Dr Netsanet Negewo

Background: The 6 gene expression signature (6GS) predicts in¿ammatory phenotype, exacerbation risk, and corticosteroid responsiveness in asthma. In COPD, patterns of airway in¿ammation are similar, suggesting the 6GS may be useful. This study determines the diagnostic and prognostic ability of 6GS in predicting in¿ammatory phenotypes and exacerbation risk in COPD. Methods: We performed 2 studies: a cross-sectional phenotype prediction study in stable COPD (total N=132; n=34 eosinophilic (E)-COPD, n=42 neutrophilic (N)-COPD, n=39 paucigranulocytic (PG)-COPD, n=17 mixed-granulocytic (MG)-COPD) that assessed 6GS ability to discriminate phenotypes (eosinophilia=3%; neutrophilia=61%); and a prospective cohort study (total n=54, n=8 E-COPD; n=18 N-COPD; n=20 PG-COPD; n=8 MG-COPD, n=21 exacerbation prone (=2/year)) that investigated phenotype and exacerbation prediction utility. 6GS was measured by qPCR and evaluated using multiple logistic regression and area under the curve (AUC). Short-term reproducibility (intra-class correlation) and phenotyping method agreement (¿ statistic) were assessed. Results: In the phenotype prediction study, 6GS could accurately identify and discriminate patients with E-COPD from N-COPD (AUC=96.4%; p<0.0001), PG-COPD (AUC=88.2%; p<0.0001) or MG-COPD (AUC=86.2%; p=0.0001), as well as N-COPD from PG-COPD (AUC=83.6%; p<0.0001) or MG-COPD (AUC=87.4%; p<0.0001) and was reproducible. In the prospective cohort study, 6GS had substantial agreement for neutrophilic in¿ammation (82%, ¿=0.63,p<0.001)and moderate agreement foreosinophilici n¿ammation(78%, ¿=0.42,p<0.001). 6GS could signi¿cantly discriminate exacerbationprone patients (AUC=77.2%; p=0.034). Higher IL1B levels were associated with poorer lung function and increased COPD severity. Conclusion: 6GS can signi¿cantly and reproducibly discriminate COPD in¿ammatory phenotypes and predict exacerbation prone patients and may become a useful molecular diagnostic tool assisting COPD management..

Background: COPD patients are often prescribed multiple medications for their respiratory disease and comorbidities. This can lead to complex medication regimens resulting in poor adherence, medication errors, and drug-drug interactions. The relationship between clinical outcomes and medication burden beyond medication count in COPD is largely unknown. Objectives: The aim of this study was to explore the relationships of medication burden in COPD with clinical outcomes, comorbidities, and multidimensional indices. Methods: In a cross-sectional study, COPD patients (n=222) were assessed for demographic information, comorbidities, medication use, and clinical outcomes. Complexity of medication regimens was quantified using the validated medication regimen complexity index (MRCI). Results: Participants (58.6% males) had a mean (SD) age of 69.1 (8.3) years with a postbronchodilator forced expiratory volume in 1 second % predicted of 56.5 (20.4) and a median of five comorbidities. The median (q1, q3) total MRCI score was 24 (18.5, 31). COPD-specific medication regimens were more complex than those of non-COPD medications (median MRCI: 14.5 versus 9, respectively; P<0.0001). Complex dosage formulations contributed the most to higher MRCI scores of COPD-specific medications while dosing frequency primarily drove the complexity associated with non-COPD medications. Participants in Global Initiative for Chronic Obstructive Lung Disease quadrant D had the highest median MRCI score for COPD medications (15.5) compared to those in quadrants A (13.5; P=0.0001) and B (12.5; P<0.0001). Increased complexity of COPD-specific treatments showed significant but weak correlations with lower lung function and 6-minute walk distance, higher St George¿s Respiratory Questionnaire and COPD assessment test scores, and higher number of prior year COPD exacerbations and hospitalizations. Comorbid cardiovascular, gastrointestinal, or metabolic diseases individually contributed to higher total MRCI scores and/or medication counts for all medications. Charlson Comorbidity Index and COPD-specific comorbidity test showed the highest degree of correlation with total MRCI score (¿=0.289 and ¿=0.326; P<0.0001, respectively). Conclusion: In COPD patients, complex medication regimens are associated with disease severity and specific class of comorbidities.

Introduction: Sputum eosinophilia occurs in approximately one-third of stable chronic obstructive pulmonary disease (COPD) patients and can predict exacerbation risk and response to corticosteroid treatments. Sputum induction, however, requires expertise, may not always be successful, and does not provide point-of-care results. Easily applicable diagnostic markers that can predict sputum eosinophilia in stable COPD patients have the potential to progress COPD management. This study investigated the correlation and predictive relationship between peripheral blood and sputum eosinophils. It also examined the repeatability of blood eosinophil counts. Methods: Stable COPD patients (n=141) were classified as eosinophilic or noneosinophilic based on their sputum cell counts (=3%), and a cross-sectional analysis was conducted comparing their demographics, clinical characteristics, and blood cell counts. Receiver operating characteristic curve analysis was used to assess the predictive ability of blood eosinophils for sputum eosinophilia. Intraclass correlation coefficient was used to examine the repeatability of blood eosinophil counts. Results: Blood eosinophil counts were significantly higher in patients with sputum eosinophilia (n=45) compared to those without (0.3×10 /L vs 0.15×10 /L; P<0.0001). Blood eosinophils correlated with both the percentage (¿=0.535; P<0.0001) and number of sputum eosinophils (¿=0.473; P<0.0001). Absolute blood eosinophil count was predictive of sputum eosinophilia (area under the curve =0.76, 95% confidence interval [CI] =0.67¿0.84; P<0.0001). At a threshold of =0.3×10 /L (specificity =76%, sensitivity =60%, and positive likelihood ratio =2.5), peripheral blood eosinophil counts enabled identification of the presence or absence of sputum eosinophilia in 71% of the cases. A threshold of =0.4×10 /L had similar classifying ability but better specificity (91.7%) and higher positive likelihood ratio (3.7). In contrast, =0.2×10 /L offered a better sensitivity (91.1%) for ruling out sputum eosinophilia. There was a good agreement between two measurements of blood eosinophil count over a median of 28 days (intraclass correlation coefficient =0.8; 95% CI =0.66¿0.88; P<0.0001). Conclusion: Peripheral blood eosinophil counts can help identify the presence or absence of sputum eosinophilia in stable COPD patients with a reasonable degree of accuracy. 9 9 9 9 9