Novel Device for Measuring Lung Function using Oscillometry

The forced oscillation technique (FOT) is a non-invasive means of measuring lung mechanics. Broad-band oscillations in flow are delivered to the lungs while the resultant pressure oscillations are recorded. These signals are processed to yield the input impedance of the respiratory system (Zrs), which encapsulates the mechanical properties of the lung over the frequency range spanned by the oscillations. Clinically, can be used to assess pulmonary pathologies such as asthma and COPD. Standard methods of performing FOT are limited to the non-ambulatory clinical setting. Production of a light-weight device that operates without an external power source would allow real-time measurements of in a wide variety of more natural settings. Breath-driven oscillators, such as the Smith’s Medical Acapella and D R Burton vPEP, are currently used clinically to help cystic fibrosis patients clear mucus from their lungs by generating pressure oscillations that travel into the airways. We hypothesized that these oscillations could be used to determine . We performed FOT on healthy individuals without history of lung disease using a calibrated piston oscillator (Flexivent) to determine reference between 1 and 20 Hz. We then measured airway pressure and flow using the same sensors but with the oscillations produced by the Acapella and vPEP during tidal breathing. Respiratory resistance (Rrs), elastance (Ers) and Inertance (Irs) were determined by fitting the single-compartment model of the respiratory system to the time-domain signals from all three measurement devices. Correlation coefficients, Bland-Altman plots, and coefficients of variation were used to compare the results obtained with the three devices. We found bias values of 0.633857 [0.214382378, 1.053331908] cmH2O.s.L-1, 0.041333 [-0.38432604, 0.46699271] cmH2O.s.L-1 for comparing the Flexivent against the Acapella and vPEP, respectively. Coefficients of variation of 9.003%, 9.855%, and 9.643% were obtained for the Flexivent, Acapella, and vPEP, respectively. These results demonstrate that breath-driven oscillators are promising alternatives to conventional powered oscillators for the measurement of

Missing Covariates in Longitudinal Data with Informative Dropouts: Bias Analysis and Inference

We consider estimation in generalized linear mixed models (GLMM) for longitudinal data with informative dropouts. At the time a unit drops out, time-varying covariates are often unobserved in addition to the missing outcome. However, existing informative dropout models typically require covariates to be completely observed. This assumption is not realistic in the presence of time-varying covariates. In this article, we first study the asymptotic bias that would result from applying existing methods, where missing time-varying covariates are handled using naive approaches, which include: (1) using only baseline values; (2) carrying forward the last observation; and (3) assuming the missing data are ignorable. Our asymptotic bias analysis shows that these naive approaches yield inconsistent estimators of model parameters. We next propose a selection/transition model that allows covariates to be missing in addition to the outcome variable at the time of dropout. The EM algorithm is used for inference in the proposed model. Data from a longitudinal study of human immunodeficiency virus (HIV)–infected women are used to illustrate the methodology.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66072/1/j.1541-0420.2005.00340.x.pd