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The Prognostic Value of the GAP Model In Chronic Interstitial Lung DiseaseStaging in Chronic Interstitial Lung Disease: The Quest for a Staging System FREE TO VIEW

Athol U. Wells, MD; Katerina M. Antoniou, MD
Author and Funding Information

From the Interstitial Lung Disease Unit (Dr Wells), Royal Brompton Hospital, Royal Brompton & Harefield NHS Foundation Trust; and Department of Thoracic Medicine & Laboratory of Cellular and Molecular Pneumonology (Dr Antoniou), Medical School, University of Crete

Correspondence to: Athol U. Wells, MD, Emmanuel Kaye Bldg, Manresa Rd, Chelsea, London SW3 6LR, England; e-mail: athol.wells@rbht.nhs.uk


Financial/nonfinancial disclosures: The authors have reported to CHEST that no potential conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article.

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians. See online for more details.


Chest. 2014;145(4):672-674. doi:10.1378/chest.13-2908
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The validation and use of staging in interstitial lung disease (ILD) is long overdue. Treatment decisions made by clinicians are dichotomous: essentially, to introduce a treatment or to enroll a patient in a treatment trial. The data on which such decisions tend to be made are continuous (eg, measured FVC values), which immediately poses the question of which severity thresholds should be used to identify patients at higher risk of disease progression. In idiopathic pulmonary fibrosis (IPF), staging systems have been based on a combination of the composite physiologic index and 6-min walk data1 and on the integration of spirometric volumes, gas transfer levels, age, and sex (the GAP model).2 In non-IPF chronic ILD, the evaluation of high-resolution CT (HRCT) scanning has been combined with pulmonary function tests in staging ILD in systemic sclerosis,3 other forms of connective tissue disease,4 and sarcoidosis.5 However, until now, informative severity thresholds have been disease-specific and, by virtue of this fact, are less likely to be applied with confidence in mixed populations of patients with ILD.

In this issue of CHEST (see page 723), Ryerson and colleagues6 have applied the GAP model to large patient subgroups with IPF, chronic hypersensitivity pneumonitis, a combined subgroup with idiopathic nonspecific interstitial pneumonia (NSIP) or connective tissue disease-associated ILD (CTD-ILD), and unclassifiable disease. Their findings are striking. If one is moved by the symmetry of numbers, Table 2 in their article is a thing of beauty. Observed mortality across four chronic diseases is remarkably similar at three time points (1, 2, and 3 years) for all four GAP subdivisions and differs little from mortality predicted at baseline by the GAP model. Surprisingly, given that the model was validated exclusively in IPF, severity-based mortality distinctions were comparable in IPF and the other disease groups. The similarity between IPF and other diseases in overall mortality reflects the use of a disease-subtype variable to account for lower survival in IPF. However, the data also suggest that the major historical differences in survival between IPF and other disorders reflect, in part, later presentation in IPF, underlining the importance of achieving earlier diagnosis in that disease.7

Acknowledging that the study is retrospective, the findings appear robust. To power prognostic analyses, it was necessary for the authors to amalgamate idiopathic NSIP and CTD-ILD into a single patient subgroup. This decision can be questioned as in CTD-ILD, the highest mortality is associated with an HRCT scan or histologic pattern of usual interstitial pneumonia in rheumatoid arthritis.8,9 However, the findings speak for themselves: The distinction between NSIP and CTD-ILD carried no independent prognostic value. Indeed, in clinical practice, a subgroup of patients with underlying and often undiagnosed usual interstitial pneumonia exists in all three non-IPF groups. The authors highlight the fact that the prognostic accuracy of the GAP model is slightly lower in IPF and in CTD-ILD but this is only to be expected, given the significant prevalence of unpredictable fatal acute exacerbations in IPF10 and both systemic disease and disproportionate pulmonary hypertension in CTD-ILD. It must also be accepted that rigorous adjustment for treatment cannot be achieved over a period of several years, due to the multiplicity of therapeutic interventions made during follow-up, including decisions to observe without treatment in some cases, to reduce or withdraw treatment in stable disease, and the need to adapt treatment to side effects and according to patient wishes. These important caveats should be addressed in future prospective work but the strength of the findings is, nonetheless, remarkable.

As Ryerson and colleagues6 point out, their findings provide broad prognostic distinctions in large patient cohorts but have not yet been shown to quantify risk reliably in individual patients, such that staging to inform treatment decisions across chronic ILD is within our grasp. The GAP model is largely based on predefined FVC and diffusing capacity of lung for carbon monoxide thresholds, which have their own limitations. The normal pulmonary function range is a significant confounder. Prior to the development of disease,11 normal pulmonary function tests range from 80% to 120% of values predicted from age, sex, and height. An FVC threshold of 75% predicted, for example, represents a decline of between 5% and 45% depending upon premorbid values, a ninefold variability. It is very likely that HRCT scan evaluation would usefully refine FVC and diffusing capacity of lung for carbon monoxide thresholds by identifying patients in whom measured values are misleading.

Irrespective of this limitation, the critical importance of evaluating the intrinsic progressiveness of disease in individual patients cannot be overemphasized. Thresholds for the severity of disease do not, in isolation, meet this need. Biomarker data should, in principle, identify patients at higher risk of progression, irrespective of disease severity, but to date, despite promising findings in IPF and systemic sclerosis, no current biomarker is routinely fit for clinical purpose. It appears intuitively likely, if it is accepted that changes in pathogenesis occur as disease progresses, that accurate prognostication will require the integration of severity and biomarker data. For example, in the most compelling IPF biomarker study to date, four of five serum proteins that were found to predict the course of disease were discarded when severity information was incorporated as an independent prognostic determinant in a final model.11 Short-term changes in disease severity also add substantially to prognostic evaluation. In a pharmaceutical cohort, outcome in IPF was linked equally to the baseline severity of disease and events in the next year, including decline in FVC and hospital admissions.12 As clinicians who look after patients with IPF and other chronic ILDs know, the likely prognosis, in IPF and other chronic ILOs, can change radically during early follow-up, especially when progression, despite treatment, is apparent.

In the search for a staging system that informs management across chronic ILD, the observations of Ryerson and colleagues6 are best viewed as providing a severity scaffold on which to build a definitive staging system, also incorporating biomarker information and short-term changes in disease severity. However, in one important regard, the findings are of immediate value in clinical practice. The problem of unclassifiable disease has been underrecognized in the medical literature. The San Francisco group has recently reported that the proportion of unclassifiable disease in patients with chronic ILD exceeds 10%.13 In recognition of the fact that no evidence base exists for management, the American Thoracic Society/European Respiratory Society committee updating the classification of the idiopathic interstitial pneumonias has proposed that management decisions in unclassifiable disease should be informed by a disease behavior classification, which includes the evaluation of disease severity.14 Knowledge that the GAP model provides parallel outcome distinctions across chronic ILD, including unclassifiable disease, provides clinicians with a means of applying the American Thoracic Society/European Respiratory Society disease behavior classification with greater confidence.

Most important initiatives have small beginnings. The findings of Ryerson and colleagues6 do not give us a fully formed staging system in chronic ILD but they do at least make clear that this goal is likely to be attainable.

References

Mura M, Porretta MA, Bargagli E, et al. Predicting survival in newly diagnosed idiopathic pulmonary fibrosis: a 3-year prospective study. Eur Respir J. 2012;40(1):101-109. [CrossRef]
 
Ley B, Ryerson CJ, Vittinghoff E, et al. A multidimensional index and staging system for idiopathic pulmonary fibrosis. Ann Intern Med. 2012;156(10):684-691. [CrossRef]
 
Goh NS, Desai SR, Veeraraghavan S, et al. Interstitial lung disease in systemic sclerosis: a simple staging system. Am J Respir Crit Care Med. 2008;177(11):1248-1254. [CrossRef]
 
Walsh SL, Sverzellati N, Devaraj A, Keir GJ, Wells AU, Hansell DM. Connective tissue disease related fibrotic lung disease: high resolution computed tomographic and pulmonary function indices as prognostic determinants [published online ahead of print November 11, 2013]. Thorax. doi:10.1136/thoraxjnl-2013-203843.
 
Walsh SLF, Wells AU, Sverzellati N, et al. An integrated clinicoradiological staging system for pulmonary sarcoidosis: a case-cohort study. Lancet Respir Med. 2014;2(2):123-130. [CrossRef]
 
Ryerson CJ, Vittinghoff E, Ley B, et al. Predicting survival across chronic interstitial lung disease: the ILD-GAP model. Chest. 2014;145(4):723-728.
 
Cordier JF, Cottin V. Neglected evidence in idiopathic pulmonary fibrosis: from history to earlier diagnosis. Eur Respir J. 2013;42(4):916-923. [CrossRef]
 
Kim EJ, Elicker BM, Maldonado F, et al. Usual interstitial pneumonia in rheumatoid arthritis-associated interstitial lung disease. Eur Respir J. 2010;35(6):1322-1328. [CrossRef]
 
Park JH, Kim DS, Park IN, et al. Prognosis of fibrotic interstitial pneumonia: idiopathic versus collagen vascular disease-related subtypes. Am J Respir Crit Care Med. 2007;175(7):705-711. [CrossRef]
 
Antoniou KM, Wells AU. Acute exacerbations of idiopathic pulmonary fibrosis. Respiration. 2013;86(4):265-274. [CrossRef]
 
Richards TJ, Kaminski N, Gibson KF. Plasma proteins for risk prediction in idiopathic pulmonary fibrosis. Am J Respir CritCare Med. 2012;185(12):1329-1330. [CrossRef]
 
du Bois RM, Weycker D, Albera C, et al. Ascertainment of individual risk of mortality for patients with idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 2011;184(4):459-466. [CrossRef]
 
Ryerson CJ, Urbania TH, Richeldi L, et al. Prevalence and prognosis of unclassifiable interstitial lung disease. Eur Respir J. 2013;42(3):750-757. [CrossRef]
 
Travis WD, Costabel U, Hansell DM, et al; ATS/ERS Committee on Idiopathic Interstitial Pneumonias. An official American Thoracic Society/European Respiratory Society statement: update of the international multidisciplinary classification of the idiopathic interstitial pneumonias. Am JRespir Crit Care Med. 2013;188(6):733-748. [CrossRef]
 

Figures

Tables

References

Mura M, Porretta MA, Bargagli E, et al. Predicting survival in newly diagnosed idiopathic pulmonary fibrosis: a 3-year prospective study. Eur Respir J. 2012;40(1):101-109. [CrossRef]
 
Ley B, Ryerson CJ, Vittinghoff E, et al. A multidimensional index and staging system for idiopathic pulmonary fibrosis. Ann Intern Med. 2012;156(10):684-691. [CrossRef]
 
Goh NS, Desai SR, Veeraraghavan S, et al. Interstitial lung disease in systemic sclerosis: a simple staging system. Am J Respir Crit Care Med. 2008;177(11):1248-1254. [CrossRef]
 
Walsh SL, Sverzellati N, Devaraj A, Keir GJ, Wells AU, Hansell DM. Connective tissue disease related fibrotic lung disease: high resolution computed tomographic and pulmonary function indices as prognostic determinants [published online ahead of print November 11, 2013]. Thorax. doi:10.1136/thoraxjnl-2013-203843.
 
Walsh SLF, Wells AU, Sverzellati N, et al. An integrated clinicoradiological staging system for pulmonary sarcoidosis: a case-cohort study. Lancet Respir Med. 2014;2(2):123-130. [CrossRef]
 
Ryerson CJ, Vittinghoff E, Ley B, et al. Predicting survival across chronic interstitial lung disease: the ILD-GAP model. Chest. 2014;145(4):723-728.
 
Cordier JF, Cottin V. Neglected evidence in idiopathic pulmonary fibrosis: from history to earlier diagnosis. Eur Respir J. 2013;42(4):916-923. [CrossRef]
 
Kim EJ, Elicker BM, Maldonado F, et al. Usual interstitial pneumonia in rheumatoid arthritis-associated interstitial lung disease. Eur Respir J. 2010;35(6):1322-1328. [CrossRef]
 
Park JH, Kim DS, Park IN, et al. Prognosis of fibrotic interstitial pneumonia: idiopathic versus collagen vascular disease-related subtypes. Am J Respir Crit Care Med. 2007;175(7):705-711. [CrossRef]
 
Antoniou KM, Wells AU. Acute exacerbations of idiopathic pulmonary fibrosis. Respiration. 2013;86(4):265-274. [CrossRef]
 
Richards TJ, Kaminski N, Gibson KF. Plasma proteins for risk prediction in idiopathic pulmonary fibrosis. Am J Respir CritCare Med. 2012;185(12):1329-1330. [CrossRef]
 
du Bois RM, Weycker D, Albera C, et al. Ascertainment of individual risk of mortality for patients with idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 2011;184(4):459-466. [CrossRef]
 
Ryerson CJ, Urbania TH, Richeldi L, et al. Prevalence and prognosis of unclassifiable interstitial lung disease. Eur Respir J. 2013;42(3):750-757. [CrossRef]
 
Travis WD, Costabel U, Hansell DM, et al; ATS/ERS Committee on Idiopathic Interstitial Pneumonias. An official American Thoracic Society/European Respiratory Society statement: update of the international multidisciplinary classification of the idiopathic interstitial pneumonias. Am JRespir Crit Care Med. 2013;188(6):733-748. [CrossRef]
 
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