The Lognormal Lung: A new approach to quantifying lung inhomogeneity in COPD.

Early diagnosis and disease phenotyping in COPD are currently limited by the use of spirometry, which may remain normal despite significant small-airways disease and which may not fully capture a patient's underlying pathophysiology. In this study we explored the use of a new non-invasive technique that assesses gas-exchange inhomogeneity in patients with COPD of varying disease severity (according to GOLD Stage), compared with age-matched healthy controls. The technique, which combines highly accurate measurement of respiratory gas exchange using a bespoke molecular flow sensor and a mechanistic mathematical model of the lung, provides new indices of lung function: the parameters σCL, σCd, and σVD represent the standard deviations of distributions for alveolar compliance, anatomical deadspace and vascular conductance relative to lung volume, respectively. It also provides parameter estimates for total anatomical deadspace and functional residual capacity (FRC). We demonstrate that these parameters are robust and sensitive, and that they can distinguish between healthy individuals and those with mild-moderate COPD (stage 1-2), as well as distinguish between mild-moderate COPD (stage 1-2) and more severe (stage 3-4) COPD. In particular, σCL, a measure of unevenness in lung inflation/deflation, could represent a more sensitive non-invasive marker of early or mild COPD. In addition, by providing a multi-dimensional assessment of lung physiology, this technique may also give insight into the underlying pathophysiological phenotype for individual patients. These preliminary results warrant further investigation in larger clinical research studies, including interventional trials.

Altered lung physiology in two cohorts after COVID-19 infection as assessed by computed cardiopulmonography.

The longer-term effects of COVID-19 on lung physiology remain poorly understood. Here, a new technique, computed cardiopulmonography (CCP), was used to study two COVID-19 cohorts (MCOVID and C-MORE-LP) at both ∼6 and ∼12 mo after infection. CCP is comprised of two components. The first is collection of highly precise, highly time-resolved measurements of gas exchange with a purpose-built molecular flow sensor based around laser absorption spectroscopy. The second component is estimation of physiological parameters by fitting a cardiopulmonary model to the data set. The measurement protocol involved 7 min of breathing air followed by 5 min of breathing pure O2. One hundred seventy-eight participants were studied, with 97 returning for a repeat assessment. One hundred twenty-six arterial blood gas samples were drawn from MCOVID participants. For participants who had required intensive care and/or invasive mechanical ventilation, there was a significant increase in anatomical dead space of ∼30 mL and a significant increase in alveolar-to-arterial Po2 gradient of ∼0.9 kPa relative to control participants. Those who had been hospitalized had reductions in functional residual capacity of ∼15%. Irrespectively of COVID-19 severity, participants who had had COVID-19 demonstrated a modest increase in ventilation inhomogeneity, broadly equivalent to that associated with 15 yr of aging. This study illustrates the capability of CCP to study aspects of lung function not so easily addressed through standard clinical lung function tests. However, without measurements before infection, it is not possible to conclude whether the findings relate to the effects of COVID-19 or whether they constitute risk factors for more serious disease.NEW & NOTEWORTHY This study used a novel technique, computed cardiopulmonography, to study the lungs of patients who have had COVID-19. Depending on severity of infection, there were increases in anatomical dead space, reductions in absolute lung volumes, and increases in ventilation inhomogeneity broadly equivalent to those associated with 15 yr of aging. However, without measurements taken before infection, it is unclear whether the changes result from COVID-19 infection or are risk factors for more severe disease.

A dynamic model of the body gas stores for carbon dioxide, oxygen, and inert gases that incorporates circulatory transport delays to and from the lung.

Many models of the body's gas stores have been generated for specific purposes. Here, we seek to produce a more general purpose model that: 1) is relevant for both respiratory (CO2 and O2) and inert gases; 2) is based firmly on anatomy and not arbitrary compartments; 3) can be scaled to individuals; and 4) incorporates arterial and venous circulatory delays as well as tissue volumes so that it can reflect rapid transients with greater precision. First, a "standard man" of 11 compartments was produced, based on data compiled by the International Radiation Protection Commission. Each compartment was supplied via its own parallel circulation, the arterial and venous volumes of which were based on reported tissue blood volumes together with data from a detailed anatomical model for the large arteries and veins. A previously published model was used for the blood gas chemistry of CO2 and O2. It was not permissible ethically to insert pulmonary artery catheters into healthy volunteers for model validation. Therefore, validation was undertaken by comparing model predictions with previously published data and by comparing model predictions with experimental data for transients in gas exchange at the mouth following changes in alveolar gas composition. Overall, model transients were fastest for O2, intermediate for CO2, and slowest for N2. There was good agreement between model estimates and experimentally measured data. Potential applications of the model include estimation of closed-loop gain for the ventilatory chemoreflexes and improving the precision associated with multibreath washout testing and respiratory measurement of cardiac output.NEW & NOTEWORTHY A model for the body gas stores has been generated that is applicable to both respiratory gases (CO2 and O2) and inert gases. It is based on anatomical details for organ volumes and blood contents together with anatomical details of the large arteries. It can be scaled to the body size and composition of different individuals. The model enables mixed venous gas compositions to be predicted from the systemic arterial compositions.