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TOPICAL REVIEW
Ahead of print publication  

Recent advances in quantitative computerized tomography and home spirometry for diagnosing and monitoring of interstitial lung disease associated with connective tissue diseases: A narrative review


1 Department of Rheumatology, Ramóny Cajal University Hospital, Madrid, Spain
2 Department of Respiratory, Recoletas Campo Grande Hospital, Valladolid, Spain

Date of Submission18-Nov-2020
Date of Acceptance25-Mar-2021

Correspondence Address:
Jesus Loarce-Martos,
Department of Rheumatology, Ramony Cajal University Hospital, 28034, Madrid
Spain
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/injr.injr_304_20

  Abstract 


Interstitial lung disease (ILD) is one of the main causes of morbidity and mortality in patients with connective tissue diseases (CTDs), and it remains a challenge for rheumatologists in terms of diagnosis and monitoring. Although various imaging techniques and functional and laboratory tests have been used for identifying and assessing progression in ILD, high-resolution computerized tomography and pulmonary function tests remain the main tools for this purpose. Several advances have been developed in the past years, including automated analysis and quantification of lung abnormalities in chest computerized tomography and portable spirometry, which may improve the diagnosis and follow-up of these patients. The aim of this study is to review recent advances in quantitative computerized tomography of lung and portable spirometry in ILD associated with CTDs.

Keywords: Chest computed tomography, connective tissue disease, interstitial lung disease, respiratory function tests, systemic sclerosis



How to cite this URL:
Loarce-Martos J, Leon-Roman FX, Garrote-Corral S. Recent advances in quantitative computerized tomography and home spirometry for diagnosing and monitoring of interstitial lung disease associated with connective tissue diseases: A narrative review. Indian J Rheumatol [Epub ahead of print] [cited 2021 Dec 9]. Available from: https://www.indianjrheumatol.com/preprintarticle.asp?id=328980




  Introduction Top


Interstitial lung diseases (ILDs) are a heterogeneous group of disorders characterized by the inflammation and fibrosis of the lungs, including the interstitium, distal airways, and alveoli. The etiology of these diseases includes disorders of unknown cause (idiopathic interstitial pneumonia) and others with known causes (such as connective tissue disease [CTD]-associated ILD [CTD-ILD], related to occupational and environmental agents, or related to drug or radiation exposure). Among these, CTDs are one of the most common causes of ILD, including diseases such as rheumatoid arthritis (RA), Sjögren syndrome (SjS), systemic sclerosis (SSc), systemic lupus erythematosus, or idiopathic inflammatory myopathy. ILD affects a significant proportion of these patients, and it is associated with substantial morbidity and mortality.[1],[2]

Besides, CTD-ILD remains a challenge for the rheumatologist in terms of diagnosis, prognostic stratification, and treatment. In the past years, several tools have been used for diagnosis and monitoring of disease progression, such as new imaging techniques (lung ultrasound, positron emission tomography, or magnetic resonance) and novel serum biomarkers such as Krebs von den Lungen-6 or surfactant protein A/D.[3] Nevertheless, lung imaging with high-resolution computerized tomography (HRCT) and pulmonary function tests (PFTs) remain the main tools for this purpose.[4],[5],[6],[7],[8] There have been some advances in HRCT imaging analysis and new developments in PFTs in recent years. Quantitative analysis has been applied to HRCT to improve diagnosis and monitoring of disease progression in ILD.[9],[10] In clinical practice with regard to CTD-ILD, PFTs are essential for assessing severity and prognosis in these patients. Recent advances in this field have focused on performing portable spirometry, as it allows for more frequent monitoring and avoiding the need to go to the hospital to perform these measurements.[11]

The aim of this narrative review is to evaluate the recent evidence on quantitative analysis of CT (QCT) and portable PFT in diagnosing and monitoring of CTD-ILD.


  Computerized Tomography Top


HRCT is the preferred imaging tool for the assessment of ILD, with regard to classification, diagnosis, and monitoring.[7] Although changes in HRCT are able to predict pulmonary function decline and mortality,[12] several issues remain in quantifying pathologic features. Semi-quantitative analysis (semiQA) has been used to quantify and characterize ILD, with several scores that are calculated based on visual analysis performed by radiologists.[13] Nevertheless, there is no standard method; this approach is time-consuming and susceptible to disagreement even among experts.[14] QCT has been developed and applied in ILD, providing automated quantification of lung parameters and reducing intra- and interobserver variability. There are several QCT methods for data analysis that had been used in ILD, such as density analysis, texture analysis, and several software and machine learning approaches.[9],[10]

Density analysis is based on measuring voxel density without assessing morphology or spatial relationship between voxels to characterize textural features. Threshold-based analysis is applied by counting the number of voxels above or below a certain attenuation value, to determine a relative or absolute volume. For example, this has been applied to define the extent and severity of emphysema, because emphysematous lung is characterized by a lower lung density in CT. Another example would be the quantification of high-attenuation areas (HAAs), which are areas of increased density that are likely to correspond with ILD. Nevertheless, threshold-based analysis does not assess morphology, and cannot differentiate with other pathologies with similar attenuation values, such as emphysema, cysts, or air trapping. Histogram-based analysis assesses the density of the voxels in a region of interest (ROI) or the whole lung, displaying it in a histogram with a certain mean (mean lung attenuation [MLA]), kurtosis, skew, and standard deviation. These histogram curves can differentiate certain diseases from normal lung. For example, emphysema, as is characterized by abnormal lucent parenchyma, results in decreased MLA and a left-skewed curve. ILD, as is characterized by fibrosis and increased lung density, results in augmented MLA and in a right-skewed curve.

Texture analysis does consider the spatial relationship between voxels and/or ROIs and is able to characterize different morphological features, using methods such as run-length matrices, fractal measures, or gray-level co-occurrence matrices.

There are several tools and machine learning techniques that have used density and texture analysis in patients with ILD,[15] such as Computer-Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER). CALIPER is a computer software that uses three-dimensional CT scans to automatically quantify interstitial lung anomalies such as ground glass, reticular opacities, honeycombing, HAAs, and vessel-related structures.[16]

Interstitial lung disease associated with connective tissue disease

With regard to QCT application in CTD-ILD, most of the studies have been performed in SSc [Table 1] and [Table 2].
Table 1: Quantitative computerized tomography in systemic sclerosis (study design and objectives)

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Table 2: Quantitative computerized tomography in systemic sclerosis (results)

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Saldana et al.[17] studied QCT through density-based measurements in patients with SSc, performed at baseline and follow-up. QCT parameters correlated with PFT and semiQA at baseline and at follow-up. Correlation with mortality was obtained in unadjusted analysis, but not in multivariable analysis. Carvalho et al.[20] used density analysis in HRCT to calculate ILD extent in patients with SSc and healthy controls without lung disease. QCT had good sensitivity and specificity for identifying SSc-ILD, and ILD extension correlated with forced vital capacity (FVC) and carbon monoxide diffusion (DLCO). Bocchino et al.[21] used histogram analysis to develop a computerized integrated index (CII) for diagnosing ILD in SSc patients. This CII had a good sensitivity for identifying ILD in SSc patients, and correlated with PFT at baseline and DLCO decline at 1 year of follow-up. Ufuk et al.[22] studied QCT in SSc patients using six different density-based methods (threshold analysis and histogram analysis). Overall, these methods had a close correlation between them. They also had a good correlation with semiQA scoring and PFT, and a good discrimination between limited and extensive ILD.

Other studies evaluated texture analysis for quantifying SSc-ILD. Occhipinti et al.[16] studied quantitative analysis of lung parenchyma in SSc patients through texture analysis using CALIPER, and compared it with semiQA and PFT, all performed at baseline and follow-up. At baseline, QCT scores correlated with semiQA and PFT parameters. At follow-up, HRCT and PFT were repeated. Variation in total lung volume (LV) determined by QCT between baseline and follow-up was predictive of progression according to PFT. Ferrazza et al.[23] used CALIPER to characterize and quantify normal lung parenchyma and lung abnormalities at baseline, and also evaluated correlation with lung function at 12 months of follow-up. They found the percentage of reticular pattern correlated with PFT parameters at baseline, and it also correlated with DLCO decline at follow-up.

Milanese et al.[24] used a complex approach to study QCT in SSc patients. Their objective was dual, as they aimed to evaluate if texture analysis was useful to correctly classify SSc-associated ILD versus other types of lung disease, and they also tested if different reconstruction algorithms and radiation doses impact QCT results. After applying texture analysis, they used an artificial neural network (ANN) to classify SSc-associated ILD versus other types of lung disease. Using the different reconstruction algorithms and radiation doses, the ANN was able to correctly classify most of the patients.

A different approach was taken by Occhipinti et al.,[25] as they aimed to study the relationship between vascular measures (pulmonary vascular volume [PVV] and the ratio of PVV with LV) with different lung patterns, as calculated by CALIPER (the same methodology used in their previous work[16]). They found that vascular CT metrics had an inverse correlation with functional parameters and a positive correlation with various clinical and laboratory parameters. Interestingly, the pulmonary volumes were more increased in areas not affected by ILD, suggesting redistribution of blood flow in healthy lung parenchyma.

With regard to QTC in other CTD-ILD, recent studies have been published in patients with SjS and RA [Table 3] and [Table 4].
Table 3: Quantitative computerized tomography in Sjögren syndrome and rheumatoid arthritis (study design and objectives)

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Table 4: Quantitative computerized tomography in Sjögren syndrome and rheumatoid arthritis (results)

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Guisado-Vasco et al.[26] studied QCT in patients with SjS. They applied histogram analysis in a cohort of 102 patients with primary or secondary SjS, and they also performed semiQA scores (Goh et al 4, Taouli et al).[27] Almost all QCT indices had a moderate-to-good correlation with semiQA and PFT (FVC and DLCO). QCT indices had a good discrimination between SjS-ILD and SjS without ILD.

Ufuk et al.[28] used density analysis (threshold-based and histogram-based) in 34 SjS patients using different ILD definitions (3 different methods). QCT showed a good correlation with semiQA and was able to distinguish between limited and extensive ILD. Reproducibility was almost perfect for each method that was used. Jacob et al.[29] aimed to identify patients with RA-ILD and a progressive fibrotic phenotype similar to idiopathic pulmonary fibrosis (IPF). In this retrospective study, they included 157 patients with RA-ILD (and 179 patients with IPF as controls) and evaluated survival at 3 and 6 years of follow-up. QCT was able to predict mortality, even when analyzed alongside the other visual staging systems.


  Pulmonary Function Tests Top


Given the current SARS-CoV-2 pandemic, it is challenging to perform respiratory function tests in patients with ILD. Specialists have recommended home monitoring by video consultation and spirometry. In recent years, home spirometry has been applied for assessing progression of ILD. Previous studies in IPF revealed that daily home spirometry could be an effective tool to assess ILD progression. It also provides relevant clinical information and can be performed by the majority of patients.[31]

In a study published in 2018, Moor et al.[32] studied PFT through a Bluetooth-enabled spirometer in ten patients with IPF. They demonstrated that wireless home spirometry could detect real-time changes in FVC in patients with IPF. Measurements of FVC and FEV1 with portable spirometry and hospital spirometry had a high correlation. The home monitoring program was able to facilitate personalized attention and was highly valued by the patients. Marcoux et al.[33] reported the usefulness of portable spirometry in IPF. Twenty-two patients were enrolled, and they performed home spirometry and laboratory-based spirometry at baseline and every 4 weeks for 12 weeks. Correlation between handheld spirometry and laboratory-based spirometry at baseline, 4, 8, and 12 weeks was high (0.97, 0.96, 0.93, and 0.90, respectively, using Spearman correlation). Moor et al.[34] performed a nonblinded, multicenter randomized controlled trial investigating a home-monitoring program, which included portable spirometry. The correlation between home spirometry and hospital spirometry was very strong, with low variability within-patient. Maher et al.[35] compared home spirometry with clinic spirometry at baseline and follow-up in 346 patients with IPF. Although strong correlation was observed for FVC measurements at all time points, correlation rates of FVC decline between both techniques were weak.

Home spirometry has been used in ILDs other than IPF. Veit et al.[36] studied home spirometry using a handheld spirometer in 47 patients (20 with IPF and 27 with non-IPF, including 10 patients with CTD-ILD). They obtained a strong correlation between hospital FVC and home FVC (measured at baseline, 3 months, and 6 months). Interestingly, they observed that some patients had a relatively high variability in daily FVC, and this variability was higher in patients with progressive ILD compared to stable ILD (median FVC coefficient of variation 8.6% vs. 4.8%). Nevertheless, 7/47 patients (14.9%) were not included in the analysis as they were unable to perform daily spirometry or because their measurements were of low quality. Maher et al.[37] performed a randomized, double-blinded, multicenter Phase II clinical trial to assess the efficacy and safety of pirfenidone in patients with progressive fibrosing unclassifiable ILD. Their primary endpoint was mean change in FVC from baseline over 24 weeks measured by daily home spirometry, using a portable handheld spirometer. They used a spirometer-based algorithm to categorize blows as accepted, borderline accepted, or rejected, based on their quality. Surprisingly, analysis of their primary outcome could not be performed due to issues related to technical reliability and the impossibility to perform prespecified statistical analyses. There are other studies going on, for example, the STARLINER study,[38] that will assess daily home spirometry and accelerometry in patients with suspected ILD.

Finally, recently published articles in 2020 highlight the importance of home monitoring of patients with ILDs during the SARS-CoV-2 pandemic, addressing the safety of home monitoring and thus avoiding face-to-face consultation during the pandemic.[11] Monitoring of several chronic respiratory diseases can be carried out using the electronic portable spirometer. Furthermore, by using the portable spirometer, the information can be downloaded to computers or personal mobile devices, facilitating the transmission of the information to health-care professionals.[39] Other technologies, such as wrist-worn activity trackers or smartphone spirometry, could also be used to measure the level of daily physical activity in patients with ILD, and require further investigation.[33]


  Conclusion Top


There have been advances in the diagnosis and monitoring of CTD-ILD. Quantitative computerized tomography has proven to be useful in diagnosing ILD in patients with CTD, differentiating CTD-ILD versus other types of lung disease and providing additional information compared to visual CT analysis. This technique provides automated CT evaluation, eliminating inter- and intra-reader variation, and may allow a more precise and efficient assessment of this group of patients. Nevertheless, QCT does require standardized CT protocols, CT image data handling, and specific software analysis to extract the CT metrics, and requires further validation and methodology standardization before being used in clinical practice. Portable spirometry has proven to be correlated with hospital-based PFT in patients with ILD, allowing for frequent monitoring and early detection of lung function decline. Some issues have arisen in recent studies, with regard to adherence, reliability, and the need for an adequate technique to perform home spirometry. Besides the need for further validation and refinement of these techniques, portable PFTs require changes in hospital organization and a specific training program for patients and health-care professionals. Once these barriers have been overcome, it may be implemented in home monitoring programs, improving patient care, and decreasing the need for such tests in the hospital.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Wijsenbeek M, Cottin V. Spectrum of fibrotic lung diseases. N Engl J Med 2020;383:958-68.  Back to cited text no. 1
    
2.
Mira-Avendano I, Abril A, Burger CD, Dellaripa PF, Fischer A, Gotway MB, et al. Interstitial lung disease and other pulmonary manifestations in connective tissue diseases. Mayo Clin Proc 2019;94:309-25.  Back to cited text no. 2
    
3.
Jee AS, Sahhar J, Youssef P, Bleasel J, Adelstein S, Nguyen M, et al. Review: Serum biomarkers in idiopathic pulmonary fibrosis and systemic sclerosis associated interstitial lung disease – Frontiers and horizons. Pharmacol Therap 2019;202:40-52.  Back to cited text no. 3
    
4.
Goh NS, Desai SR, Veeraraghavan S, Hansell DM, Copley SJ, Maher TM, et al. Interstitial lung disease in systemic sclerosis: A simple staging system. Am J Respir Crit Care Med 2008;177:1248-54.  Back to cited text no. 4
    
5.
Zamora-Legoff JA, Krause ML, Crowson CS, Ryu JH, Matteson EL. Patterns of interstitial lung disease and mortality in rheumatoid arthritis. Rheumatol (United Kingdom) 2017;56:344-50.  Back to cited text no. 5
    
6.
Chartrand S, Lee JS, Swigris JJ, Stanchev L, Fischer A. Clinical characteristics and natural history of autoimmune forms of interstitial lung disease: A single-center experience. Lung 2019;197:709-13.  Back to cited text no. 6
    
7.
Hoffmann-Vold AM, Maher TM, Philpot EE, Ashrafzadeh A, Barake R, Barsotti S, et al. The identification and management of interstitial lung disease in systemic sclerosis: Evidence-based European consensus statements. Lancet Rheumatol 2020;2:e71-83.  Back to cited text no. 7
    
8.
Showalter K, Hoffmann A, Rouleau G, Aaby D, Lee J, Richardson C, et al. Performance of forced vital capacity and lung diffusion cutpoints for associated radiographic interstitial lung disease in systemic sclerosis. J Rheumatol 2018;45:1572-6.  Back to cited text no. 8
    
9.
Newell JD Jr., Tschirren J, Peterson S, Beinlich M, Sieren J. Quantitative CT of interstitial lung disease. Semin Roentgenol 2019;54:73-9.  Back to cited text no. 9
    
10.
Weatherley ND, Eaden JA, Stewart NJ, Bartholmai BJ, Swift AJ, Bianchi SM, et al. Experimental and quantitative imaging techniques in interstitial lung disease. Thorax 2019;74:611-9.  Back to cited text no. 10
    
11.
Nakshbandi G, Moor CC, Wijsenbeek MS. Home monitoring for patients with ILD and the COVID-19 pandemic. Lancet Respir Med 2020;8:1172-4.  Back to cited text no. 11
    
12.
Moore OA, Goh N, Corte T, Rouse H, Hennessy O, Thakkar V, et al. Extent of disease on high-resolution computed tomography lung is a predictor of decline and mortality in systemic sclerosis-related interstitial lung disease. Rheumatology (Oxford) 2013;52:155-60.  Back to cited text no. 12
    
13.
Assayag D, Kaduri S, Hudson M. High resolution computed tomography scoring systems for evaluating interstitial lung disease in systemic sclerosis patients. Rheumatol Curr Res 2012;1:1-6.  Back to cited text no. 13
    
14.
Watadani T, Sakai F, Johkoh T, Noma S, Akira M, Fujimoto K, et al. Interobserver variability in the CT assessment of honeycombing in the lungs. Radiology 2013;266:936-44.  Back to cited text no. 14
    
15.
Walsh SL, Humphries SM, Wells AU, Brown KK. Imaging research in fibrotic lung disease; applying deep learning to unsolved problems. Lancet Respir Med 2020;8:1144-53.  Back to cited text no. 15
    
16.
Occhipinti M, Bosello S, Sisti LG, Cicchetti G, de Waure C, Pirronti T, et al. Quantitative and semi-quantitative computed tomography analysis of interstitial lung disease associated with systemic sclerosis: A longitudinal evaluation of pulmonary parenchyma and vessels. PLoS One 2019;14:e0213444.  Back to cited text no. 16
    
17.
Saldana DC, Hague CJ, Murphy D, Coxson HO, Tschirren J, Peterson S, et al. Association of computed tomography densitometry with disease severity, functional decline, and survival in systemic sclerosis-associated interstitial lung disease. Ann Am Thorac Soc 2020;17:813-20.  Back to cited text no. 17
    
18.
Ryerson CJ, Vittinghoff E, Ley B, Lee JS, Mooney JJ, Jones KD, et al. Predicting survival across chronic interstitial lung disease: The ILD-GAP model. Chest 2014;145:723-8.  Back to cited text no. 18
    
19.
Morisset J, Vittinghoff E, Elicker BM, Hu X, Le S, Ryu JH, et al. Mortality risk prediction in scleroderma-related interstitial lung disease: The SADL model. Chest 2017;152:999-1007.  Back to cited text no. 19
    
20.
Carvalho AR, Guimarães AR, Sztajnbok FR, Rodrigues RS, Silva BR, Lopes AJ, et al. Automatic quantification of interstitial lung disease from chest computed tomography in systemic sclerosis. Front Med (Lausanne) 2020;7:577739.  Back to cited text no. 20
    
21.
Bocchino M, Bruzzese D, D'Alto M, Argiento P, Borgia A, Capaccio A, et al. Performance of a new quantitative computed tomography index for interstitial lung disease assessment in systemic sclerosis. Sci Rep 2019;9:9468.  Back to cited text no. 21
    
22.
Ufuk F, Demirci M, Altinisik G. Quantitative computed tomography assessment for systemic sclerosis-related interstitial lung disease: Comparison of different methods. Eur Radiol 2020;30:4369-80.  Back to cited text no. 22
    
23.
Ferrazza AM, Gigante A, Gasperini ML, Ammendola RM, Paone G, Carbone I, et al. Assessment of interstitial lung disease in systemic sclerosis using the quantitative CT algorithm CALIPER. Clin Rheumatol 2020;39:1537-42.  Back to cited text no. 23
    
24.
Milanese G, Mannil M, Martini K, Maurer B, Alkadhi H, Frauenfelder T. Quantitative CT texture analysis for diagnosing systemic sclerosis: Effect of iterative reconstructions and radiation doses. Medicine (Baltimore) 2019;98:e16423.  Back to cited text no. 24
    
25.
Occhipinti M, Bruni C, Camiciottoli G, Bartolucci M, Bellando-Randone S, Bassetto A, et al. Quantitative analysis of pulmonary vasculature in systemic sclerosis at spirometry-gated chest CT. Ann Rheum Dis 2020;79:1210-7.  Back to cited text no. 25
    
26.
Guisado-Vasco P, Silva M, Duarte-Millán MA, Sambataro G, Bertolazzi C, Pavone M, et al. Quantitative assessment of interstitial lung disease in Sjögren's syndrome. PLoS One 2019;14:e0224772.  Back to cited text no. 26
    
27.
Taouli B, Brauner MW, Mourey I, Lemouchi D, Grenier PA. Thin-section chest CT findings of primary Sjögren's syndrome: Correlation with pulmonary function. Eur Radiol 2002;12:1504-11.  Back to cited text no. 27
    
28.
Ufuk F, Demirci M, Altinisik G, Karasu U. Quantitative analysis of Sjogren's syndrome related interstitial lung disease with different methods. Eur J Radiol 2020;128:109030.  Back to cited text no. 28
    
29.
Jacob J, Hirani N, Van Moorsel CHM, Rajagopalan S, Murchison JT, Van Es HW, et al. Predicting outcomes in rheumatoid arthritis related interstitial lung disease. Eur Respir J 2019;53:1800869.  Back to cited text no. 29
    
30.
Lynch DA, Sverzellati N, Travis WD, Brown KK, Colby TV, Galvin JR, et al. Diagnostic criteria for idiopathic pulmonary fibrosis: A Fleischner Society White Paper. Lancet Respir Med 2018;6:138-53.  Back to cited text no. 30
    
31.
Russell AM, Adamali H, Molyneaux PL, Lukey PT, Marshall RP, Renzoni EA, et al. Daily home spirometry: An effective tool for detecting progression in idiopathic pulmonary fibrosis. Am J Respir Crit Care Med 2016;194:989-97.  Back to cited text no. 31
    
32.
Moor CC, Wapenaar M, Miedema JR, Geelhoed JJ, Chandoesing PP, Wijsenbeek MS. A home monitoring program including real-time wireless home spirometry in idiopathic pulmonary fibrosis: A pilot study on experiences and barriers. Respir Res 2018;19:105.  Back to cited text no. 32
    
33.
Marcoux V, Wang M, Burgoyne SJ, Fell CD, Ryerson CJ, Sajobi TT, et al. Mobile health monitoring in patients with idiopathic pulmonary fibrosis. Ann Am Thorac Soc 2019;16:1327-9.  Back to cited text no. 33
    
34.
Moor CC, Mostard RL, Grutters JC, Bresser P, Aerts JG, Chavannes NH, et al. Home monitoring in patients with idiopathic pulmonary fibrosis. A randomized controlled trial. Am J Respir Crit Care Med 2020;202:393-401.  Back to cited text no. 34
    
35.
Maher T, Cottin V, Russell AM, Corte T, Hammerl P, Michael A, et al. Correlation between home and clinic spirometry in subjects with IPF: Results from the INMARK trial. Eur Respir J Eur Respir Soc 2019; 54: Suppl. 63, PA1318.  Back to cited text no. 35
    
36.
Veit T, Barnikel M, Crispin A, Kneidinger N, Ceelen F, Arnold P, et al. Variability of forced vital capacity in progressive interstitial lung disease: A prospective observational study. Respir Res 2020;21:270.  Back to cited text no. 36
    
37.
Maher TM, Corte TJ, Fischer A, Kreuter M, Lederer DJ, Molina-Molina M, et al. Pirfenidone in patients with unclassifiable progressive fibrosing interstitial lung disease: A double-blind, randomised, placebo-controlled, phase 2 trial. Lancet Respir Med 2020;8:147-57.  Back to cited text no. 37
    
38.
Wijsenbeek M, Bendstrup E, Valenzuela C, Henry MT, Moor C, Bengus M, et al. Design of a study assessing disease behaviour during the peri-diagnostic period in patients with interstitial lung disease: The STARLINER study. Adv Ther 2019;36:232-43.  Back to cited text no. 38
    
39.
Kouri A, Gupta S, Yadollahi A, Ryan CM, Gershon AS, To T, et al. Addressing reduced laboratory-based pulmonary function testing during a pandemic. Chest 2020;158:2502-10.  Back to cited text no. 39
    



 
 
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