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

Background: The impact of iron deficiency on COPD morbidity independent of anemia status is unknown. Understanding the association between iron deficiency, anemia status, and risk of hospitalization in COPD may inform an approach to these comorbidities.

Study design and methods: Adults ≥40 years from the Johns Hopkins COPD Precision Medicine Center of Excellence data repository with an outpatient iron profile and 1 year of subsequent follow-up time were included in the study. Baseline characteristics were compared across iron status, defined by transferrin saturation (TSAT), using t-tests and Chi-squared tests. The association between continuous TSAT and all-cause hospitalization over the 1-year follow-up period was assessed by logistic regression. Models were adjusted by covariates with an interaction term for anemia and stratified by sex.

Results: There were 6532 individuals included with an average age of 65±12 years, 59% were female, and 56% White. Fifty-two percent of the cohort were iron deficient (TSAT≤20%), among whom 27% were non-anemic. Iron-deficient individuals had lower lung function and a higher prevalence of heart failure and diabetes. Iron deficiency was more prevalent among females (57%) compared to males (44%). In adjusted models, a decrease in TSAT by 10% was associated with 14.3% higher odds of all-cause hospitalization for females (95%CI:1.0-1.3), but not among males (OR:1.08, 95%CI:0.9-1.3). There was effect modification by anemia such that the association between TSAT and all-cause hospitalization was greater in non-anemic women (p-value interaction=0.08).

Interpretation: Iron deficiency may be associated with adverse outcomes in the absence of anemia, with non-anemic women being a COPD sub-population particularly sensitive to iron deficiency.

Background: Despite guideline recommendations, most patients with chronic obstructive pulmonary disease (COPD) do not undergo alpha-1 antitrypsin deficiency (AATD) testing and approximately 90% of people with AATD in the United States remain undiagnosed. This study sought to develop a predictive model using real-world data to improve detection of AATD-positive patients in the general COPD population.

Methods: A predictive model using extreme gradient boosting was developed using the EVERSANA database, including longitudinal, patient-level medical claims, prescription claims, AATD-specific testing data, and electronic health records (EHR). The model was trained and then validated to predict AATD-positive status. Patients were coded as AATD positive based on the presence of any of the following criteria: (1) ≥2 AATD diagnosis codes in claims; (2) an AATD diagnosis code in the EHR; (3) a positive laboratory test for AATD; or (4) use of AATD-related medication. Over 500 variables were used to train the predictive model and >20 models were run to optimize the predictive power.

Results: A total of 13,585 AATD-positive patients and 7796 AATD-negative patients were included in the model. The inclusion of non-AATD laboratory test results was critical for defining cohorts and optimizing model prediction (e.g., respiratory comorbidities, and calcium, glucose, hemoglobin, and bilirubin levels). The final model yielded high predictive power, with an area under the receiver operating characteristic curve of 0.9.

Conclusion: Predictive modeling using real-world data is a sound approach for assessing AATD risk and useful for identifying COPD patients who should be confirmed by genetic testing. External validation is warranted to further assess the generalizability of these results.

Background: Most studies on mental health among individuals with chronic obstructive pulmonary disease (COPD) utilize screening questionnaires, which detect psychiatric symptoms, but cannot be used to diagnose depression/anxiety disorders. We utilized the Mini-International Neuropsychiatric Interview (MINI) to identify depression/anxiety disorders meeting the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition diagnostic criteria and described associated disease burden in people with COPD.

Methods: This is a cross-sectional, secondary analysis of a multicenter study designed to evaluate anxiety questionnaires in COPD patients. Research coordinators administered both the MINI and screening questionnaires to determine participants who met diagnostic criteria for depression/anxiety disorders and to capture symptom burden, respectively. Bivariate analyses were conducted to assess differences in COPD and patient-reported outcomes between those with and without depression/anxiety disorders.

Results: Of 220 participants, 18 (8%) met the MINI criteria for depression and 17 (8%) for anxiety. Depression was associated with more breathlessness (modified Medical Research Council Dyspnea Scale 4 versus 3, p=0.045), higher COPD disease burden (COPD Assessment Test [CAT] 27 versus 17, p<0.001), worse sleep quality (Pittsburgh Sleep Quality Index 11 versus 7, p=0.001) and health-related quality of life (5-Level EQ-5D 0.31 versus 0.59, p<0.001). Anxiety was associated with lower CAT scores and worse health-related quality of life and function. Most with depression/anxiety disorders were not using antidepressants/anxiolytics, or receiving mental health counseling.

Conclusion: Depression and anxiety disorders meeting diagnostic criteria are relatively common comorbidities that substantially impair quality of life and are undertreated, highlighting a need to prioritize mental health as an integral part of comprehensive COPD care.

This study aimed to evaluate the performance of machine learning models for predicting readmission of patients with Chronic Obstructive Pulmonary Disease (COPD) based on administrative data and chart review data. The study analyzed 4,327 patient encounters from the University of Chicago Medicine to assess the risk of readmission within 90 days after an acute exacerbation of COPD. Two random forest prediction models were compared. One was derived from chart review data, while the other was derived using administrative data. The data were randomly partitioned into training and internal validation sets using a 70%/30% split. The two models had comparable accuracy (administrative data AUC = 0.67, chart review AUC = 0.64). These results suggest that despite its limitations in precisely identifying COPD admissions, administrative data may be useful for developing effective predictive tools and offer a less labor-intensive alternative to chart reviews.

Introduction: Lung physiology and chronic obstructive pulmonary disease (COPD) pathophysiology differ between sexes. This post hoc analysis investigated the InforMing the Pathway of COPD Treatment (IMPACT) trial outcomes by patient sex.

Methods: IMPACT was a double-blind, 52-week trial. Patients ≥40 years with symptomatic COPD and a history of exacerbations were randomized 2:2:1 to fluticasone furoate/umeclidinium/vilanterol (FF/UMEC/VI) 100/62.5/25μg, FF/VI 100/25μg, or UMEC/VI 62.5/25μg. Annual rate and risk of moderate/severe exacerbations, change from baseline in trough forced expiratory volume in 1 second (FEV₁) and St George's Respiratory Questionnaire (SGRQ) score, and safety were assessed.

Results: Of 10,355 patients, 66.3% were male. More females reported ≥2 moderate/severe prior exacerbations (58% versus 53%) at screening versus males. Additionally, females had worse mean (standard deviation) SGRQ scores (52.4[15.97] versus 49.8[17.24]) at baseline. FF/UMEC/VI improved annual exacerbation rate, lung function, and health status for both sexes versus dual therapy. The difference in trough FEV₁ across time points with FF/UMEC/VI versus FF/VI was 103mL-110mL in males and 70mL-84mL in females. On-treatment moderate/severe exacerbation rates remained higher for females (FF/UMEC/VI: 0.99; FF/VI: 1.19; UMEC/VI: 1.35) than males (0.87; 1.01; 1.14). Fewer exacerbations were experienced by females with eosinophil counts <150 cells/µL (0.81[0.68, 0.97], p=0.024) or <2 exacerbations in the past year (0.73[0.57, 0.94], p=0.013) with FF/UMEC/VI versus UMEC/VI.

Conclusion: More females with COPD reported exacerbations in the prior year at screening, as well as during the study, versus males, across all treatments. FF/UMEC/VI improved exacerbation rates versus UMEC/VI in females with eosinophil counts <150 cells/µL or <2 exacerbations in the prior year, suggesting inhaled corticosteroids may play an important role in exacerbation reduction for females in this patient population. Clinical Trial Registration: GSK (CTT116855/NCT021645B).

Introduction: A recent study found that the prevalence of chronic obstructive pulmonary disease (COPD) is significantly higher among adults who began smoking cigarettes before (versus after) 15 years of age, independent of current smoking, cigarette pack years, and smoking duration. The current analysis went a step further to also account for secondhand smoke exposure, using data from U.S. adults aged 40+ years during Wave 5 (2018-2019) of the Population Assessment of Tobacco and Health Study.

Methods: Adults who had ever smoked cigarettes were asked at what age they began smoking fairly regularly. Multivariable Poisson regression assessed the risk of self-reported COPD diagnosis due to childhood smoking (<15 years), adjusting for current smoking, cigarette pack years or smoking duration, secondhand smoke exposure, and sociodemographic covariates.

Results: Overall, 13.4% reported that they had COPD. COPD prevalence was 7.5% for adults who never smoked compared to 29.0% and 21.1% for smoking onset at age <15 and 15+ years, respectively. Adults who initiated smoking at <15 (versus 15+) years had a higher prevalence of current smoking (45.9% versus 33.3%), longer smoking duration (mean 34.2 versus 27.3 years), greater cigarette pack years (mean 48.8 versus 30.8), and greater secondhand smoke exposure (p's<0.05). In multivariable analysis, the relative risk for COPD for smoking onset <15 (versus 15+) years of age was 1.27 (95% confidence interval=1.06, 1.51).

Conclusion: The increased risk of COPD due to childhood smoking was independent of cigarette pack years, smoking duration, secondhand smoke exposure, and current smoking. The findings give further evidence of increased COPD risk related to childhood smoking.

Rationale: Acute exacerbations of chronic obstructive pulmonary disease (AECOPDs) are heterogeneous. Machine learning (ML) has previously been used to dissect some of the heterogeneity in COPD. The widespread adoption of electronic health records (EHRs) has led to the rapid accumulation of large amounts of patient data as part of routine clinical care. However, it is unclear whether the implementation of ML in EHR-derived data has the potential to identify subgroups of AECOPD.

Objectives: To determine whether ML implementation using EHR data from severe AECOPDs requiring hospitalization identifies relevant subgroups.

Methods: This study used 2 retrospective cohorts of patients with AECOPDs (non-COVID-19 and COVID-19) treated at Yale-New Haven Hospital. K-means clustering was used to identify patient subgroups.

Measurements and main results: We identified 3 subgroups in the non-COVID cohort (n=1736). Each subgroup had distinct clinical characteristics. The reference subgroup was the largest (n=904), followed by cardio-renal (n=548) and eosinophilic (n=284). The eosinophilic subgroup had milder severity of AECOPD, including a shorter hospital stay (p<0.01). The cardio-renal subgroup had the highest mortality during (5%) and in the year after hospitalization (30%). Validation of the severe AECOPD classifier in the COVID-19 cohort recapitulated the characteristics seen in the non-COVID cohort. AECOPD subgroups in the COVID-19 cohort had different interleukin (IL)-1 beta, IL-2R, and IL-8 levels (false discovery rate ≤ 0.05). These specific leukocyte and cytokine profiles resulted in inflammatory differences between the AECOPD subgroups based on C-reactive protein levels.

Conclusions: Incorporating ML with EHR data allows the identification of specific clinical and biological subgroups for severe AECOPD.

Background: Continuous respiratory monitoring can support integrated care for chronic obstructive pulmonary disease (COPD) patients, by coupling them with remote clinical personnel who triage patients in coordination with their health care providers. When deploying such services, there remains uncertainty surrounding outcomes when at-risk patients are proactively identified and escalated for provider evaluation. This study presents findings from a service deployed in a real-world COPD cohort by analyzing the clinical interventions made during in-person and telehealth pulmonary outpatient visits following remote escalations.

Methods: A single-center, retrospective, observational study of real-world COPD patients at a multisite pulmonary practice was conducted. Patients who were enrolled in a continuous respiratory monitoring service for at least one year and were seen by a provider within 7 days of an escalation by the service (N=168) were included. To evaluate the potential impact of these escalations on provider and patient burden, medical charts from outpatient visits were manually reviewed and grouped into 6 categories based on the clinical action(s) taken by the provider.

Results: A total of 245 outpatient visits occurred from 168 patients within 7 days of escalation. Of the 245 visits, 206 (84.1%) resulted in clinical intervention and 163 (66.5%) resulted in treatment consistent with acute exacerbations of COPD. A total of 1.6% of the outpatient visits resulted in referral to the emergency department.

Conclusion: Provider encounters occurring following the escalation of a patient from a continuous respiratory monitoring service consistently resulted in that provider administering a treatment to the escalated patient.

Rationale: Evidence-based guidelines recommend screening all individuals with chronic obstructive pulmonary disease (COPD) for the genetic disorder alpha-1 antitrypsin deficiency (AATD). However, it is estimated that only 5% of people with COPD have been tested for AATD, and a large fraction of the estimated 70,000 to 100,000 Americans with AATD have not yet been diagnosed. Low familiarity with AATD and limited knowledge about diagnostic tests and available treatments contribute to suboptimal screening rates.

Objectives: Our objective was to address barriers to and improve rates of guideline-based AATD diagnostic testing among racially and ethnically diverse patients with COPD at a large community health center.

Methods: A quality improvement initiative consisting of educational sessions and electronic health record (EHR) system interventions was implemented to improve the adoption of guideline-based screening for AATD in patients with COPD.

Results: An analysis of EHR data demonstrated that among patients with a COPD diagnosis (n=1030), 22.2% (n=229) were screened for AATD in the 12 months following the start of the quality improvement initiative compared with 1.3% (n=13) of patients with a COPD diagnosis (n=972) seen in the 12 months prior to the start of the quality improvement initiative (P<0.001).

Conclusions: A quality improvement initiative consisting of educational sessions and EHR system modifications was successful in increasing clinicians' knowledge and diagnostic screening rates for AATD in patients with COPD at a large community health center.

Alpha-1 antitrypsin (AAT) deficiency is an autosomal codominant disorder caused by SERPINA1 gene mutations. PI*Z and PI*S mutations commonly underlie this deficiency, but rarer homozygous PI*Null (Q0) mutations may result in a complete loss of AAT. Such rare mutations lead to severe AAT deficiency and early onset of lung disease. We present a case of a 35-year-old female never-smoker born to consanguineous parents who developed severe panlobular emphysema and end-stage respiratory insufficiency requiring lung transplantation. Subsequent genetic testing identified her as homozygous for a novel c.82del mutation-here named Q0_Bani-Yas based on the region of the primary carrier's origin-which resulted in undetectable levels of the AAT protein.