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Objective To identify the risk factors of patients with frequent acute exacerbations of chronic obstructive pulmonary disease (AECOPD) and construct a prediction model based on the clinical data,providing a theoretical basis for the clinical prevention and treatment. Methods A total of 25 638 COPD patients admitted to the Department of Respiratory and Critical Care Medicine,the Third People's Hospital of Chengdu from January 1,2013 to May 1,2023 were selected.Among them,11 315 patients were included according to the inclusion and exclusion criteria,and their clinical characteristics were analyzed.Multivariate Logistic regression was carried out to identify the risk factors for frequent AECOPD.A nomogram model was utilized to quantify the risk of acute exacerbation,and the performance of the prediction model was assessed based on the area under the receiver operating characteristic (ROC) curve. Results In the patients with frequent AECOPD,male percentage (P<0.001),age (P<0.001),urban residence (P<0.001),smoking (P<0.001),length of stay (P<0.001),total cost (P<0.001),antibiotic cost (P<0.001),diabetes (P=0.003),respiratory failure (P<0.001),heart disease (P<0.001),application of systemic glucocorticoids (P<0.001),white blood cell count (P<0.001),neutrophil percentage (P<0.001),C-reactive protein (P<0.001),total cholesterol (P<0.001),and brain natriuretic peptide (BNP) (P<0.001) were all higher than those in the patients with infrequent AECOPD.Multivariate Logistic regression analysis revealed that age,urban residence,smoking,diabetes,heart disease,Pseudomonas aeruginosa infection,application of systemic glucocorticoids,antibiotics,respiratory failure,and elevated white blood cell count,total cholesterol,and BNP were independent risk factors for hospitalization due to frequent AECOPD.A nomogram model of hospitalization due to frequent AECOPD was constructed according to risk factors.The ROC curve was established to evaluate the performance of the model,which showed the area under the ROC curve of 0.899 (95%CI=0.892-0.905),the sensitivity of 85.30%,and the specificity of 79.80%. Conclusion Frequent AECOPD is associated with smoking,heart disease,application of systemic glucocorticoids,Pseudomonas aeruginosa infection,age,low body mass index,and elevated BNP.Predicting the risks of hospitalization due to frequent AECOPD by the established model can provide theoretical support for the treatment and risk factor management of the patients.

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BACKGROUND: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with high mortality, morbidity, and poor quality of life and constitute a substantial burden to patients and health care systems. New approaches to prevent or reduce the severity of AECOPD are urgently needed. Internationally, this has prompted increased interest in the potential of remote patient monitoring (RPM) and digital medicine. RPM refers to the direct transmission of patient-reported outcomes, physiological, and functional data, including heart rate, weight, blood pressure, oxygen saturation, physical activity, and lung function (spirometry), directly to health care professionals through automation, web-based data entry, or phone-based data entry. Machine learning has the potential to enhance RPM in chronic obstructive pulmonary disease by increasing the accuracy and precision of AECOPD prediction systems. OBJECTIVE: This study aimed to conduct a dual systematic review. The first review focuses on randomized controlled trials where RPM was used as an intervention to treat or improve AECOPD. The second review examines studies that combined machine learning with RPM to predict AECOPD. We review the evidence and concepts behind RPM and machine learning and discuss the strengths, limitations, and clinical use of available systems. We have generated a list of recommendations needed to deliver patient and health care system benefits. METHODS: A comprehensive search strategy, encompassing the Scopus and Web of Science databases, was used to identify relevant studies. A total of 2 independent reviewers (HMGG and CM) conducted study selection, data extraction, and quality assessment, with discrepancies resolved through consensus. Data synthesis involved evidence assessment using a Critical Appraisal Skills Programme checklist and a narrative synthesis. Reporting followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. RESULTS: These narrative syntheses suggest that 57% (16/28) of the randomized controlled trials for RPM interventions fail to achieve the required level of evidence for better outcomes in AECOPD. However, the integration of machine learning into RPM demonstrates promise for increasing the predictive accuracy of AECOPD and, therefore, early intervention. CONCLUSIONS: This review suggests a transition toward the integration of machine learning into RPM for predicting AECOPD. We discuss particular RPM indices that have the potential to improve AECOPD prediction and highlight research gaps concerning patient factors and the maintained adoption of RPM. Furthermore, we emphasize the importance of a more comprehensive examination of patient and health care burdens associated with RPM, along with the development of practical solutions.

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This study aimed to assess the potential of home monitoring using a monitoring application for the early prediction of acute exacerbations (AEs) in patients with fibrosing interstitial lung diseases (F-ILDs) by tracking symptoms, peripheral blood oxygen saturation (SpO2), and heart rate (HR). Data on symptoms, SpO2, and HR before and after a 1-min sit-to-stand test (1STST) were collected using an online home monitoring application. Symptoms were recorded at least 3 times a week, including cough intensity and frequency (Cough Assessment Test scale (COAT) score), breathlessness grade (modified Medical Research Council (mMRC) score), and SpO2 and HR before and after 1STST. Eighty-five patients with stable F-ILDs were enrolled. We observed a significant increase in COAT and mMRC scores, alongside a significant decrease in SpO2 before and after 1STST, 2 weeks before the first recorded AE. Furthermore, a combination of variables-an increase in COAT (≥ 4) and mMRC(≥ 1) scores, a decrease in SpO2 at rest (≥ 5%), and a decrease in SpO2 after 1STST (≥ 4%)- proved the most effective in predicting AE onset in patients with F-ILDs at 2 weeks before the first recorded AE. Home telemonitoring of symptoms, SpO2 holds potential value for early AE detection in patients with F-ILDs.

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Objective: Although the mechanism is unclear, Pseudomonas aeruginosa (PA) infection directly affects the frequency of acute exacerbations in patients with bronchiectasis. The aims of this article are to analyze the genetic mutation characteristics of the algUmucABD operon in PA, isolated from hospitalized patients with bronchiectasis, and to explore independent risk factors for frequent acute exacerbations of bronchiectasis. Methods: Based on the number of acute exacerbations that occurred in the past year, these patients with bronchiectasis were divided into those with frequent acute exacerbations (Group A) and those with non-frequent acute exacerbations (Group B). We identified the distribution of mucoid phenotypes (MPs) and alginate morphotypes (AMs) in PA, and classified them into I-IV categories based on their different AMs; otherwise, the gene mutation types (GMTs) of the algUmucABD operon were tested. Subsequently, the relationship between GMT, MP, and AM and the independent risk factors for frequent acute exacerbations in patients with bronchiectasis were explored. Results: A total of 93 patients and 75 PA strains, from January 2019 to August 2023, were included in this study. The MP and AM distributions of PA were as follows: 64 strains (85.33%) of mucoid (the AMs were 38 strains of type I, 3 strains of type II, and 23 strains of type IV) and 11 strains of non-mucoid (the AM was type III only). Mucoid PA with algU, mucA, mucB, and mucD mutations accounted for 19.61%, 74.51%, 31.37%, and 50.98%, respectively. GMT was divided into the following: mucA mutations only, mucA combined with other gene mutations, other gene mutations without mucA mutations, and without gene mutations. In 91.7% of PA with type I of AM, only mucA mutations occurred, and in both separate MP and AM, the GMT differences were statistically significant. Lastly, the number of lung lobes with bronchiectasis and the number of PA with mucA mutations only were the independent risk factors for frequent acute exacerbations. Conclusion: The mucA mutation was primarily responsible for the mucoid of MP and type I of AM in PA, and it was also an independent risk factor for frequent exacerbations of bronchiectasis.

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BACKGROUND: Acute exacerbations of COPD (AECOPDs) are increasingly recognized as episodes of heightened risk of cardiovascular events. It is not known whether exacerbation history is differentially associated with future myocardial infarction (MI) or pulmonary embolism (PE). RESEARCH QUESTION: Is the number and severity of AECOPDs associated with increased risk of MI or PE in a real-life cohort of patients with COPD? STUDY DESIGN AND METHODS: We identified a cohort of 66,422 patients (≥ 30 years of age) with a primary diagnosis of COPD in the Swedish National Airway Register from January 2014 to June 2022, with complete data on lung function. Patients were classified by moderate (prescription of oral corticosteroids) and severe (hospitalization) exacerbations the year before index date and were followed until December 2022 for hospitalization or death from MI or PE, corresponding to > 265,000 patient-years, with a maximum follow-up time of 9 years. Competing risk regression, according to the Fine-Gray model, was used to calculate subdistribution hazard ratios with 95% CIs. RESULTS: Compared with no AECOPDs in the baseline period, AECOPD number and severity were associated with increased long-term risk of both MI and PE in a gradual fashion, ranging from a subdistribution hazard ratio of 1.10 (95% CI, 0.97-1.24) and 1.33 (95% CI, 1.11-1.60), respectively, for one moderate exacerbation, to 1.82 (95% CI, 1.36-2.44) and 2.62 (95% CI, 1.77-3.89), respectively, for two or more severe exacerbations. In a time-restricted follow-up sensitivity analysis, the associations were stronger during the first year of follow-up and diminished over time. INTERPRETATION: The risk of MI and PE increases with the frequency and severity of AECOPD in this large, real-life cohort of patients with COPD.

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Background: This present work focused on predicting prognostic outcome of inpatients developing acute exacerbation of chronic obstructive pulmonary disease (AECOPD), and enhancing patient monitoring and treatment by using objective clinical indicators. Methods: The present retrospective study enrolled 322 AECOPD patients. Registry data downloaded based on COPD Pay-for-Performance Program database from January 2012 to December 2018 were used to check whether the enrolled patients were eligible. Our primary and secondary outcomes were ICU admission and in-hospital mortality, respectively. The best feature subset was chosen by recursive feature elimination. Moreover, seven machine learning (ML) models were trained for forecasting ICU admission among AECOPD patients, and the model with the most excellent performance was used. Results: According to our findings, random forest (RF) model showed superb discrimination performance, and the values of area under curve (AUC) were 0.973 and 0.828 in training and test cohorts, separately. Additionally, according to decision curve analysis, the net benefit of RF model was higher when differentiating patients with a high risk of ICU admission at a <0.55 threshold probability. Moreover, the ML-based prediction model was also constructed to predict in-hospital mortality, and it showed excellent calibration and discrimination capacities. Conclusion: The ML model was highly accurate in assessing the ICU admission and in-hospital mortality risk for AECOPD cases. Maintenance of model interpretability helped effectively provide accurate and lucid risk prediction of different individuals.

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BACKGROUND: Serum levels of stratifin (SFN), a member of the 14-3-3 protein family, increase in patients with drug-induced lung injury associated with diffuse alveolar damage. Therefore, we hypothesised that SFN levels would be higher in those experiencing acute exacerbation of interstitial lung disease (AE-ILD). A secondary analysis was also planned to determine whether SFN levels could discriminate survival in those with AE. METHODS: Thirty-two patients with clinically stable ILD (CS-ILD) and 22 patients with AE-ILD were examined to assess whether high serum SFN levels were associated with AE-ILD and whether SFN levels reflected disease severity or prognosis in patients with AE-ILD. RESULTS: Serum SFN levels were higher in the AE-ILD group than in the CS-ILD group (8.4 ± 7.6 vs. 1.3 ± 1.2 ng/mL, p < 0.001). The cut-off value of the serum SFN concentration for predicting 90-day and 1-year survival was 6.6 ng/mL. SFN levels were higher in patients who died within 90 days and 1 year than in patients who survived beyond these time points (13.5 ± 8.7 vs. 5.6 ± 5.3 ng/mL; p = 0.011 and 13.1 ± 7.5 vs. 3.1 ± 1.9 ng/mL; p < 0.001, respectively) in the AE-ILD group. When this cut-off value was used, the 90-day and 1-year survival rates were significantly better in the population below the cut-off value than in those above the cut-off value (p = 0.0017 vs. p < 0.0001). CONCLUSIONS: High serum SFN levels are associated with AE-ILD and can discriminate survival in patients with AE-ILD.

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BACKGROUND: Associations between air pollution and the acute exacerbations (AEs) of COPD have been established primarily in time-series studies in which exposure and case data were at the aggregate level, limiting the identification of susceptible populations. RESEARCH QUESTION: Are air pollutants associated with the onset of AEs of COPD in China? Who is more susceptible to the effects of air pollutants? STUDY DESIGN AND METHODS: Data regarding AEs of COPD were obtained from the Acute Exacerbation of Chronic Obstructive Pulmonary Disease Registry study, and air pollution data were assigned to individuals based on their residential address. We adopted a time-stratified case-crossover study design combined with conditional logistic regression models to estimate the associations between six air pollutants and AEs of COPD. Stratified analyses were performed by individual characteristics, disease severity, COPD types, and the season of exacerbations. RESULTS: A total of 5,746 patients were included. At a 2-day lag, for each interquartile range increase in fine particulate matter (PM2.5) and inhalable particulate matter (PM10) concentrations, ORs for AEs of COPD were 1.054 (95% CI, 1.012-1.097) and 1.050 (95% CI, 1.009-1.092), respectively. The associations were more pronounced in participants who were younger than 65 years, had experienced at least one severe AE of COPD in the past year, received a diagnosis of COPD between 20 and 50 years of age, and experienced AEs of COPD in the cool seasons. By contrast, significant associations for nitrogen dioxide, sulfur dioxide, and carbon monoxide lost significance when excluding patients collected before 2020 or with larger distance from the monitoring station, and no significant association was observed for ozone. INTERPRETATION: This study provides robust evidence that short-term exposure to PM2.5 and PM10 was associated with higher odds of AEs of COPD onset. Individuals who are young, have severe COPD, or whose first diagnosis of COPD was made when they were between 20 and 50 years of age and experience an exacerbation during the cooler seasons may be particularly susceptible. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT2657525; URL: www. CLINICALTRIALS: gov.

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Purpose: This study investigated if individuals with chronic obstructive pulmonary disease (COPD) and frailty are more likely to have acute exacerbations of COPD or require hospitalization for exacerbation than those without frailty. Patients and Methods: Data on 135 outpatients with stable COPD were analyzed with the Cox proportional hazards model to assess the risk of future events. The Kihon Checklist was administered at baseline to classify the participants as robust, pre-frail, or frail. The follow-up period was a maximum of six and a half years. Results: In all, 76 patients (56.3%) experienced an exacerbation and 46 (34.1%) were hospitalized due to it. Multivariate Cox proportional hazards analysis that accounted for FEV1 and sex showed that the frail group was more likely to face future risks of COPD exacerbations [Hazard ratio 1.762 (95% CI 1.011-3.070), p=0.046] and hospitalizations for exacerbation [2.238 (1.073-4.667), p=0.032] than the robust group. No significant differences were observed when comparing robust patients to those who were pre-frail or pre-frail to frail either in exacerbations or hospitalizations. When comparing the C-indices for frailty and FEV1, the former index (exacerbation 0.591 and hospitalization 0.663) did not exceed the latter (0.663 and 0.769) in either analysis. Conclusion: Frail COPD patients have a more unfavorable future risk of acute exacerbations of COPD and hospitalizations for exacerbation than robust patients. However, no significant differences were observed when comparing robust patients to those who were pre-frail or pre-frail to frail, suggesting that the future risk for COPD patients with frailty is only higher compared to those who are considered robust. Additionally, FEV1 was found to be a more reliable predictor of future events than measures of frailty.

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Background: Chronic obstructive pulmonary disease (COPD) is closely associated with frailty, and prevention of acute exacerbations is important for disease management. Moreover, COPD patients with frailty experience a higher risk of acute exacerbations. However, the frailty instruments that can better predict acute exacerbations remain unclear. Purpose: (1) To explore the factors influencing frailty and acute exacerbations in stable COPD patients, and (2) quantify the ability of multidimensional frailty instruments to predict acute exacerbations within 1 year. Patients and methods: In this retrospective longitudinal study, stable COPD patients were recruited from the outpatient department of Sichuan Provincial People's Hospital from July 2022 to June 2023. COPD patients reviewed their frailty one year ago and their acute exacerbations within one year using face-to-face interviews with a self-developed frailty questionnaire. Frailty status was assessed using the Frailty Index (FI), frailty questionnaire (FRAIL), and Clinical Frailty Scale (CFS). One-way logistic regression was used to explore the factors influencing frailty and acute exacerbations. Multivariate logistic regression was used to establish a prediction model for acute exacerbations, and the accuracy of the three frailty instruments was compared by measuring the area under the receiver operating characteristic curve (AUC). Results: A total of 120 individuals were included. Frailty incidence estimates using FI, FRAIL, and CFS were 23.3%, 11.7%, and 15.8%, respectively. The three frailty instruments showed consistency in COPD assessments (P<0.05). After adjusting for covariates, frailty reflected by the FI and CFS score remained an independent risk factor for acute exacerbations. The CFS score was the best predictor of acute exacerbations (AUC, 0.764 (0.663-0.866); sensitivity, 57.9%; specificity, 80.0%). Moreover, the combination of CFS plus FRAIL scores was a better predictor of acute exacerbations (AUC, 0.792 (0.693-0.891); sensitivity, 86.3%; specificity, 60.0%). Conclusion: Multidimensional frailty assessments could improve the identification of COPD patients at high risk of acute exacerbations and facilitate targeted interventions to reduce acute exacerbations in these patients.

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BACKGROUND: Acute exacerbations (AEs) of fibrotic idiopathic interstitial pneumonia (fIIP) that require hospitalization occur in some patients. During hospitalization, these patients can develop hospital-acquired pneumonia (HAP), a common hospital-acquired infection with a high mortality rate. However, the characteristics of HAP in AE-fIIP remain unknown. The purpose of this study was to determine the incidence, causative pathogens, and outcomes of HAP in patients with AE-fIIP. METHODS: The medical records of consecutive patients who were hospitalized with AE-fIIP from January 2008 to December 2019 were analyzed for the incidence, causative pathogen, and survival of HAP. The records of patients with an obvious infection-triggered AE were excluded from analysis. RESULTS: There were 128 patients with AE-fIIP (89 with idiopathic pulmonary fibrosis [IPF] and 39 with non-IPF fIIP) who were hospitalized a total of 155 times (111 with IPF and 44 with non-IPF fIIP). HAP occurred in 49 patients (40 with IPF and 9 with non-IPF fIIP). The incidence and the in-hospital mortality rates of HAP in patients with AE-fIIP were high, at 32.2% and 48.9%, respectively. Corynebacterium spp. was the most common causative pathogen, which was followed by human cytomegalovirus (HCMV). CONCLUSIONS: The incidence and the in-hospital mortality rates of HAP in patients with AE-fIIP are high. To improve their survival, patients with fIIP who had AEs and HAP should receive prompt empirical treatment for possible infections with Corynebacterium spp. and testing for HCMV.

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This study compared the treatment outcomes of acute exacerbation of COPD (AECOPD) at an academic tertiary care emergency department before and during the COVID-19 pandemic. Analyzing data from 976 patients, our study showed a significant surge in overall respiratory therapy interventions amidst the noticeable decline in the total number of AECOPD cases during the pandemic. The marked increase in the utilization of non-invasive ventilation (NIV) was particularly important, soaring from 12% to 18% during the pandemic. Interestingly, this heightened reliance on NIV stood in contrast to the stability observed in other therapeutic modalities, including oxygen insufflation alone, high-flow nasal cannulas, and invasive ventilation. This distinctive treatment pattern underscores the adaptability of healthcare providers in the face of novel challenges, with a discernible emphasis on the strategic utilization of NIV. The shift in patient acuity during the pandemic became evident as the data showed a cohort of individuals presenting with AECOPD who were more severely ill. This was reflected in the increased use of NIV and, notably, a statistically significant rise in one-year mortality rates-from 32% before the pandemic to 38% during the pandemic (p = 0.046). These findings underscore the intricate balance healthcare providers must strike in navigating the complexities of patient care during a public health crisis. A closer examination of the longitudinal trajectory revealed a subtle decrease in re-admission rates from 65% to 60%. The increased reliance on NIV, a key finding of this investigation, reflects a strategic response to the unique demands of the pandemic, potentially influenced by both medical considerations and non-medical factors, such as the prevalent "fear of aerosols" and the imperative to navigate transmission risks within the healthcare setting. These insights contribute to understanding the evolving dynamics of AECOPD management during public health crises.

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OBJECTIVES: We aimed to evaluate the utility of an Observation Unit (OU) in management of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) and to identify the clinical characteristics of patients readmitted within 30-days for AECOPD following index admission to the OU or inpatient floor from the OU. METHODS: This is a retrospective observational study of patients admitted from January to December 2017 for AECOPD to an OU in an urban-based tertiary care hospital. Primary outcome was rate of 30-day readmission after admission for AECOPD for patients discharged from the OU versus inpatient service after failing OU management. Regression analyses were used to define risk factors. RESULTS: 163 OU encounters from 92 unique patients were included. There was a lower readmission rate (33%) for patients converted from OU to inpatient care versus patients readmitted after direct discharge from the OU (44%). Patients with 30-day readmissions were more likely to be undomiciled, with history of congestive heart failure (CHF), pulmonary embolism (PE), or had previous admissions for AECOPD. Patients with >6 annual OU visits for AECOPD had higher rates of substance abuse, psychiatric diagnosis, and prior PE; when these patients were excluded, the 30-day readmission rate decreased to 13.5%. CONCLUSION: Patients admitted for AECOPD with a history of PE, CHF, prior AECOPD admissions, and socioeconomic deprivation are at higher risk of readmission and should be prioritized for direct inpatient admission. Further prospective studies should be conducted to determine the clinical impact of this approach on readmission rates.

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BACKGROUND: Approximately 50% of patients with interstitial lung disease (ILD) experience frailty, which remains unexplored in acute exacerbations of ILD (AE-ILD). A better understanding may help with prognostication and resource planning. We evaluated the association of frailty with clinical characteristics, physical function, hospital outcomes, and post-AE-ILD recovery. METHODS: Retrospective cohort study of AE-ILD patients (01/2015-10/2019) with frailty (proportion ≥0.25) on a 30-item cumulative-deficits index. Frail and non-frail patients were compared for pre- and post-hospitalization clinical characteristics, adjusted for age, sex, and ILD diagnosis. One-year mortality, considering transplantation as a competing risk, was analysed adjusting for age, frailty, and Charlson Comorbidity Index (CCI). RESULTS: 89 AE-ILD patients were admitted (median: 67 years, 63% idiopathic pulmonary fibrosis). 31 were frail, which was associated with older age, greater CCI, lower 6-min walk distance, and decreased independence pre-hospitalization. Frail patients had more major complications (32% vs 10%, p = .01) and required more multidisciplinary support during hospitalization. Frailty was not associated with 1-year mortality (HR: 0.97, 95%CI: [0.45-2.10]) factoring transplantation as a competing risk. CONCLUSIONS: Frailty was associated with reduced exercise capacity, increased comorbidities and hospital complications. Identifying frailty may highlight those requiring additional multidisciplinary support, but further study is needed to explore whether frailty is modifiable with AE-ILD.

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BACKGROUND: The clinical significance of the impulse oscillometry-defined small airway bronchodilator response (IOS-BDR) is not well-known. Accordingly, this study investigated the clinical characteristics of IOS-BDR and explored the association between lung function decline, acute respiratory exacerbations, and IOS-BDR. METHODS: Participants were recruited from an Early Chronic Obstructive Pulmonary Disease (ECOPD) cohort subset and were followed up for two years with visits at baseline, 12 months, and 24 months. Chronic obstructive pulmonary disease (COPD) was defined as a post-bronchodilator forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) ratio < 0.70. IOS-BDR was defined as meeting any one of the following criteria: an absolute change in respiratory system resistance at 5 Hz ≤ - 0.137 kPa/L/s, an absolute change in respiratory system reactance at 5 Hz ≥ 0.055 kPa/L/s, or an absolute change in reactance area ≤ - 0.390 kPa/L. The association between IOS-BDR and a decline in lung function was explored with linear mixed-effects model. The association between IOS-BDR and the risk of acute respiratory exacerbations at the two-year follow-up was analyzed with the logistic regression model. RESULTS: This study involved 466 participants (92 participants with IOS-BDR and 374 participants without IOS-BDR). Participants with IOS-BDR had higher COPD assessment test and modified Medical Research Council dyspnea scale scores, more severe emphysema, air trapping, and rapid decline in FVC than those without IOS-BDR over 2-year follow-up. IOS-BDR was not associated with the risk of acute respiratory exacerbations at the 2-year follow-up. CONCLUSIONS: The participants with IOS-BDR had more respiratory symptoms, radiographic structural changes, and had an increase in decline in lung function than those without IOS-BDR. TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR1900024643. Registered on 19 July, 2019.

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BACKGROUND AND OBJECTIVE: To investigate pulmonary thromboembolism (PE) in acute exacerbation of chronic obstructive pulmonary disease (AE-COPD) patients in plateau regions, we performed a prospective cohort study to evaluate the prevalence, risk factors and clinical characteristics of PE in the cohort of hospitalized patients at high altitude. METHODS: We did a prospective study with a total of 636 AE-COPD patients in plateau regions. Demographic and clinical data, laboratory data, including ultrasound scans of the lower extremities and cardiac ultrasound, and computed tomographic pulmonary angiography (CTPA) variables were obtained, and comparisons were made between groups with and without PE. We also conducted logistic regression to explore the risk factors of PE. RESULTS: Of the 636 patients hospitalized with AE-COPD (age 67.0 ± 10.7 years, 445[70.0%] male), 188 patients developed PE (29.6% [95% CI: 26.0%, 33.1%]). Multivariable logistic regression showed that ethnic minorities, D-dimer > 1 mg/L, AST > 40 U/L, chest pain, cardiac insufficiency or respiratory failure, Padua score > 3, and DVT were associated with a higher probability of PE. CONCLUSIONS: The prevalence of PE is high and those with a higher Padua score, the occurrence of deep venous thrombosis, higher neutrophil count, chest pain, cardiac insufficiency or respiratory failure, higher levels of AST, and a higher level of D-dimer had a higher risk of PE. The analysis of AE-COPD may help to provide more accurate screening for PE and improve clinical outcomes of patients with AE-COPD in plateau regions.

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Background: The neutrophil-to-lymphocyte ratio (NLR) is an inexpensive biomarker that potentially predicts acute exacerbations of chronic obstructive pulmonary disease (AECOPDs). We evaluated the association of baseline NLR and respiratory hospitalization risk within one year among chronic obstructive pulmonary disease (COPD) patients in Uganda, a low- and middle-income country. Methods: A total of 312 COPD patients were followed for one year. Clinical characteristics and exacerbation rates were collected. Poisson regression with robust variance estimators was used to measure the association between NLR and hospital admissions due to COPD exacerbations. Receiver-operator characteristic (ROC) curves and the area under the curve were used to assess the ability of NLR to predict AECOPDs. Results: The median (Q 1, Q 3) age was 64 years (53, 71). Females comprised 50.96% (n=159) of the cohort, and 71.2% (n=222) of participants had moderate or severe COPD. A total of 9.9% (n=31) of participants experienced a COPD exacerbation during the period of follow-up. At baseline, the median (Q 1, Q 3) NLR ratio among participants who experienced an exacerbation was 1.46 (0.92, 2.33) compared to 1.03 (0.72,1.42) among those who did not experience one during the follow-up period (p=0.002). Using Youden and Liu's methods, the optimal NLR cutoff for predicting COPD exacerbation was 1.17. This cutoff resulted in a ROC curve area of 0.64 (95% confidence interval: 0.56, 0.73). Conclusion: The NLR could be used as a risk predictor, in low- and middle-income countries, for hospital admissions due to COPD exacerbations. A cutoff of 1.17 was an independent predictor of hospitalization due to acute exacerbations of COPD within one year.

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Introduction: In 2019, the Beta-Blockers for the Prevention of Acute Exacerbations of Chronic Obstructive Pulmonary Disease study (BLOCK-COPD) evaluated the effect of metoprolol on exacerbation risk and mortality in a COPD population without indications for beta-blocker use. We hypothesized that an imaging metric of coronary artery disease (CAD), the coronary artery calcium (CAC) score, would predict exacerbation risk and identify a differential response to metoprolol treatment. Methods: The study population includes participants in the BLOCK-COPD study from multiple study sites. Participants underwent clinically indicated thoracic computed tomography (CT) scans ± 12 months from enrollment. The Weston scoring system quantified CAC. Adjusted Cox proportional hazards models evaluated for associations between CAC and time to exacerbation. Results: Data is included for 109 participants. The mean CAC score was 5.1±3.7, and 92 participants (84%) had CAC scores greater than 0. Over a median (interquartile range) follow-up time of 350 (280 to 352) days, there were 61 mild exacerbations and 19 severe/very severe exacerbations. No associations were found between exacerbations of any severity and CAC>0 or total CAC. Associations were observed between total CAC and CAC>0 in the left circumflex (LCx) and time to exacerbation of any severity (adjusted hazard ratio [aHR]=1.39, confidence interval [CI]: 1.08-1.79, p=0.01) and (aHR=1.96, 95% CI: 1.04-3.70, p=0.04), respectively. Conclusions: CAD is a prevalent comorbidity in COPD accounting for significant mortality. Our study confirms the high prevalence of CAD using the CAC score; however, we did not discover an association between CAC and exacerbation risk. We did find novel associations between CAC in the LCx and exacerbation risk which warrant further investigation in larger cohorts.

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BACKGROUND: Acute exacerbations of idiopathic pulmonary fibrosis (AE-IPF) increases mortality risk, but which factors increase mortality is unknown. We aimed to perform a prognostic review of factors associated with mortality in patients with IPF. STUDY DESIGN: and methods: We searched MEDLINE, EMBASE, and CINAHL for studies that reported on the association between any prognostic factor and AE-IPF. We assessed risk of bias using the QUIPS tool. We conduced pairwise meta-analyses using REML heterogeneity estimator, and GRADE approach to assess the certainty of the evidence. RESULTS: We included 35 studies in our analysis. We found that long-term supplemental oxygen at baseline (aHR 2.52 [95 % CI 1.68 to 3.80]; moderate certainty) and a diagnosis of IPF compared to non-IPF ILD (aHR 2.19 [95 % CI 1.22 to 3.92]; moderate certainty) is associated with a higher risk of death in patients with AE-IPF. A diffuse pattern on high resolution computed tomography (HRCT) compared to a non-diffuse pattern (aHR 2.61 [95 % CI 1.32 to 2.90]; moderate certainty) is associated with a higher risk of death in patients with AE-IPF. We found that using corticosteroids prior to hospital admission (aHR 2.19 [95 % CI 1.26 to 3.82]; moderate certainty) and those with increased neutrophils (by % increase) in bronchoalveolar lavage (BAL) during the exacerbation is associated with a higher risk of death (aHR 1.02 [1.01 to 1.04]; moderate certainty). INTERPRETATION: Our results have implications for healthcare providers in making treatment decisions and prognosticating the clinical trajectory of patients, for researchers to design future interventions to improve patient trajectory, and for guideline developers in making decisions about resource allocation.

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BACKGROUND: Accurate prediction of acute exacerbation helps select patients with chronic obstructive pulmonary disease (COPD) for individualized therapy. The potential of lymphocyte subsets to function as clinical predictive factors for acute exacerbations of chronic obstructive pulmonary disease (AECOPD) remains uncertain. METHODS: In this single-center prospective cohort study with a 2-year follow-up, 137 patients aged 51 to 79 with AECOPD were enrolled. We examined the prognostic indicators of AECOPD by analyzing lymphocyte subsets and baseline symptom score. Furthermore, a predictive model was constructed to anticipate the occurrence of respiratory failure in patients experiencing AECOPD. RESULTS: The COPD Assessment Test (CAT) score combined with home oxygen therapy and CD4+CD8+ T cells% to predict respiratory failure in AECOPD patients were the best (the area under the curves [AUC] = 0.77, 95% CI: 0.70-0.86, P < 0.0001, sensitivity: 60.4%, specificity: 86.8%). The nomogram model, the C index, calibration plot, decision curve analysis, and clinical impact curve all indicate the model's good predictive performance. The observed decrease in the proportions of CD4+CD8+ T cells appears to be correlated with more unfavorable outcomes. CONCLUSIONS: The nomogram model, developed to forecast respiratory failure in patients with AECOPD, utilizing variables such as home oxygen therapy, CAT score, and CD4+CD8+ T cells%, demonstrated a high level of practicality in clinical settings. CD4+CD8+ T cells serve as a reliable and readily accessible predictor of AECOPD, exhibiting greater stability compared to other indices. It is less susceptible to subjective influences from patients or physicians. This model facilitated personalized estimations, enabling healthcare professionals to make informed decisions regarding preventive interventions.

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