantation for idiopathic pulmonary fibrosis(IPF) or chronic obstructive pulmonary disease(COPD) in the US between 2017-2024. We applied target trial emulation and clone-censor weight methodology to emulate a prospective randomized trial comparing SLT versus BLT. We tabulated 3,274 dually listed lung allograft recipients, of whom 1,026 were transplanted for COPD and 2,248 for IPF. Median follow-up was 24.5 months. COPD patient survival at five years was superior following BLT (61%[55-66%]) versus SLT (52%[46-58%, P=0.009). Meanwhile, among IPF patients, five year survival was 59%[54-63%] following BLT versus 56%[51-61%], P=0.009) after SLT. In a target trial emulation, among COPD patients, BLT remained associated with reduced mortality hazard (HR 0.67, CI 0.50-0.88). However, stratifying by age, SLT and BLT yielded comparable outcomes among patients ≥70years (HR 0.75, Credible Interval 0.38-1.37). Evaluating IPF recipients, after adjustment, BLT remained associated with comparable mortality hazard (HR 1.04, CI 0.85-1.26). No age-related effect was noted. Our findings suggest SLT could yield acceptable post-transplantation survival in select patients, while expanding access to this invaluable resource.

Chronic obstructive pulmonary disease (COPD) is characterized by global immune dysregulation. The most abundant immune cells in the distal airways and alveolar compartments are macrophages and lymphocytes. These cells have been poorly characterized in human airways due to their plasticity and numerous subtypes. There is a marked scarcity of information in COPD airway, limiting our understanding of the inflammatory process that drives COPD progression. The primary aim of this study was to characterize the differences in composition of the immune cells in the alveolar space of patients with COPD versus healthy controls and the relation with severity of airflow limitation at a single cell resolution. Individuals with and without COPD were recruited. All participants underwent bronchoscopy, where a single cell suspension was created from the retrieved bronchoalveolar lavage and then sequenced using single cell RNA sequencing technology. Full pulmonary function tests, thoracic computed tomography (CT) and xenon-129 hyperpolarized gas magnetic resonance imaging (MRI) were performed on participants for phenotyping. From 17 participants, a total of 97,254 cells were recovered with 52,840 cells belonging to the COPD group (N = 10). The COPD immune landscape was perturbed and characterized by an expansion of non-typable macrophages, increased neutrophils and classical monocytes, and significant changes in regulatory and double-negative T lymphocytes compared to controls (FDR < 0.05). The percentage of these cells were further altered with increasing GOLD (Global initiative for chronic Obstructive Lung Disease) severity stages. The percentage of double negative T cells in BAL was significantly related to airway luminal area on CT; whereas that of regulatory T cells was inversely related to MRI gas transfer and DLCO of COPD patients. The immune cell transcriptomic profile of COPD BAL reveals global immune dysregulation, with increased abundance of proinflammatory and profibrotic macrophages, monocytes and neutrophils, potentially driving airway remodeling. Regulatory T cells may play a significant role in gas exchange perturbations of COPD.

Long non-coding RNA (lncRNA), a key molecule within gene expression regulatory network, is important in various respiratory diseases. This study assessed the expression changes and regulatory mechanism of lncRNA FOXD2-AS1 in chronic obstructive pulmonary disease (COPD). Two hundred COPD patients were enrolled, comprising 100 with AECOPD and 100 with stable COPD. 16HBE cells were exposed to cigarette smoke extract (CSE) to simulate disease conditions in vitro. Relative mRNA levels were measured via RT-qPCR. Cell function, including cell proliferation, apoptosis and inflammation, was assessed. Target binding genes were predicted using online tools, with their functions annotated via GO and KEGG analysis. Both stable COPD patients and AECOPD patients had significantly higher serum FOXD2-AS1 levels than healthy controls, with the highest values in AECOPD. It can differentiate stable COPD from healthy controls and AECOPD, and increased gradually with COPD stage progression. Silencing FOXD2-AS1 afforded protection against apoptotic and inflammatory phenotype induced by CSE in 16HBE cells. FOXD2-AS1 sponges miR-185-5p, which were downregulated in clinical serum samples and cell models. miR-185-5p downregulation reversed the effects of FOXD2-AS1 silencing on cell apoptosis and inflammation. CDC42, a downstream target of miR-185-5p, was upregulated in COPD patients and cell models, and negatively related to serum miR-185-5p levels. CDC42 reversed the role of FOXD2-AS1/ miR-185-5p in CSE-exposed 16HBE cells. Serum FOXD2-AS1 was markedly upregulated in COPD patients, with close correlation to disease severity. The FOXD2-AS1/miR-185-5p/CDC42 axis may contribute to COPD pathogenesis by mediating bronchial epithelial cell apoptosis and inflammation.

Self-care is critical for improving outcomes and quality of life in patients with chronic obstructive pulmonary disease (COPD), yet substantial barriers remain. The Middle-Range Theory of Self-Care of Chronic Illness provides a framework for systematically examining these obstacles. To explore barriers to self-care among patients with COPD. A descriptive qualitative design was employed. Semi-structured interviews were conducted with COPD patients from a rehabilitation centre in China and analysed using deductive content analysis and reported in line with COREQ guidelines. Sixteen patients (mean age 71.4 years; 75% male) participated. Across 3 dimensions of self-care, 7 categories and 21 subcategories were identified. In maintenance, patients reported difficulties with lifestyle modification, physical activity, and medication adherence. In monitoring, challenges included limited symptom recognition and lack of access to monitoring devices. In management, low confidence in autonomous care and reluctance to consult providers were major barriers. Nurses are encouraged to assess patients' existing barriers to self-care within their specific contexts and cultural backgrounds and to develop targeted, patient-centred nursing interventions aimed at strengthening self-care knowledge, motivation, and skills in clinical practice. Supportive policies are needed to promote the development of innovative self-care programmes.

Chronic Obstructive Pulmonary Disease (COPD) is a major health concern in low and middle-income countries. Drug-related problems (DRPs), defined as issues in drug therapy that interfere with desired outcomes, add further challenges. Evidence on the prevalence and costs burden of DRPs in COPD patients is limited. This study assessed the prevalence of DRPs, identified associated factors, and compared prescription costs between patients with and without DRPs. A cross-sectional study was conducted among 156 COPD patients attending a first referral hospital in Dailekh, Nepal, from May to September 2024. Data were collected through in-person interviews using a structured questionnaire and medical record review. DRPs were classified using the Pharmaceutical Care Network 9.1 system. Prevalence and medications costs were reported. Factors associated with DRPs were analyzed using bivariate tests and multivariate logistic regression. Medication costs were calculated using hospital pharmacy prices and mean costs with 95% confidence interval were compared across groups. The prevalence of DRPs was 71.8%, with patients experiencing an average of 1.45 DRPs. Male patients (AOR 3.0; 95% CI 1.2-7.4), those from disadvantaged ethnic group (AOR 3.3; 95% CI 1.1-9.8), and patients with comorbidity (AOR 2.7; 95% CI 1.2-5.9) were more likely to have DRPs. The mean cost per patient was NRs. 6543 ± 1423.4). DRPs are highly prevalent among COPD patients and contribute to significant financial burden. Strengthening the role of pharmacists and other health professionals in identifying and preventing DRPs is essential. Targeted interventions for high-risk groups can help reduce DRPs, improve treatment outcomes, and lessen the economic impact on patients.

This study aimed to investigate the prevalence, awareness, treatment and control rates of chronic obstructive pulmonary disease (COPD) and their influencing factors among adults in regional China. In 2023, 6403 community-dwelling residents aged 40 years and older were randomly chosen from Nanjing municipality of China. COPD was determined as self-reported physician-diagnosed patients or post-bronchodilator FEV1/FVC < 0.70, and without other lung function impaired diseases. Multivariate logistic regression models were introduced to identify influencing factors for each rate. Totally, 5605 participants were analyzed. The spirometry-based COPD prevalence, awareness, treatment and control rates were 15.4%, 4.4%, 2.4% and 56.8%, separately, while the corresponding age- and sex-standardized rates were 15.5%, 4.6%, 2.4% and 56.5%, respectively. Age, marital status, education, physical activity and body weight status significantly correlated with COPD prevalence. Among COPD patients, these with higher educational level, chronic respiratory symptoms, or family history, and former smokers were more likely to be aware of COPD. Those smoking formerly, aged 60-79 years, with chronic respiratory symptoms, family history of COPD, higher educational level, or physical inactivity were more likely to receive treatment. Additionally, patients living in urban areas tended to have the disease under control. The prevalence of COPD among adults aged 40 years and older was high, but awareness, treatment and control rates remained limited in regional China. For the purpose to reduce COPD burden, it shall be a priority for policy-makers to initiate/provide effective and accessible education and lung function screening programs of COPD for adults in China.

Mitochondrial DNA-mediated cGAS-STING activation and its crosstalk with NLRP3 inflammasome in chronic obstructive pulmonary disease.

Examining Sleep Quality and Mortality Risks in COPD, Asthma, and Asthma-COPD Overlap Patients.

Patients with chronic obstructive pulmonary disease (COPD) are at a high risk of depression, which not only accelerates disease progression but also significantly reduces patients' quality of life. This study aimed to develop a model for the accurate prediction of depression risk in COPD patients using machine learning techniques. A total of 2234 patients with COPD were enrolled from the China Health and Retirement Longitudinal Study (CHARLS) 2020 wave, and 42 indicators covering behavioral, health, psychological and sociodemographic domains were analyzed. The least absolute shrinkage and selection operator (LASSO) regression was applied to screen predictors, and 9 machine learning models (including light gradient boosting machine (LightGBM), support vector machine (SVM), and multilayer perceptron neural network (MLP)) were constructed to identify the optimal predictive model. In addition, temporal validation was performed using CHARLS 2015 data, and model interpretation was conducted with Shapley additive explanations (SHAP). Among the 2234 included patients, 1007 (44.1%) presented with depressive symptoms. Seventeen key variables were identified and used for model construction. The results demonstrated that the LightGBM model exhibited the best performance in terms of discrimination, calibration and clinical utility, with an area under the receiver operating characteristic curve (AUROC) ranging from 0.76 to 0.81. In the validation dataset, the LightGBM model achieved an accuracy of 75.38%, a sensitivity of 78.39%, a precision of 81.14%, a specificity of 70.51%, an F1 score of 79.74% and an AUC of 0.81. Based on the optimal LightGBM model, this study provides a potentially useful approach for assessing the risk of depression in patients with COPD. The model may support early risk identification and facilitate subsequent clinical evaluation, although further validation is required to confirm its practical applicability. (1) Nine machine learning methods were used to construct the prediction model, and the LASSO method was applied to screen key factors. (2) The model was trained using the latest 2020 cohort of CHARLS and validated using the 2015 cohort. (3) The models were comprehensively compared using ROC curves, precision-recall curves, calibration curves, and DCA curves to identify the optimal model. (1) This study had a retrospective design, and the data lacked key variables such as gold-standard pulmonary function measures. (2) Validation was only performed using different time-series datasets from the same database; external validation should be conducted in future studies.

Exploring the bone-vascular axis: AI-augmented chest CT analysis in COPD highlights association between vertebral bone density and arterial calcifications.

Environmental exposures such as air pollution are well established triggers for Chronic obstructive pulmonary disease (COPD) exacerbation and impaired lung function. However, the role of aeroallergens, particularly pollen, has not been thoroughly examined in COPD, despite evidence that pollen inhalation can cause airway inflammation. We investigated whether short-term exposure to higher concentrations of ambient pollen is associated with changes in lung function in people with COPD. Thirty COPD participants, contributing 1,808 observations, were enrolled in the Study of Pollution and COPD Exacerbation (SPACE) and completed four clinic visits over one year while performing daily spirometry during four 30day seasonal periods. Ambient total pollen was measured regionally. Associations of short-term pollen exposure (lag 1-7 days and cumulative 3- and 7-day concentration) with FEV₁ and FVC were assessed using distributed lag non-linear models with a Generalized Additive Mixed Model. We tested if inflammatory biomarkers, inhaler medications, asthma history and concurrent exposure ambient pollutants (NO2, PM2.5, O3) modified associations between pollen exposure and lung function. Pollen exposure at lag 3 day was associated with lower FEV₁ (-5.5 mL per IQR pollen increase; 95% CI: -9.6, -1.3). Cumulative 3-day pollen exposure was similarly associated with lower FEV₁ (-4.9 mL; 95% CI: -9.6, -0.2). Associations of pollen and FEV1 were greater in those with higher CRP and diagnosis of asthma. In this longitudinal study of people with COPD, higher short-term exposure to ambient pollen in the preceding 3 days was associated with lower FEV₁. Our findings suggest that aeroallergen exposure may worsen airflow obstruction in people with COPD.

Chronic Obstructive Pulmonary Disease (COPD) and Heart Failure with preserved Ejection Fraction (HFpEF) frequently coexist, leading to increased hospitalization, mortality, and healthcare burden. Early identification of HFpEF risk in COPD patients is critical for timely intervention. To develop and validate an interpretable machine learning (ML) model for predicting HFpEF risk in COPD patients and to identify key predictors using explainable artificial intelligence techniques. This retrospective study analyzed 1,550 COPD patients, divided into COPD-only and COPD-HFpEF groups. Feature selection was performed using LASSO regression, logistic regression, and Boruta random forest. Ten ML models were developed and evaluated on an internal test set, with the best model further validated on an external cohort (n = 69). Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Nine predictors were consistently selected: NT-proBNP, red blood cell count, fibrinogen, cholesterol, arterial PaO₂, inspiratory capacity (IC), IC% predicted, late diastolic mitral inflow velocity (A wave), and CAT score. The XGBoost model achieved the best performance, with an AUC of 0.898 (95% CI: 0.867-0.929) on the internal test set and 0.819 (95%CI: 0.713 - 0.924) on external validation. SHAP analysis identified NT-proBNP as the most influential predictor. The developed XGBoost model accurately predicts HFpEF risk in COPD patients and offers clinically interpretable insights into key predictive markers, supporting early identification and stratified management.

Chronic obstructive pulmonary disease (COPD) and cardiovascular diseases (CVDs) have shared risk factors and mechanisms. CVDs are highly prevalent in COPD patients. Additionally, the risk of suffering a cardiovascular (CV) event is increased following an exacerbation and remains elevated for months afterwards. This link between exacerbations and increased CV risk further highlights the importance of preventing exacerbations. Clinical management during and after exacerbations regarding the prevention of CV events remains to be optimised. CV events occur in patients with COPD who have not previously been diagnosed with CVD. Conventional CV risk tools have historically underestimated the risk of CV events in patients with COPD. All patients with COPD should be investigated for CVDs and markers of CV risk should be assessed at the time of COPD exacerbations. Improving survival in COPD depends on reducing the risk of exacerbations, particularly severe exacerbations, addressing identified CV risk factors and managing CVDs when identified according to guidelines.

To develop a machine learning (ML)-based predictive model for identifying high-risk populations of osteoporosis among patients with chronic obstructive pulmonary disease (COPD), thereby facilitating early detection and personalized management. Using data from patients diagnosed with COPD in the MIMIC-IV database, we divided the dataset into training and validation sets at a 7:3 ratio. LASSO regression and logistic regression were applied to screen 35 variables, and 6 ML algorithms were employed to construct predictive models with internal validation. Model performance was evaluated using multiple metrics, followed by SHAP analysis for interpretability. All 6 ML models achieved high AUC values in both the training and test sets, as measured by the area under the receiver operating characteristic curve, with the XGBoost model demonstrating the highest overall performance. Feature importance analysis revealed that age, sex, and prothrombin time were the top 3 factors influencing osteoporosis risk. This study developed an interpretable ML-based risk prediction model for osteoporosis in COPD patients. The model provides clinicians with a novel tool for individualized osteoporosis risk assessment and early intervention, supporting personalized patient management and improved prognosis. However, this model currently relies on internal validation only and requires external testing in independent cohorts prior to clinical application.

Chronic obstructive pulmonary disease (COPD) is viewed as a significant health problem, and the prognosis of patients with this disease is strongly influenced by the inflammatory response and nutritional well-being of the body. The advanced lung cancer inflammation index (ALI) provides a complete measurement of these two factors. Although the ALI has promising applications, its relationship with the prognosis of COPD patients remains unexplored. This research sought to bridge this knowledge gap by investigating the connection between the ALI and the outcomes of COPD patients. This study retrospectively investigated 2,884 COPD patients who were admitted to the respiratory department because of acute exacerbation. The study period extends from January 1, 2017, to December 31, 2022. All these COPD patients subsequently received follow-up, and the cause-specific and all-cause mortality of these patients was reported. Kaplan‒Meier analysis was employed to investigate the associations of the ALI with all-cause mortality and mortality resulting from specific causes among COPD patients. In addition, univariable and multivariable Cox proportional hazards models were used to explore this association in further detail after adjusting for various confounding variables. A restricted cubic spline (RCS) analysis was performed to assess the nonlinear relationships of the ALI with all-cause and cause-specific death rates among COPD patients. In addition, subgroup and sensitivity analyses were conducted to verify the validity of the findings. In total, 2,884 patients with COPD were recruited. A greater ALI was strongly associated with a lower risk of all-cause mortality and mortality resulting from respiratory and cardiovascular illnesses specifically among patients with COPD. The findings of the RCS analysis indicated a reverse J-shaped, nonlinear relationship between ALI and all-cause mortality among COPD patients, and an inflection point was identified at 95 (p for nonlinearity <0.0001). The inflection point of the J-shaped pattern indicates the ALI that is associated with the lowest risk of mortality. For ALIs less than 95, an increase of 10 units in the ALI was associated with a 14% reduction in the possibility of all-cause mortality (HR: 0.86; 95% CI: 0.81-0.92; p for trend=0.01). However, when the ALI was greater than 95, a 10-unit increase in the ALI resulted in a 5% increase in the likelihood of all-cause mortality (HR: 1.05; 95% CI: 1.01-1.07; Ptrend=0.01). Similar J-shaped patterns were observed for deaths related to cardiovascular and respiratory illnesses, in which context the inflection points were 97 and 96, respectively. These findings were consistent across various medical history and demographic subgroups and remained stable during the sensitivity analysis. This study revealed a unique relationship between a high ALI and a low risk of death among COPD patients. Additionally, the relationships between the ALI and mortality (both all-cause and from specific causes) exhibited nonlinear, J-shaped patterns. These findings indicate the potential for maintaining ALI within a specific range to improve long-term survival outcomes for patients with COPD.