Effect of stimulating waveform and of data processing on respiratory impedance measurement

Several commercial and custom-made forced oscillation technique (FOT) devices are used to assess respiratory system impedance. The impulse oscillometry system (IOS) is a widespread device, which yields similar, but not identical results to those provided by other FOT systems. Differences may be related to the forcing waveform, the device hardware, or the data processing algorithms. We evaluated the agreement between resistance (Rrs) and reactance (Xrs) measurements while alternating between different forcing waveforms and data processing algorithms

COPD - do the right thing

A gap exists between guidelines and real-world clinical practice for the management and treatment of chronic obstructive pulmonary disease (COPD). Although this has narrowed in the last decade, there is room for improvement in detection rates, treatment choices and disease monitoring. In practical terms, primary care practitioners need to become aware of the huge impact of COPD on patients, have non-judgemental views of smoking and of COPD as a chronic disease, use a holistic consultation approach and actively motivate patients to adhere to treatment. This article is based on discussions at a virtual meeting of leading Nordic experts in COPD (the authors) who were developing an educational programme for COPD primary care in the Nordic region. The article aims to describe the diagnosis and lifelong management cycle of COPD, with a strong focus on providing a hands-on, practical approach for medical professionals to optimise patient outcomes in COPD primary care

Spaghetti plots of the BCCS COPD cohort.

<p>This figure was similarly labeled as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190061#pone.0190061.g004" target="_blank">Fig 4</a>.</p

Spaghetti plots of the regional CF cohort.

<p>The individual estimated median regression splines and their predicted first and second derivatives are depicted in the three CF FEV₁% Dynamic Classes. A thousand equally spaced values of the estimated median regression spline and their predicted first and second derivatives were plotted for each subject in each FEV₁% dynamic class (thin grey lines). The mean value at each interval point is plotted as the black solid line.</p

Mosaic plots of the FEV₁% dynamic classes.

<p>Mosaic plots were used to assess positive or negative associations between clinical phenotypes and FEV₁ dynamic classes with other categorical variables. Since width and length of the boxes are proportional to the observed frequencies in the datasets, the area of the boxes represent the proportion of subjects that share the attributes. To the extent that the observed proportions of subjects differs from expected proportions, the boxes are shaded to different levels of Pearson residuals. The associations between FEV₁% Dynamic Class with MDCPs and gender in the CF cohort are shown (A). The associations between FEV₁% Dynamic Class, COPD GOLD grades and either MMRC dyspnea scores or pulmonary exacerbations are depicted (B,C).</p

Distribution of physiologic variables of CF and COPD subjects in the FEV₁% dynamic classes.

<p>Notched boxplots of age, AF product, BMI and slope of the least-squares trend line are shown for CF (6A-D) and COPD (6E-H) subjects in the FEV₁% Dynamic Classes. Also shown is the distribution of the median difference between best FEV₁% (CF subjects) and the MMRC dyspnea score (COPD subjects).</p

CF and COPD FEV₁% dynamic classes classifying variables.

<p>Notched boxplots demonstrate the distribution of FEV₁% dynamic classifying variables for CF and COPD subjects in each of the FEV₁% Dynamic Classes, i.e. stable (S), intermediate (I) and hypervariable (HV).</p

FEV₁% dynamic variables in CF and COPD clinical phenotypes.

<p>Notched boxplots of the FEV₁% dynamic variables for CF (A) and COPD (B) subjects are shown for each CF MDCP class and COPD GOLD grade.</p

Median regression spline modeling of longitudinal FEV₁ measurements in cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) patients

<div><p>Rationale</p><p>Clinical phenotyping, therapeutic investigations as well as genomic, airway secretion metabolomic and metagenomic investigations can benefit from robust, nonlinear modeling of FEV₁ in <i>individual</i> subjects. We demonstrate the utility of measuring FEV₁ dynamics in representative cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) populations.</p><p>Methods</p><p>Individual FEV₁ data from CF and COPD subjects were modeled by estimating median regression splines and their predicted first and second derivatives. Classes were created from variables that capture the dynamics of these curves in both cohorts.</p><p>Results</p><p>Nine FEV₁ dynamic variables were identified from the splines and their predicted derivatives in individuals with CF (n = 177) and COPD (n = 374). Three FEV₁ dynamic classes (i.e. stable, intermediate and hypervariable) were generated and described using these variables from both cohorts. In the CF cohort, the FEV₁ hypervariable class (HV) was associated with a clinically unstable, female-dominated phenotypes while stable FEV₁ class (S) individuals were highly associated with the male-dominated milder clinical phenotype. In the COPD cohort, associations were found between the FEV₁ dynamic classes, the COPD GOLD grades, with exacerbation frequency and symptoms.</p><p>Conclusion</p><p>Nonlinear modeling of FEV₁ with splines provides new insights and is useful in characterizing CF and COPD clinical phenotypes.</p></div

Clinical phenotype characterization.

<p>Notched boxplots of basic clinical and physiological characteristics of the CF (Fig 1A) and COPD (Fig 1B) cohorts. Each subject was mapped to either a MDCP class [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190061#pone.0190061.ref013" target="_blank">13</a>] or COPD GOLD grade. The AF product of an individual is the age at the most recent FEV₁ multiplied by the best FEV₁% during the study period. The linear slope is the slope of the least squares trendline fitted to the FEV₁ data during the study period.</p