Article Text

other Versions

Download PDFPDF

Measuring lung function in airways diseases: current and emerging techniques
Free
  1. Nayia Petousi1,2,3,
  2. Nick P Talbot1,2,3,
  3. Ian Pavord1,3,
  4. Peter A Robbins2
  1. 1Nuffield Department of Clinical Medicine Division of Experimental Medicine, University of Oxford, Oxford, UK
  2. 2Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
  3. 3Oxford Centre for Respiratory Medicine, Oxford University Hospitals NHS Trust, Oxford, UK
  1. Correspondence to Dr Nayia Petousi, Nuffield Department of Clinical Medicine Division of Experimental Medicine, Oxford OX3 9DU, UK; nayia.petousi{at}dpag.ox.ac.uk

Abstract

Chronic airways diseases, including asthma, COPD and cystic fibrosis, cause significant morbidity and mortality and are associated with high healthcare expenditure, in the UK and worldwide. For patients with these conditions, improvements in clinical outcomes are likely to depend on the application of precision medicine, that is, the matching of the right treatment to the right patient at the right time. In this context, the identification and targeting of ‘treatable traits’ is an important priority in airways disease, both to ensure the appropriate use of existing treatments and to facilitate the development of new disease-modifying therapy. This requires not only better understanding of airway pathophysiology but also an enhanced ability to make physiological measurements of disease activity and lung function and, if we are to impact on the natural history of these diseases, reliable measures in early disease. In this article, we outline some of the key challenges faced by the respiratory community in the management of airways diseases, including early diagnosis, disease stratification and monitoring of therapeutic response. In this context, we review the advantages and limitations of routine physiological measurements of respiratory function including spirometry, body plethysmography and diffusing capacity and discuss less widely used methods such as forced oscillometry, inert gas washout and the multiple inert gas elimination technique. Finally, we highlight emerging technologies including imaging methods such as quantitative CT and hyperpolarised gas MRI as well as quantification of lung inhomogeneity using precise in-airway gas analysis and mathematical modelling. These emerging techniques have the potential to enhance existing measures in the assessment of airways diseases, may be particularly valuable in early disease, and should facilitate the efforts to deliver precision respiratory medicine.

  • lung function
  • spirometry
  • inhomogeneity
  • airways disease
  • physiology
  • thoracic imaging

Statistics from Altmetric.com

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Introduction

Chronic obstructive lung disease (COPD) affects ~200 million people worldwide,1 is responsible for >10% of acute National Health Service (NHS) hospital admissions2 and will become the third-leading cause of death by 2020.1 Asthma affects ~300 million people worldwide, causes greater overall disability in the UK than diabetes or breast cancer1 and places a huge burden on healthcare resources.3 Cystic fibrosis (CF), the most common genetic illness in the UK, is a life-limiting illness affecting children and young adults and is associated with high costs of daily care.4 These chronic airways diseases differ substantially in underlying pathophysiology, but are all characterised by airway inflammation, airflow obstruction and high morbidity and mortality.1

In recent years, there has been significant progress towards understanding the biology underlying airways diseases. In asthma and COPD, the identification of distinct inflammatory phenotypes and biomarkers such as FeNO and blood eosinophil counts has allowed the development of highly targeted biological drugs.5–7 In CF, novel drugs targeting specific CF transmembrane conductance regulator (CFTR) mutations have revolutionised the management of some patients.8 9 Such targeted therapy, the essence of precision medicine, is becoming increasingly feasible across the spectrum of airways diseases.

Unfortunately, the assessment of lung function in airways diseases has not kept pace with these exciting scientific and therapeutic advances. We remain heavily reliant on traditional spirometric measures such as the forced expiratory volume in 1 sec (FEV1) and forced vital capacity (FVC) for diagnosis and disease monitoring, despite an increasing recognition that they fail to detect subtle disease, are insensitive to treatment effects and cannot capture the complexity of heterogeneous pathology. In this review, we outline the challenges this presents for the respiratory community, focusing particularly on: (1) early diagnosis; (2) identification of specific phenotypes or ‘treatable traits’ in patients with established disease, and (3) identification of response to treatment. We provide an overview of established techniques for measuring lung function, a more detailed account of which is provided elsewhere,10 before discussing emerging techniques that may help to overcome these clinical challenges.

Current challenges in airways diseases

Chronic obstructive pulmonary disease (COPD)

A major challenge to efforts to modify the natural history of COPD is identification of early disease. Guidelines advocate the demonstration of obstructive spirometry (post-bronchodilation FEV1/FVC <0.70)11 for diagnosis, but FEV1 reflects predominantly large airway dysfunction and may miss active disease in the smaller (<2 mm diameter) airways. In younger patients, this may lead either to failure of diagnosis despite significant symptoms or to late diagnosis when structural changes in the lung may be irreversible. In either case, important opportunities for early therapy (ie, inhaled corticosteroids for eosinophilic disease)12 or secondary prevention (including smoking cessation) are missed,13 14 highlighting the need for markers of early disease.15 In older patients, the opposite may occur when FEV1/FVC<0.70 is used as a diagnostic criterion: overdiagnosis and unnecessary treatment.16 It has been proposed that classification should be based on a lower limit of normal (LLN), that is, more than 1.64 SD below the predicted level (fifth percentile), which should add accuracy and confidence to the diagnostic approach, but this recommendation has not been taken up by global guidelines.16 17

A second challenge is stratification. FEV1 remains the principal means of classifying COPD severity, and it is predictive of mortality and outcomes at a population level.18 However, its correlation with symptoms and key clinical outcomes such as exacerbation frequency and treatment response is poor, as recognised by GOLD (Global Initiative for chronic obstructive lung disease) in its recent updated statement.11 This may reflect the inability of spirometry to capture or adequately distinguish heterogeneous underlying pathologies, for example, obstructive bronchitis, emphysema or the progression of childhood/chronic asthma. Finally, it is now widely acknowledged that fixed airflow obstruction in later adult life can be the result of several different lung-function decline trajectories.19 Without the ability to discriminate between these trajectories, it will be difficult to identify true disease-modifying interventions.

Asthma

An important but underappreciated feature of asthma is the discordance between lung function measures and clinical markers of disease control, including symptoms, exacerbation frequency and response to treatment. Baseline FEV1, for example, correlates poorly with symptom severity and quality of life20 and may also be dissociated from markers of airway inflammation such as FeNO,21 perhaps reflecting the poor sensitivity of spirometry for disease located in the small airways.

Asthma treatment guidelines rely on a stepwise increase or decrease in medications based on symptom control22 but patients with asthma vary in their underlying biology and response to treatment, so this approach often leads to poor targeting of treatment. Classification into inflammatory phenotypes based on ‘type-2’ biomarkers reflecting type-2 cytokine activity (blood/sputum eosinophils and FeNO) has been useful in understanding and targeting therapies,23–26 but does not always predict response. Some patients with type-2 high inflammation, for example, do not respond to inhaled corticosteroids and require systemic treatment for disease control. This may, in part, reflect a more peripheral distribution of disease, inaccessible to inhaled corticosteroids.27 28 Current measures of lung pathophysiology cannot identify this phenotype.

The advent of targeted biological therapy offers new opportunities in severe asthma, with early evidence of improvement in quality of life, reduction in oral corticosteroid use and decreased frequency of exacerbations.5 6 29 30 However, biologics are expensive and further advances will require careful patient selection and evaluation of efficacy, including the withdrawal of ineffective therapy. Again, baseline indices often provide a limited perspective on long-term efficacy, leading some authors to suggest that the crude ‘global evaluation of treatment effectiveness’ by physicians is the best measure of response.31 Clearly, more objective measurements of disease activity would be helpful.

Cystic fibrosis

Spirometry remains the principal means of monitoring CF progression, but advances in multidisciplinary care mean that many patients maintain a normal FEV1 well into adulthood. There is mounting evidence that bronchiectasis and airway obstruction may go undetected in earlier life,32 preventing early intervention. The advent of highly effective but expensive new CFTR modulators8 9 has highlighted the urgent need for more sensitive assays of lung function, not only for timely introduction of therapy but also to monitor response and consider withdrawal in those who do not respond.

There is also a particular need for earlier detection of deteriorating lung function in patients with CF, which may precede clinical exacerbations. Such episodes are important events, from which patients may not fully recover, leading to progressive lung function decline.33 Prompt and effective treatment is paramount and is currently limited by reliance on changes in FEV1.

Pulmonary function tests used routinely in clinical practice

The utility of the routine pulmonary function tests in assessing airways diseases, together with their advantages and disadvantages, are detailed in table 1.

Table 1

Advantages and disadvantages of current routinely-used pulmonary function tests

Spirometry is the most routinely and widely used lung function test in the diagnosis and monitoring of airways diseases (figure 1A). The FEV1/FVC ratio is used to diagnose airflow obstruction and the %predicted FEV1 to classify severity and monitor disease progression. Important information is also acquired from the flow–volume loop (figure 1B): a concave expiratory loop shape and a low maximal mid-expiratory flow (MMEF) index indicate small airways dysfunction, even when FEV1 or FEV1/FVC are normal.

Figure 1

(A) Volume–time graph obtained during a spirometry test, with volume of gas exhaled during a forced expiration against time, for a healthy volunteer (blue) and a patient with airway obstruction (red). FEV1 is the volume of gas that the subject is able to exhale in the first second of forced expiration; FVC is the total volume of gas the subject can exhale in one forced exhaled breath, from total lung capacity (TLC) to residual volume (RV). (B) Flow–volume loop obtained during a spirometry test in a healthy volunteer (blue). The expiration loop displays flow against volume during forced expiration from TLC to RV and the inspiration loop flow against volume during inspiration from RV to TLC. MMEF (or FEF25–75) signifies the mean expiratory flow between the flow at 25% of FVC (MEF25) and the flow at 75% of FVC (MEF75). The red trace indicates ‘scooping’ of the expiratory loop, as seen in airway obstruction.

Body plethysmography measures static lung volumes and provides indices such as: total lung capacity (TLC), residual volume (RV), functional residual capacity (FRC), RV/TLC and airways resistance (Raw). It is useful in detecting hyperinflation, that is, increased lung volume at end expiration, which may be caused by airway narrowing in either asthma or COPD, or by loss of elastic recoil in emphysema, leading to early airway closure and gas trapping. In COPD, body plethysmography is routinely used to guide management of hyperinflation using lung volume reduction therapies such endobronchial valves or coils.34 35 In asthma, body plethysmography can inaccurately overestimate lung volumes,36 although measures such as %predicted RV and RV/TLC may correlate with small airways disease37 and gas trapping in severe disease.38 Additionally, measurement of specific airways resistance (sRaw, ratio of shift volume-to flow rate) and airways resistance (Raw, alveolar pressure change-to flow rate, arithmetically equal to sRaw/FRC) can be obtained from resistance–volume loops. It has been suggested that these measurements may be useful in patients with COPD with hyperinflation and that they are more sensitive to change following bronchodilation or bronchial provocation tests.39 However, these potential advantages need to be set against measurement complexity and increased within-subject variability.

Gas transfer factor quantifies the transfer of carbon monoxide (CO) from the alveolar gas into the pulmonary capillaries. Measurement involves inhalation of a gas mixture containing a low concentration of CO, plus a known concentration of an inert gas, for example, helium. During a single 10 sec breath hold, the rate of CO uptake from alveolar gas into capillary blood (KCO) is measured, and the accessible lung volume (VA) is estimated by helium dilution. TLCO is calculated as KCO×VA and essentially represents an index of whole lung CO transfer from gas to blood. Transfer factor is useful in distinguishing conditions characterised by alveolar destruction, for example, emphysematous COPD, from those with preserved lung architecture, for example, asthma. In asthma and CF, TLCO and KCO are typically preserved or may even be elevated, due to either increased pulmonary blood flow (which increases TLCO and KCO) or reduced apparent VA (which disproportionately increases KCO). Such KCO changes may be useful markers of early or heterogeneous disease.40 A new, but similar, measure is the transfer factor for nitric oxide (TLNO), which is 4.5–5.0 times the TLCO value. Unlike TLCO, TLNO is unaffected by the partial pressure of alveolar oxygen or haemoglobin concentration.41 In practice, TLNO and TLCO are measured simultaneously, and the TLNO/TLCO ratio may prove to have some diagnostic advantage over the TLCO alone.42

Physiological assessments of lung function currently not in routine use

A number of physiological techniques, detailed below, have been explored as tools for the assessment of small airways function or the detection of early or subtle lung damage.

Oscillometry techniques

Forced oscillometry techniques (FOT),43–45 first described in 1956,43 apply oscillating pressure airwaves of multiple frequencies (3–30 Hz) during normal tidal breathing. Impulse oscillometry is a form of FOT in which a fixed 5 Hz square pressure wave is applied and from which other frequencies are derived. The resulting changes in pressure and flow at the mouth are analysed using Fourier transformation to calculate parameters including resistance (Rrs) and reactance (Xrs), the latter predominantly reflecting elasticity of the peripheral lung parenchyma and chest wall (which also affects Rrs to a smaller extent). Low frequencies (eg, 5 Hz) penetrate deep into the lung, while high frequencies (eg, 20 Hz) are absorbed before reaching the smaller peripheral airways. Hence, R5 represents the total airway resistance, R20 the central airways resistance and R5–R20 is an index of small airway resistance.

A predominant increase of Rrs measured by oscillometry at low frequencies is thought to reflect obstruction in distal airways in asthma and COPD.44 46–48 Reactance at low frequencies (eg, X5) is more negative in obstructive lung disease. Impulse oscillometry indices are sensitive to bronchodilator effects in COPD and asthma49–51 and inhaled corticosteroids in asthma.52 They may also be useful in monitoring recovery from exacerbation.53 In addition to whole-breath system analysis, breath-by-breath analysis using oscillometry provides additional measures of airflow limitation. For example, patients with COPD experience expiratory flow limitation (EFL) during tidal breathing, which is a major determinant of dynamic hyperinflation and dyspnoea. The difference between inspiratory and expiratory reactance (ΔXrs) measured by oscillometry can reliably detect this EFL in patients with COPD.54 Additionally, inspiratory–expiratory ΔX5 analysis differentiated patients with asthma from those with COPD, presumably reflecting enhanced dynamic airway narrowing on expiration in COPD.55

The technique is non-invasive, easy to perform, effort independent and reproducible. It is particularly attractive in small children but has some disadvantages. Interference from swallowing and upper airway artefacts is common and to avoid these coaching and repetition may be required. While oscillometry measurements are portrayed as a marker of small airway dysfunction, the relevant indices may also be influenced by large airway abnormalities. Furthermore, universal normal reference ranges have not been established. Moreover, the interpretation of measurements and meaning of results is not straightforward for users. These disadvantages may explain why, although the equipment is widely available commercially, it has not been widely adopted by clinical lung function laboratories.

Inert gas washout tests

It was recognised in the 1950s that multiple breath inert gas washout (MBW) could provide an index of ventilatory inhomogeneity56 and shown almost 40 years ago that this technique might be helpful in the identification of patients with early lung disease.57 However, only since the development of modern gas analysers and computers has this approach become feasible in clinical practice. MBW measures ventilation inhomogeneity by tracking the washout of an endogenous (eg, nitrogen) or exogenous (eg, SF6) inert gas, with the latter requiring an initial wash-in phase.58 59 In the case of nitrogen washout, a subject breathes 100% oxygen, and the composition of exhaled gas is measured using a respiratory mass spectrometer or other device. Since nitrogen will be washed out rapidly from well-ventilated areas and more slowly from poorly ventilated areas, the overall rate and shape of nitrogen washout is a function of ventilation inhomogeneity (figure 2).

Figure 2

Nitrogen concentration and volume tracings as a function of time during a nitrogen multiple breath washout (MBW) test. During the washout, the subject breathes 100% oxygen, which causes a progressive reduction in nitrogen concentration in the lung as it is being replaced by oxygen. The rate and shape of nitrogen washout is a function of ventilation inhomogeneity. The Lung Clearance Index (LCI) is the number of lung turnovers or functional residual capacity (FRC) equivalents required to produce a fall in nitrogen concentration to 1/40th and is calculated using: (1) LCI=CEV/FRC, where CEV signifies the cumulative expired volume and (2) FRC=Vtracergas/(Cinitial–Cend), where Vtracergas signifies the total volume of expired tracer gas and Cinitial and Cend are the starting and final concentrations of tracer gas, respectively. This MBW was done during a provocation phase with histamine. The inset illustrates the alveolar slope phase III in nitrogen concentration versus expired volume for breaths 1 and 20 (scaled to mean nitrogen expired concentration), which is seen to increase from breath 1 to 20. The normalised phase III slopes are used in further analyses to compute Sacin and Scond. Figure reproduced with permission from Verbanck et al.67

This technique has been most widely used in patients with CF, for whom recent advances in treatment have strengthened the rationale for identifying early disease.60 In this setting, the most commonly used inhomogeneity index is the Lung Clearance Index (LCI).58 61 To measure the LCI, the washout begins at FRC and continues until the concentration of tracer gas (eg, nitrogen) has fallen to 1/40th of its original concentration. LCI is defined as the number of lung turnovers or FRC equivalents required to produce this fall in tracer gas concentration (see figure 2 for details). In adults or adolescents, a fixed tidal volume is typically used to minimise changes in the deadspace-to-tidal volume ratio, which may influence LCI independently of inhomogeneity. In younger children, this is usually not possible, and measurements are made during normal tidal breathing.

The LCI has attracted particular attention in children with CF, for whom it is easier than forced spirometry.61 62 LCI identified abnormal lung function more readily than either plethysmography or spirometry in children with CF (73% compared with 47% and 13%, respectively) and was higher in children with Pseudomonas colonisation.62 Similarly, LCI was more useful than spirometry for tracking CF progression over time in preschool patients63 and was more sensitive than FEV1 in detecting response to Ivacaftor in patients with mild lung disease.64 In contrast, in a group of 110 children with CF, LCI failed to detect significant effects of CFTR gene therapy, despite small rises in FEV1.65 Of note, however, LCI did identify a treatment effect in a patient subgroup with mild disease at baseline, in whom FEV1 did not change. This reinforces the likely role for LCI in patients with preserved spirometry, rather than those with established disease.

The LCI provides a global measure of ventilation inhomogeneity, but other MBW-derived indices such as Scond and Sacin, have been used to classify inhomogeneity as being either convection dependent (occurring in the conductive airways) or diffusion–convection dependent (occurring in the acinar airways), respectively. These indices are derived from a normalised nitrogen phase III slope analysis for each exhalation during the MBW66 67 (figure 2). In smokers, changes in Sacin and Scond predated changes in spirometry,68 and Scond showed sustained reversibility with smoking cessation.69 In COPD, Scond correlated with FEV1, while Sacin correlated with diffusing capacity.70 In asthma, inhomogeneity indices correlated with airway inflammation, severity and asthma control71–73 and have shown promise in predicting hyper-responsiveness and response to inhaled corticosteroids.74–76

Despite these promising results, MBW and its derived indices have limitations. As reflected in a recent European Respiratory Society/American Thoracic Society (ATS) consensus statement,58 work is needed to improve the standardisation and specification of equipment, procedures and software/analytical algorithms.77 78 In particular, some equipment is associated with considerable technical noise.79 Other limitations include: the dependence of the LCI on just two points from the washout for its derivation, the requirement for washouts to be performed in triplicate and the lack of consensus regarding standard ranges.58 77 Finally, the LCI cannot differentiate between structural damage, such as bronchiectasis, and airway narrowing due to mucus secretion. To date, these complexities have largely limited MBW to the research setting, other than in a few paediatric CF centres.

Multiple inert gas elimination technique (MIGET)

The MIGET was developed in the 1970s and measures the pulmonary exchange of six different inert gases.80 These gases are simultaneously intravenously infused and when in steady state within the lung, their concentrations are measured in mixed expired gas and arterial blood, using gas chromatography. The measurements are used with a mathematical model of the lung to compute ventilation–perfusion distributions that best explain the simultaneous exchange of these gases. As such, MIGET can quantify ventilation–perfusion (Embedded Image) inequalities and pulmonary shunts.

This technique has significantly advanced our understanding of pulmonary pathophysiology.81 In patients with COPD, MIGET identified three distinct patterns of Embedded Image distribution (figure 3).82 83 Patients with the so-called ‘pink puffer’ phenotype, for example, generally demonstrated large amounts of ventilation to high Embedded Image areas, with minimal ventilation of very low Embedded Image regions, presumably reflecting predominant emphysema and alveolar wall destruction. The correlation between Embedded Image distribution and clinical picture was less striking for other phenotypes, but MIGET clearly has the potential to identify important subpopulations.82 It also appears to be a sensitive marker of early disease in COPD, with substantial Embedded Image abnormalities reported in patients with only mild spirometric disease (GOLD stage 1).84 In asthma, asymptomatic patients with mild airway obstruction similarly had extensive Embedded Image mismatch using MIGET, with a distinct mode of low Embedded Image units.85 Furthermore, during acute exacerbations, Embedded Image inequality was unrelated to spirometric abnormality, suggesting that factors other than bronchoconstriction, such as small airway mucous plugging, make an important contribution to Embedded Image imbalance.86 87

Figure 3

Ventilation–perfusion (Embedded Image) ratio distributions obtained using the multiple inert elimination technique for a healthy individual in (A) and two patients with COPD in (B) and (C). In (A), the distributions for both ventilation (open circles) and perfusion (closed circles) are narrow, unimodal and centred around a Embedded Image ratio of 1. In (B), the ventilation distribution is bimodal with areas of high Embedded Image ratio. This pattern is predominantly seen in patients with the emphysematous-type of COPD, likely due to alveolar wall destruction. In (C), the perfusion distribution is bimodally shaped with areas of low Embedded Image. This pattern is predominantly seen in patients with bronchitis-type of COPD, likely due to mucus plugging in airways. There is a third mixed pattern (not shown here), which likely represents the presence of both types of pathologies. Figure reproduced with permission from West.83

Despite these important advantages over conventional tests, MIGET is rarely used in the clinical setting, primarily due to its complexity and invasive nature. It is currently available only in a few expert research centres.

Emerging techniques for assessing lung pathophysiology

Lung imaging

Quantitative CT

CT imaging has made an important contribution to the diagnosis and management of airways disease for many years. It allows the identification of emphysema and bronchiectasis in COPD and CF, respectively, and demonstrates significant expiratory gas trapping in asthma.88 89 Its advantages include widespread availability, excellent spatial and temporal resolution and rapid image acquisition. Important limitations include the lack of immediate accessibility in the clinic environment, the relatively subjective nature of reporting and the need for ionising radiation. On the latter point, low-dose and ultralow-dose CT scanning is emerging as a robust alternative to traditional protocols and may permit more regular and widespread use of CT in the future.90

In relation to subjectivity, the so-called ‘quantitative CT’ (QCT) is beginning to address this issue. This technique uses commercial or bespoke software to quantify, either at the lobar or the whole lung level, the extent of emphysema, based on voxel-by-voxel attenuation; the degree of bronchial wall thickening, based on the ratio of average lumen diameter to wall area in the small airways and the extent of gas trapping, based on differences between inspiratory and expiratory phase images.91–96

The extent of the correlation between QCT indices and conventional markers of disease severity appears variable,91 92 but importantly, QCT indices correlate independently with clinical outcomes including disease progression, mortality and symptoms.93 94 Furthermore, some radiological phenotypes appear to correspond well with particular genetic risk factors, raising the possibility that early CT might guide prognosis and management.95 Finally, there may be scope for combining CT with functional 18F-fluorodeoxyglucose positron emission tomography (FDG PET) imaging, which has shown some promise in the identification of local inflammation in patients with asthma,97 raising the prospect of ‘functional’ CT imaging of airways pathophysiology.

Magnetic resonance imaging

In contrast to CT, thoracic MRI has historically played a minor part in respiratory medicine, due to the lack of parenchymal resolution.98 However, functional MRI using inhaled hyperpolarised noble gases such as helium-3 (3He) and xenon-129 (129Xe) has recently gained momentum in the research setting. Based on the intensity of gas signal in each voxel during a relatively brief breath hold, the distribution of hyperpolarised gas can be quantified as the ‘percentage ventilated volume’, highlighting ventilation defects due to bronchoconstriction or airway inflammation, or the ‘apparent diffusion coefficient’ (ADC), which serves as an index of the extent of parenchymal destruction, for example, due to emphysema (figure 4).99

Figure 4

Images obtained using thoracic MRI, enhanced with hyperpolarised helium-3 (3He; right) and xenon-129 (129Xe; left), in two healthy volunteers and two patients with COPD. The figures show the distribution of the ‘apparent diffusion coefficient’ (ADC), a measure of parenchymal destruction. The images reveal low values and a uniform distribution for ADC in healthy volunteers, but significantly increased values with a markedly heterogenous distribution in patients with COPD. Hyperpolarised gas MRI also allows visualisation and quantitation of the distribution of static ventilation in patients without emphysema, in whom ventilation deficits reflect airway narrowing due to inflammation or bronchoconstriction (not shown here). Figure reproduced with permission from Mugler and Altes.99

In patients with asthma, hyperpolarised gas MRI indices of ventilation heterogeneity were increased compared with healthy volunteers and responded to treatment with bronchodilation or bronchial thermoplasty.100 In a recent study, while heterogeneity assessed by 3He MRI improved significantly after bronchodilation in patients with severe asthma, residual heterogeneity was greater in those with poorly-controlled eosinophilic disease.101 In CF, heterogeneity can be observed prior to changes in FEV1,102 while in COPD, there is variable correlation between hyperpolarised MRI parameters and conventional lung function measures.91 103

Overall, hyperpolarised gas MRI holds considerable promise as a means of non-invasively estimating lung heterogeneity, perhaps even allowing assessment of gas transfer98 and providing phenotypic information beyond simple correlation with existing parameters. However, important caveats include the limited availability in the clinical setting and high cost. These limitations may be overcome in the future through the use of more widely available contrast agents, for example, oxygen, in combination with standard proton MRI. A recent study in healthy volunteers reported good correlation between Embedded Image relationships derived from a single slice proton MRI and those derived from MIGET.104

Laser gas absorption spectroscopy and mathematical modeling of lung inhomogeneity

Evidence from MIGET, MBW and imaging studies supports the notion that measuring gas-exchange inhomogeneity within the lung may be useful in the diagnosis and management of airways diseases, as inhomogeneity measures are likely to be more sensitive to disease change. Nevertheless, these methodologies have yet to make a significant impact in the clinical setting.

Recently, a novel technology for highly precise in-airway gas analysis has been developed.105 This device accurately measures respiratory flows and uses laser absorption spectroscopy to assess the composition of respired gases with substantially greater precision than any other available device.105 This level of precision has facilitated the development of a novel mathematical model of gas exchange within the lung, which uses data from a nitrogen MBW (10 min air, 5 min oxygen) to recover parameters reflecting multiple aspects of gas-exchange inhomogeneity.106 Briefly, the model divides the lung into many (125) subunits, each with an equal fractional share of total alveolar volume (VA) at FRC, but a different fractional share of total lung compliance (CL), deadspace (VD) and pulmonary vascular conductance (Cd), and returns distributions for each of these properties, relative to volume, across the lung. The SD of these distributions, σVD:VA for the deadspace distribution and σCl:VA and σCd:VA for compliance and conductance, respectively, quantify the degree of inhomogeneity. Total alveolar volume (VAtot), total anatomical deadspace (VDtot) and shunt fraction are also calculated, and Embedded Image distributions similar to those derived from MIGET can be derived.106

This technique has important advantages over existing methods of measuring inhomogeneity. Unlike MIGET, it is non-invasive and simple to perform, both for the operator and for the patient and is appropriate for the outpatient clinic or lung function laboratory setting. In contrast to imaging methodologies, it does not use ionising radiation or require expensive scanners and reagents. In comparison with standard MBW techniques, it does not depend on the pattern of breathing and has significantly superior precision.105 For example, the typical bias in flow sensing between inspiration and expiration is an order of magnitude less (from ~5% to <0.2%) than with standard MBW equipment. It is this high precision that has made the novel approach to modelling inhomogeneity possible. Furthermore, the inhomogeneity indices obtained are computed using the whole gas-exchange data set (with datapoints every 10 ms), unlike conventional MBW-derived indices (eg, LCI, Scond and Sacin), which rely on parameterisation of the nitrogen washout profiles at specific time points. From a physiological perspective, the unique partitioning of inhomogeneity into variations in deadspace and compliance, which are intrinsic structural properties of the lung, has the potential to provide important biological insights. It may, for example, be particularly well placed to distinguish irreversible bronchiectasis (which may affect deadspace volume and variance) from reversible factors such as mucus plugging and airway inflammation (which may affect alveolar volume and the variance in lung compliance), or alternatively provide a novel sensitive marker of progressive emphysema, as suggested in preliminary studies.106

To date, it has been demonstrated that this technique can discriminate between healthy young volunteers, healthy elderly volunteers and patients with GOLD Stage 1–2 COPD (figure 5), with excellent repeatability.106 Preliminary results suggest that certain lung inhomogeneity markers may also be sensitive markers of disease in other conditions: compared with healthy controls, σCL:VA is abnormally high in patients with asthma but preserved spirometry107 and is also elevated in patients with CF and young healthy smokers with normal spirometry (Talbot et al, 2018: authors’ preliminary data). The latter observation suggests that σCL:VA may provide a useful marker for assessing small airway dysfunction or early disease in the ‘unobstructed smoker’.

Figure 5

Example distributions recovered by fitting the lung inhomogeneity model to the gas-exchange data from a nitrogen multiple breath inert gas washout (MBW) using the laser gas absorption spectroscopy device, for a healthy volunteer (A, C, E and G) and for a patient with GOLD Stage II COPD (B, D, F and H). Shown are distributions for: ventilation:perfusion ratios (Embedded Image) in (A) and (B); fractional compliance:volume ratios (CL:VA) in (C) and (D); fractional vascular conductance:volume ratios (Cd:VA) in (E) and (F) and scaled fractional deadspace:volume ratios (VD:VA) in (G) and (H) . Note that all distributions have larger variances in COPD and estimates for shunt and deadspace are significantly higher in the COPD patient. Figure reproduced with data from Mountain et al.106

These data suggest that this novel technique holds significant promise for the early detection of lung damage, disease stratification and monitoring of disease progression or regression. Larger studies are required to determine normative ranges in health and disease and to assess the potential to address specific clinically-relevant questions. As the technique is in its infancy, particular shortcomings are not yet clear; at present, limitations include a relatively long measurement time (15 min), a requirement for excellent nose and mouth seal throughout the test and an apparatus deadspace that is currently too large for use in young children. These limitations are likely to be surmountable with further engineering.

Conclusions: towards precision medicine

To improve outcomes in airways diseases and accelerate research and development of new disease-modifying drugs that can target ‘treatable traits’, we must improve our ability to phenotype/endotype patients and detect disease activity earlier so that we can evaluate interventions that potentially modify its trajectory and track disease activity (progression or regression) with greater sensitivity. This requires (a) improvements in physiological measurements of disease activity in the lung, such that tests are both sensitive to small changes and able to find patterns of abnormalities specific for particular airway or parenchymal pathology and (b) a recognition that different test modalities, for example, lung function, inflammatory/molecular phenotyping and imaging offer different insights into disease process. Appropriate integration of these test modalities will improve our ability to phenotype patients and allow us to provide the ‘right treatment to the right patient at the right time’.

References

Footnotes

  • NP and NPT contributed equally.

  • Contributors NP and NPT contributed equally to this manuscript. All authors were responsible for the conception and design of the work. NP and NPT drafted the manuscript. All authors contributed significantly to revising the work and approved the final version published.

  • Funding This work was supported by the NIHR Biomedical Research Centre, Oxford. NPT is supported by a NIHR Academic Clinical Lectureship

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.