CONCLUSIONS AND RELEVANCE Chronic obstructive pulmonary disease is a complicated disease requiring intensive treatment. Appropriate use of long-acting maintenance bronchodilators, inhaled corticosteroids, and pulmonary rehabilitation decreases symptoms, optimizes functional performance, and reduces exacerbation frequency. Supplemental oxygen in patients with resting hypoxemia prolongs life, and other advanced treatments are available based on specific patient characteristics.

IntroductionFEF25-75 is one of the standard results provided in spirometry reports; however, in adult asthmatics there is limited information on how this physiological measure relates to clinical or biological outcomes independently of the FEV1 or the FEV1/FVC ratio.PurposeTo determine the association between Hankinson’s percent-predicted FEF25-75 (FEF25-75%) levels with changes in healthcare utilization, respiratory symptom frequency, and biomarkers of distal airway inflammation.MethodsIn participants enrolled in the Severe Asthma Research Program 1–2, we compared outcomes across FEF25-75% quartiles. Multivariable analyses were done to avoid confounding by demographic characteristics, FEV1, and the FEV1/FVC ratio. In a sensitivity analysis, we also compared outcomes across participants with FEF25-75% below the lower limit of normal (LLN) and FEV1/FVC above LLN.ResultsSubjects in the lowest FEF25-75% quartile had greater rates of healthcare utilization and higher exhaled nitric oxide and sputum eosinophils. In multivariable analysis, being in the lowest FEF25-75% quartile remained significantly associated with nocturnal symptoms (OR 3.0 [95%CI 1.3–6.9]), persistent symptoms (OR 3.3 [95%CI 1–11], ICU admission for asthma (3.7 [1.3–10.8]) and blood eosinophil % (0.18 [0.07, 0.29]). In the sensitivity analysis, those with FEF25-75%

The interplay effect has a limited impact on gated RapidArc therapy when evaluated with a linear phantom. Variations in patient breathing patterns, however, are of much greater clinical significance. Caution must be taken when evaluating patients' respiratory efforts for gated arc therapy.

Rationale Depression is a prevalent comorbidity of chronic obstructive pulmonary disease (COPD) that, along with COPD, has been associated with inflammation. An association between inflammation and depression in COPD has not been validated in a large COPD cohort. Methods Individuals from the University of Pittsburgh SCCOR cohort and the COPDGene cohort with tobacco use history and airway obstruction (FEV 1 /FVC <0.7) were evaluated using the Beck Depression Inventory II (BDI-II) and the Hospital Anxiety and Depression Scale (HADS), respectively. Participants completed symptom-related questionnaires and plasma IL-6 measurements. T -test, Fisher’s Exact tests and logistic regression were used for statistical analysis. Results The SCCOR cohort included 220 obstructed participants: 44% female and 21.4% with elevated depressive symptoms. GOLD staging distribution was predominantly stage I and II. The COPDGene cohort included 745 obstructed participants: 44% female and 13.0% with elevated depressive symptoms. GOLD distribution was predominantly stage II and III. In the SCCOR cohort, correlation between IL-6 and depressive symptoms trended toward significance (p= 0.08). Multivariable modeling adjusted for FEV 1 , age, gender and medical comorbidities showed a significant association (OR = 1.70, 95% CI = 1.08–2.69). IL-6 was significantly associated with elevated depressive symptoms in COPDGene in both univariate (p=0.001) and multivariable modeling (OR = 1.52, 95% CI =1.13–2.04). Conclusion Elevated plasma IL-6 levels are associated with depressive symptoms in individuals with COPD independent of airflow limitation and comorbid risk factors for depression. Our results suggest that systemic inflammation may play a significant and possibly bidirectional role in depression associated with COPD.

Purpose To develop and evaluate a deep learning (DL) approach to extract rich information from high‐resolution computed tomography (HRCT) of patients with chronic obstructive pulmonary disease (COPD). Methods We develop a DL‐based model to learn a compact representation of a subject, which is predictive of COPD physiologic severity and other outcomes. Our DL model learned: (a) to extract informative regional image features from HRCT; (b) to adaptively weight these features and form an aggregate patient representation; and finally, (c) to predict several COPD outcomes. The adaptive weights correspond to the regional lung contribution to the disease. We evaluate the model on 10 300 participants from the COPDGene cohort. Results Our model was strongly predictive of spirometric obstruction (r2 =  0.67) and grouped 65.4% of subjects correctly and 89.1% within one stage of their GOLD severity stage. Our model achieved an accuracy of 41.7% and 52.8% in stratifying the population‐based on centrilobular (5‐grade) and paraseptal (3‐grade) emphysema severity score, respectively. For predicting future exacerbation, combining subjects’ representations from our model with their past exacerbation histories achieved an accuracy of 80.8% (area under the ROC curve of 0.73). For all‐cause mortality, in Cox regression analysis, we outperformed the BODE index improving the concordance metric (ours: 0.61 vs BODE: 0.56). Conclusions Our model independently predicted spirometric obstruction, emphysema severity, exacerbation risk, and mortality from CT imaging alone. This method has potential applicability in both research and clinical practice.