Chronic Obstructive Pulmonary Diseases-Journal of the Copd Foundation最新文献

Background: This present work focused on predicting prognostic outcomes 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 the chronic obstructive pulmonary disease (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 intensive care unit (ICU) admission and in-hospital mortality, respectively. The best feature subset was chosen by recursive feature elimination. Moreover, 7 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, a random forest (RF) model showed superb discrimination performance, and the values of area under the receiver operating characteristic curve were 0.973 and 0.828 in training and test cohorts, separately. Additionally, according to decision curve analysis, the net benefit of the 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.

Background: Patients with chronic obstructive pulmonary disease (COPD) often develop other morbidities, suggesting a systemic component to this disease. This retrospective noninterventional cohort study investigated relationships between multimorbidities in COPD and their impact on COPD exacerbations and COPD-related health care resource utilization (HCRU) using real-world evidence from Optum's de-identified Clinformatics® Data Mart Database.

Methods: Demographic and clinical characteristics were assessed. Overall comorbidity burden and proportion of individuals with gastroesophageal reflux disease (GERD), diabetes, or osteoporosis/osteopenia were compared in age-matched COPD versus non-COPD cohorts using descriptive statistics. COPD exacerbations and COPD-related HCRU (hospitalizations and emergency department visits) were compared between age-matched cohorts of COPD patients with and without specific common morbidities (GERD, diabetes, and osteoporosis/osteopenia). Additional weight-matching was performed for matched cohorts of COPD patients with and without diabetes, and with and without osteoporosis/osteopenia. The follow-up period was 5 years.

Results: Age-matched cohorts with and without COPD each comprised 158,106 patients. Morbidities were more common in the COPD cohort than the cohort without COPD (GERD: 44.9% versus 27.8%; diabetes: 40.8% versus 31.1%; osteoporosis/osteopenia: 18.8% versus 14.1%, respectively). Compared with matched cohorts with COPD only, cohorts of COPD patients with either GERD, diabetes, or osteoporosis/osteopenia experienced increased risk of severe exacerbations (odds ratio [OR]=1.819, OR=1.119, and OR=1.373, respectively), moderate exacerbations (OR=1.699, OR=1.102, and OR=1.322, respectively), or any exacerbations (OR=1.848, OR=1.099, and OR=1.384, respectively, p<0.001 for all comparisons) and increased risk of COPD-related HCRU (emergency department visits: OR=1.983, OR=1.098, and OR=1.343, respectively; hospitalization visits: OR=2.222, OR=1.26, and OR=1.368, respectively; p<0.001 for all comparisons).

Conclusion: These real-world data confirm that GERD, diabetes, and osteoporosis are common morbidities in patients with COPD and, moreover, that they affect frequency of exacerbation and HCRU. Determining and addressing the mechanisms behind the systemic effects of COPD may be beneficial for COPD patients and may also help reduce COPD exacerbations.

Background: Social distancing early in the COVID-19 pandemic helped mitigate viral spread and protect vulnerable populations. Broad availability of vaccines allowed social re-integration, but effects on mental health, social determinants of health, and attitudes among individuals with chronic obstructive pulmonary disease (COPD), who are high risk for adverse outcomes following COVID-19 infection, are unknown.

Methods: Participants in the Losartan Effects on Emphysema Progression trial were recruited into an ancillary study from May to November 2020. Study coordinators administered telephone questionnaires to evaluate respiratory symptoms (COPD Assessment Test [CAT]), anxiety (Generalized Anxiety Disorder-7 [GAD-7]) and depressive (Patient Health Questionnaire [PHQ-8]) symptoms, social isolation, instrumental support, and attitudes and actions related to the COVID-19 pandemic. Generalized estimating equation models evaluated changes in patient-reported scores from the period before vaccine availability (prevaccine, May to December 2020) to the postvaccine period (May 2021 to September 2022).

Results: Of 157 enrolled participants, 138 were interviewed during both periods. Compared with the prevaccine period, severe respiratory symptoms (CAT>20) were higher in the postvaccine period (odds ratio [OR] 1.36, 95% confidence interval [CI] 95%: 1.00-1.85), as were moderate anxiety symptoms (GAD-7≥10; OR 1.65, 95%CI: 1.11-2.46) and moderate depressive symptoms (PHQ-8≥10; OR 1.77, 95%CI: 1.22-2.55). Social isolation improved, though not significantly, and instrumental support was unchanged. In the postvaccine period compliance with COVID-19 mitigation strategies remained high and governmental health care entities were viewed as trustworthy by fewer respondents.

Conclusion: Despite a trend towards less social isolation following broad availability of COVID-19 vaccines, individuals with COPD reported worse symptoms, and greater anxiety and depressive symptoms compared to the prevaccine period.

Objective: The objective of this review is to synthesize current evidence of the association between body mass index (BMI) categories and the risk of exacerbation in patients with chronic obstructive pulmonary disease (COPD).

Methods: A systematic search was conducted across 3 electronic databases: PubMed, Embase, and Scopus. Eligible studies must have reported on the association between BMI (either as continuous or categorical) and risk of COPD exacerbation, as defined according to recognized clinical criteria. Observational studies (cohort, case-control, cross-sectional) were eligible for inclusion. The Newcastle Ottawa Scale (NOS) was used to evaluate the methodological quality. Combined effect sizes were reported as relative risk (RR) and corresponding 95% confidence intervals (CI).

Results: A total of 11 studies were included. Of them, 4 studies were prospective, 4 were retrospective cohorts in design, 2 were cross-sectional studies, and one study was a secondary data analysis from a randomized trial. Compared to patients with a normal BMI, underweight patients had an increased risk of COPD exacerbation (RR 1.90, 95% CI: 1.03, 3.48; N=7, I²=94.2%). Overweight and obese BMI status was associated with a similar risk of exacerbation.

Conclusion: Our findings report that underweight, but not overweight or obese patients, have an increased risk of COPD exacerbation, compared to individuals with a normal BMI. This differential association emphasizes the need for nuanced investigations into the underlying mechanisms of the impact of BMI on the course of COPD. Further research is needed to inform personalized interventions and improve COPD management strategies.

Introduction: Chronic obstructive pulmonary disease (COPD) is a progressive lung disease that adds a significant economic burden to the health care system in the United States. Digital platforms integrated into clinical workflows have demonstrated success in improving patient outcomes in COPD, but few studies have explored the impact of an integrated digital and clinical approach on drivers of direct health care costs (COPD-related prescriptions, emergency department [ED] visits, and hospitalizations) in a real-world setting.

Methods: We conducted a 6-month retrospective matched control analysis to assess the impact of a digital quality improvement (QI) program delivered by clinical pharmacists on health care resource utilization among people living with COPD.

Results: Compared to matched controls at 6 months, participants in the digital QI program had a 66.7% relative reduction in COPD-related ED visits and hospitalizations (0.04±0.19 versus 0.12±0.44, p=0.044), as well as a 47% reduction in all-cause ED visits and hospitalizations (0.25±0.63 versus 0.47±1.09, p=0.059). Participants in the digital QI program also had higher rates of COPD-related prescription fills for antibiotics (0.43±0.93 versus 0.35±0.74, p=0.881) and oral corticosteroids (0.56±1.02 versus 0.36±0.91, p=0.045), as well as a greater number of COPD-related nonacute urgent care visits compared to matched controls (0.3±0.63 versus 0.14±0.44, p=0.027).

Conclusion: Digital health platforms integrated into a virtual clinical pharmacist workflow can help reduce costly COPD-related ED visits and hospitalizations, and shift utilization to less acute care. Care models integrating digital platforms may also offer a scalable approach to managing COPD and should be explored in different clinical settings.

As a result of climate change, wildfire frequency, duration, and severity are increasing in the United States. Exposure to wildfire-related air pollutants can lead to negative health outcomes, particularly among patients with preexisting respiratory diseases (e.g., asthma and chronic obstructive pulmonary disease) and those who are at higher risk for developing these conditions. Underserved communities are disproportionately affected for multiple reasons, including lack of financial and social resources, increased exposure to air pollutants at home and at work, and impaired access to health care. To best serve clinically high-risk and underserved populations, health systems must leverage community public health data, develop and mobilize a wildfire preparedness action plan to identify populations at high risk, and implement interventions to mitigate the consequences of poor air quality. University of California, Davis Health, located at the epicenter of the largest wildfires in California's history, has developed the 5 pillar Wildfire Population Health Approach: (1) identify clinically at-risk and underserved patient populations using well-validated, condition-targeted registries; (2) assemble multidisciplinary care teams to understand the needs of these communities and patients; (3) create custom analytics and wildfire-risk stratification; (4) develop care pathways based on wildfire-risk tiers by disease, risk of exposure, and health care access; and (5) identify outcome measures tailored to interventions with a commitment to continuous, iterative improvement efforts. The Wildfire Population Health Approach provides an action plan for health systems and care teams to meet the needs of clinically at-risk and underserved patients affected by the increasing health threat posed by climate change-related wildfires.

Introduction: Research evidence indicates that meaning in life (MIL) is a meaningful outcome in individuals living with chronic illness, but evidence is lacking for chronic obstructive pulmonary disease (COPD).

Objectives: We hypothesized that MIL is independently associated with clinically meaningful outcomes for patients with COPD.

Methods: We performed cross-sectional analysis from a large cohort with moderate-severe COPD that participated in a home pulmonary rehabilitation study. MIL was measured using the Meaning in Life Questionnaire (MLQ), with domains of Presence and Search. The study included 340 participants: mean age 69 years old (standard deviation [SD] 9.4), 55.3% female, forced expiratory volume in 1 second (FEV₁) 44.3% (SD 19.4), and a modified Medical Research Council (mMRC) dyspnea scale of 2.69 (SD 0.84).

Results: Patients with high MLQ Presence (versus low score) had meaningfully better scores (beyond the minimal clinically important difference) across all Chronic Respiratory Questionnaire (CRQ) domains: Dyspnea, Fatigue, Emotions, and Mastery (p≤0.02); self-management (p≤0.001); social support (p≤0.001); anxiety (p≤0.001); and depression (p≤0.01) scores. When adjusting for age, sex, FEV₁, mMRC, social support, and anxiety, MLQ Presence was independently associated with CRQ domains Fatigue, Emotions, and self-management (p≤0.01). MLQ Search was independently associated with CRQ Dyspnea, Fatigue, and Mastery (p<0.05).

Conclusions: In patients with COPD, the perception that life has meaning or the willingness to search for MIL is associated with the outcomes that patients consider most important. Our results are novel as MIL is a potentially modifiable outcome that could complement person-centered conversations during clinical visits, pulmonary rehabilitation, and health coaching.

Rationale: Physical activity, lung function, and grip strength are associated with exacerbations, hospitalizations, and mortality in people with chronic obstructive pulmonary disease (COPD). We tested whether baseline inflammatory biomarkers were associated with longitudinal outcomes of these physiologic measurements.

Methods: The COPD Activity: Serotonin Transporter, Cytokines, and Depression (CASCADE) study was a prospective observational study of individuals with COPD. A total of 14 inflammatory biomarkers were measured at baseline. Participants were followed for 2 years. We analyzed associations between baseline biomarkers and forced expiratory volume in 1 second (FEV₁), physical activity, and grip strength. We used a hierarchical hypothesis testing procedure to reduce type I error. We used Pearson correlations to test associations between baseline biomarkers and longitudinal changes in the outcomes of interest. We used Fisher's linear discriminant analysis to test if linear combinations of baseline biomarkers predict rapid FEV₁ decline. Finally, we used linear mixed modeling to test associations between baseline biomarkers and outcomes of interest at baseline, year 1, and year 2; models were adjusted for age, smoking status, baseline biomarkers, and FEV₁.

Results: A total of 302 participants (age 67.5 ± 8.5 years, 19.5% female, 28.5% currently smoking) were included. Baseline biomarkers were not associated with longitudinal changes in grip strength, physical activity, or rapid FEV₁ decline. Higher interleukin-6 and C-reactive protein were associated with lower physical activity at baseline and these relationships persisted at year 1 and year 2.

Conclusion: Baseline inflammatory biomarkers did not predict changes in lung function or physical activity, but higher inflammatory biomarkers were associated with persistently low levels of physical activity.

Background: The suboptimal use of inhalers in the treatment of patients with chronic obstructive pulmonary disease (COPD) is probably a major but poorly documented problem in hospitalized patients. We aimed to describe the prevalence of misused inhalers among patients hospitalized with COPD in a department of general internal medicine.

Methods: We conducted a monocentric cross-sectional study in consecutive patients with a diagnosis of COPD and hospitalized between August 2022 and April 2023 in the internal medicine division of Fribourg Hospital, Switzerland. Patients underwent an assessment of their inhaler technique and peak inspiratory flow (PIF) using the In-Check Dial G16^®. The primary outcome was the prevalence of misused inhalers, defined as an inhaler used with a critical error and/or insufficient PIF. Secondary outcomes included the prevalence of inhalers unsuitable to patients' characteristics and of patients using at least one misused inhaler.

Results: The study included 96 patients and 160 inhalers were assessed at admission. Among these inhalers, 111 (69.4%; 95% confidence interval [CI] 61.6-76.4) were misused; 105 (65.6%; 95% CI 57.7-72.9) due to the presence of a critical error in the inhalation technique and 22 (13.8%; 95% CI 8.8-20.1) due to insufficient PIF. Concerning the secondary outcome, 27 inhalers (16.9%) were unsuitable, and 79 patients (82.3%) used at least one misused inhaler.

Conclusion: Among patients hospitalized with a diagnosis of COPD, two-thirds of inhalers were misused. Suboptimal use was mainly due to the presence of critical errors, but also to the presence of an insufficient PIF and unsuitable inhalers.