Respiratory physiology
With many pulmonary function laboratories shut down at the beginning of the COVID 19 pandemic, the clinical value of pulmonary function test (PFT) was emphasized with an urgent call to resume services safely. It is therefore timely to review the latest literature and highlight recent insights into respiratory physiology. Functional classification of ventilatory defects relies on the results of PFTs, and the interpretation of an appropriate physiological measure, at a specific point of disease progression can be essential to target therapies and modify long term outcomes. There is however growing evidence that many current approaches to interpreting PFTs are based on historical studies that included small samples (typically males, and often smokers) using technology that is no longer relevant today. There are several recent examples from large studies and applications of artificial intelligence that highlight opportunities to improve how PFTs are conducted and interpreted. At a population level, reduced forced expiratory volume in 1 second (FEV1) is a predictor of respiratory morbidity and mortality, with increasing evidence of an association with cardiovascular disease. There is also growing evidence to support the early origins of respiratory disease; nearly half of all COPD cases are now thought to originate in childhood and early adulthood. A study of more than 50,000 people in China found that spirometric indices of small airway dysfunction [eg, maximal mid expiratory flow, forced expiratory flow (FEF) 50% and FEF 75% were reduced in more than 40% of participants and associated with multiple environmental exposures. The early and cumulative influences of environmental exposures on lung function found in these studies highlight the value of tracking respiratory function across the life course to enable early detection and intervention. However, using spirometry to track lung function is not without challenges, particularly in otherwise healthy individuals. Often FEV1 is well within the normal range during childhood and early adulthood, even in those who later develop respiratory disease. Another challenge is that the most widely used PFT to distinguish between asthma and COPD, bronchodilator response (BDR) >12% and/or 200mL change in FEV1 from the pre BDR value) may not be as sensitive in distinguishing between these two conditions as widely assumed. In a study of over 35,000 adults, 82.7% of those with a physician diagnosis of asthma did not have a BDR, whereas 18.4% of those with a clinical diagnosis of COPD did. Although spirometric indices of lung function may be good markers of lung health, the biological variability both within and between populations is large and spirometry may not be the best physiological test to identify early signs of lung disease nor to track disease progression. Standardization of respiratory function equipment and measurement protocols has had a critical role in reducing measurement error and improving interpretation for standard PFTs. The update to the American Thoracic Society/ European Respiratory Society (ATS/ERS) spirometry technical standards is the second in a series that includes technical standards for the measurement of transfer factor for carbon monoxide (TLCO or DLCO) published in 2017, and upcoming updates for measurement of lung volume and PFT interpretation. The 2019 update to the ATS/ERS spirometry standards recommends further improvements to equipment precision, standardized reporting of results and refinement of the criteria for an acceptable maneuver. With separate quality control criteria for FEV1 and FVC, the updated standards allow for the reporting of clinically "usable" results. This is particularly relevant in people with advanced lung disease, or older adults who have difficulty performing a prolonged expiration. Additionally, the use of artificial intelligence, namely convoluted neural networks, has also allowed for the development of an intuitive algorithm to evaluate spirometry quality control. Trained on more than 30,000 flow volume curves, this artificial intelligence (AI) algorithm mimics visual inspection and combines this with objective quantitative criteria to score flow volume curves with nearly 90% accuracy. Implementation of accurate automated quality control algorithms may help to facilitate the wider implementation of spirometry in primary care, or for use in remote monitoring and telemedicine, while also reducing error. Despite these improvements in standardization and interpretation for standard PFTs like spirometry, more sensitive measures of respiratory function are needed to identify early signs of disease and damage due to environmental exposures, including emerging toxins such as electronic cigarette use. Novel, but not so new, technologies such as the forced oscillation technique and multiple breath nitrogen washout