Systemic sclerosis associated interstitial lung disease: a conceptual framework for subclinical, clinical and progressive disease

David Roofeh; Kevin K Brown; Ella A Kazerooni; Donald Tashkin; Shervin Assassi; Fernando Martinez; Athol U Wells; Ganesh Raghu; Christopher P Denton; Lorinda Chung; Anna-Maria Hoffmann-Vold; Oliver Distler; Kerri A Johannson; Yannick Allanore; Eric L Matteson; Leticia Kawano-Dourado; John D Pauling; James R Seibold; Elizabeth R Volkmann; Simon L F Walsh; Chester V Oddis; Eric S White; Shaney L Barratt; Elana J Bernstein; Robyn T Domsic; Paul F Dellaripa; Richard Conway; Ivan Rosas; Nitin Bhatt; Vivien Hsu; Francesca Ingegnoli; Bashar Kahaleh; Puneet Garcha; Nishant Gupta; Surabhi Khanna; Peter Korsten; Celia Lin; Stephen C Mathai; Vibeke Strand; Tracy J Doyle; Virginia Steen; Donald F Zoz; Juan Ovalles-Bonilla; Ignasi Rodriguez-Pinto; Padmanabha D Shenoy; Andrew Lewandoski; Elizabeth Belloli; Alain Lescoat; Vivek Nagaraja; Wen Ye; Suiyuan Huang; Toby Maher; Dinesh Khanna

doi:10.1093/rheumatology/keac557

. 2022 Sep 29;62(5):1877–1886. doi: 10.1093/rheumatology/keac557

Systemic sclerosis associated interstitial lung disease: a conceptual framework for subclinical, clinical and progressive disease

David Roofeh ¹, Kevin K Brown ², Ella A Kazerooni ^3,⁴, Donald Tashkin ⁵, Shervin Assassi ⁶, Fernando Martinez ⁷, Athol U Wells ⁸, Ganesh Raghu ⁹, Christopher P Denton ¹⁰, Lorinda Chung ¹¹, Anna-Maria Hoffmann-Vold ¹², Oliver Distler ¹³, Kerri A Johannson ¹⁴, Yannick Allanore ¹⁵, Eric L Matteson ¹⁶, Leticia Kawano-Dourado ^17,^18,¹⁹, John D Pauling ^20,²¹, James R Seibold ²², Elizabeth R Volkmann ²³, Simon L F Walsh ²⁴, Chester V Oddis ²⁵, Eric S White ²⁶, Shaney L Barratt ^27,²⁸, Elana J Bernstein ²⁹, Robyn T Domsic ³⁰, Paul F Dellaripa ³¹, Richard Conway ³², Ivan Rosas ³³, Nitin Bhatt ³⁴, Vivien Hsu ³⁵, Francesca Ingegnoli ³⁶, Bashar Kahaleh ³⁷, Puneet Garcha ³⁸, Nishant Gupta ³⁹, Surabhi Khanna ⁴⁰, Peter Korsten ⁴¹, Celia Lin ⁴², Stephen C Mathai ⁴³, Vibeke Strand ⁴⁴, Tracy J Doyle ⁴⁵, Virginia Steen ⁴⁶, Donald F Zoz ⁴⁷, Juan Ovalles-Bonilla ⁴⁸, Ignasi Rodriguez-Pinto ⁴⁹, Padmanabha D Shenoy ⁵⁰, Andrew Lewandoski ⁵¹, Elizabeth Belloli ⁵², Alain Lescoat ^53,^54,⁵⁵, Vivek Nagaraja ⁵⁶, Wen Ye ⁵⁷, Suiyuan Huang ⁵⁸, Toby Maher ⁵⁹, Dinesh Khanna ^60,^✉

¹ Department of Internal Medicine, Division of Rheumatology, Scleroderma Program, University of Michigan, Ann Arbor, MI, USA

² Department of Medicine, National Jewish Health, Denver, CO, USA

³ Department of Internal Medicine, Division of Rheumatology, Scleroderma Program, University of Michigan, Ann Arbor, MI, USA

⁴ Department of Radiology, Division of Cardiothoracic Radiology, University of Michigan, Ann Arbor, MI, USA

⁵ Department of Medicine, Division of Pulmonary and Critical Care Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

⁶ Department of Internal Medicine, Division of Rheumatology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA

⁷ Department of Internal Medicine, Division of Pulmonary Critical Care Medicine, Weill Cornell School of Medicine, New York, NY, USA

⁸ Department of Internal Medicine, Division of Pulmonology, Royal Brompton Hospital and National Heart and Lung Institute, London, UK

⁹ Department of Internal Medicine, Division of Pulmonology, Critical Care and Sleep Medicine, University of Washington School of Medicine, Seattle, WA, USA

¹⁰ Centre for Rheumatology, Division of Medicine, University College London, London, UK

¹¹ Department of Internal Medicine, Division of Immunology and Rheumatology, Stanford University, and Palo Alto VA Health Care System, Palo Alto, CA, USA

¹² Department of Rheumatology, Oslo University Hospital, Oslo, Norway

¹³ Department of Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland

¹⁴ Departments of Medicine and Community Health Sciences, Section of Respiratory Medicine, University of Calgary, Calgary, Canada

¹⁵ Department of Rheumatology A, Cochin Hospital, APHP, Université de Paris, Paris, France

¹⁶ Department of Internal Medicine, Division of Rheumatology, Mayo Clinic, Rochester, MN, USA

¹⁷ HCor Research Institute, Hospital do Coração, São Paulo, Brazil

¹⁸ Pulmonary Division, Heart Institute (InCor), University of Sao Paulo Medical School, São Paulo, Brazil

¹⁹ INSERM 1152, University of Paris, Paris, France

²⁰ Musculoskeletal Research Unit, Bristol Medical School, University of Bristol, Bristol, UK

²¹ Department of Rheumatology, North Bristol NHS Trust, Southmead, Bristol, UK

²² Scleroderma Research Consultants, Aiken, SC, USA

²³ Department of Internal Medicine, Division of Rheumatology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

²⁴ National Heart and Lung Institute, Imperial College London, London, UK

²⁵ Division of Rheumatology and Clinical Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

²⁶ Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT, USA

²⁷ Academic Respiratory Unit, School of Clinical Sciences, University of Bristol, Bristol, UK

²⁸ Bristol Interstitial Lung Disease Service, North Bristol NHS Trust, Southmead, Bristol, UK

²⁹ Department of Internal Medicine, Division of Rheumatology, Columbia University School of Medicine, Vagelos College of Physicians and Surgeons, New York, NY, USA

³⁰ Department of Internal Medicine, Division of Rheumatology and Clinical Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

³¹ Department of Medicine, Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA

³² Department of Internal Medicine, Division of Rheumatology, Trinity College Dublin, University of Dublin, Dublin, Ireland

³³ Department of Internal Medicine, Division of Pulmonology, Critical Care and Sleep Medicine, Baylor College of Medicine, Houston, TX, USA

³⁴ Department of Internal Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA

³⁵ Department of Internal Medicine, Division of Rheumatology, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, USA

³⁶ Department of Clinical Sciences and Community Health, Research Center for Adult and Pediatric Rheumatic Diseases, Università degli Studi di Milano, Milano, Italy

³⁷ Department of Internal Medicine, Division of Rheumatology, University of Toledo Medical Center, Toledo, OH, USA

³⁸ Department of Internal Medicine, Division of Pulmonology, Critical Care and Sleep Medicine, Baylor College of Medicine, Houston, TX, USA

³⁹ Department of Internal Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, University of Cincinnati, Cincinnati, OH, USA

⁴⁰ Department of Internal Medicine, Division of Rheumatology, University of Cincinnati, Cincinnati, OH, USA

⁴¹ Department of Nephrology and Rheumatology, University Medical Center Göttingen, Göttingen, Germany

⁴² Genentech, Inc, San Francisco, CA, USA

⁴³ Department of Internal Medicine, Division of Pulmonology, Critical Care and Sleep Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

⁴⁴ Department of Internal Medicine, Division of Immunology and Rheumatology, Stanford University, Palo Alto, CA, USA

⁴⁵ Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA

⁴⁶ Department of Internal Medicine, Division of Rheumatology, Georgetown University School of Medicine, Washington, DC, USA

⁴⁷ Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT, USA

⁴⁸ Department of Rheumatology, Hospital General Universitario Gregorio Marañón, Madrid, Spain

⁴⁹ Autoimmune Disease Unit. Deaprtment of Internal Medicine. Hospital Mutua de Terrassa, University of Barcelona, Barcelona, Spain

⁵⁰ Department of Rheumatology, Center for Arthritis and Rheumatism Excellence, Kochi, Kerala, India

⁵¹ Department of Internal Medicine, Division of Rheumatology, University of Michigan-Metro Health, Grand Rapids, MI, USA

⁵² Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI, USA

⁵³ Department of Internal Medicine, Division of Rheumatology, Scleroderma Program, University of Michigan, Ann Arbor, MI, USA

⁵⁴ Department of Internal Medicine and Clinical Immunology, Rennes University Hospital, Rennes, France

⁵⁵ Univ Rennes, CHU Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail) - UMR_S 1085, Rennes, France

⁵⁶ Department of Internal Medicine, Division of Rheumatology, Scleroderma Program, University of Michigan, Ann Arbor, MI, USA

⁵⁷ Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA

⁵⁸ Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA

⁵⁹ Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, University of Southern California, Los Angeles, CA, USA

⁶⁰ Department of Internal Medicine, Division of Rheumatology, Scleroderma Program, University of Michigan, Ann Arbor, MI, USA

^✉

Correspondence to: Dinesh Khanna, MD, MS, Division of Rheumatology, Department of Internal Medicine, University of Michigan, 300 North Ingalls St., Suite 7C27, Ann Arbor, MI 48109, USA. E-mail: khannad@med.umich.edu

PMCID: PMC10152284 PMID: 36173318

Abstract

Objectives

To establish a framework by which experts define disease subsets in systemic sclerosis associated interstitial lung disease (SSc-ILD).

Methods

A conceptual framework for subclinical, clinical and progressive ILD was provided to 83 experts, asking them to use the framework and classify actual SSc-ILD patients. Each patient profile was designed to be classified by at least four experts in terms of severity and risk of progression at baseline; progression was based on 1-year follow-up data. A consensus was reached if ≥75% of experts agreed. Experts provided information on which items were important in determining classification.

Results

Forty-four experts (53%) completed the survey. Consensus was achieved on the dimensions of severity (75%, 60 of 80 profiles), risk of progression (71%, 57 of 80 profiles) and progressive ILD (60%, 24 of 40 profiles). For profiles achieving consensus, most were classified as clinical ILD (92%), low risk (54%) and stable (71%). Severity and disease progression overlapped in terms of framework items that were most influential in classifying patients (forced vital capacity, extent of lung involvement on high resolution chest CT [HRCT]); risk of progression was influenced primarily by disease duration.

Conclusions

Using our proposed conceptual framework, international experts were able to achieve a consensus on classifying SSc-ILD patients along the dimensions of disease severity, risk of progression and progression over time. Experts rely on similar items when classifying disease severity and progression: a combination of spirometry and gas exchange and quantitative HRCT.

Keywords: systemic sclerosis interstitial lung disease, connective tissue disease interstitial lung disease, systemic sclerosis associated interstitial lung disease subsets

Rheumatology key messages.

We created a rubric characterizing systemic sclerosis associated interstitial lung disease (SSc-ILD) along disease severity, risk of progression and progression.
Experts used this framework to classify real patients in terms of these dimensions.
This framework is a foundation for future classification criteria of SSc-ILD subsets.

Introduction

SSc is an autoimmune disease characterized by the presence of serological autoantibodies, vascular dysfunction, and progressive fibrosis of skin and internal organs [1]. SSc-associated interstitial lung disease (SSc-ILD) has a significant impact on quality of life and healthcare costs [2–5], and portends the highest risk for mortality of all potential organ involvement [6, 7]. More than 50% of SSc patients in North America have SSc-ILD [8], but the disease impact is heterogeneous, varying in terms of severity and progression [9]. This heterogeneity of ILD has been well-described, with identified SSc-ILD subsets, or subpopulations that share a similar clinical trajectory [10, 11]. With the advent of two Food and Drug Administration (FDA)-approved medications for the treatment of SSc-ILD [12, 13], there is an increasing need to develop consensus definitions of the varying SSc-ILD subsets for appropriate patient stratification [14–16].

A conceptual framework is a cognitive schema that may be used to characterize SSc-ILD subsets along the dimensions of severity, risk of progression and progression, and highlight the important variables used to delineate these subsets. A shared conceptual framework forms the basis for classification criteria, which are used for cohort enrolment in clinical studies and serve to identify those patients most likely to benefit from treatment in clinical trials. In terms of treatment and the development of therapy algorithms, decisions to initiate or advance treatment are often based on a shared understanding of severity, likelihood of progression and progressive disease. Thus, the objectives of this research effort were two-fold: (i) to build a conceptual framework that allows experts to classify severity, risk of progression and progressive disease in SSc-ILD, and (ii) observe how well the international experts agree with one another when using that framework and to identify those items most important in determining their classification.

Methods

Proposed conceptual framework and iterative revisions

Thirty-nine experts (disciplines including pulmonology medicine [n = 19], rheumatology [n = 13] and thoracic radiology [n = 7]) evaluated a proposed conceptual framework delineating subclinical, clinical and progressive ILD. Experts were invited to propose modifications and revisions; details on this process are available in the Supplementary Material, available at Rheumatology online. An updated framework was disseminated back to the working group for final feedback and subsequently presented at a national meeting [17].

Development of patient profiles

Eighty patient profiles were developed from participants in the Scleroderma Lung Study-II [18] (n = 53) and ILD patients seen at the University of Michigan Scleroderma Program (n = 27). All patients included in this study met 2013 American College of Rheumatology/European League Against Rheumatism Criteria for Systemic Sclerosis (n = 80). Experts in rheumatology, pulmonary medicine and radiology, and selected members of the Outcomes Measures in Rheumatology (OMERACT) CTD-ILD Working Group [14] provided key domains to be included in profiles.

Profiles were formatted to create baseline profiles and baseline with follow-up information over the course of 1 year profiles (Supplementary Fig. S1, available at Rheumatology online). Information on cardiopulmonary exercise testing (e.g. 6-min walking distance) and presence or absence of pulmonary hypertension was not included in the patient profiles due to a lack of available data (these data were not included uniformly in the two cohorts). Disease progression, as it is defined here, refers to progression of SSc-ILD, not other manifestations of the disease.

Expert classification

We identified 83 international experts (pulmonologists, rheumatologists and thoracic radiologists). Surveys were sent to the experts via the Qualtrics Online Survey tool (www.qualtrics.com); each survey took an estimated 30 min to complete.

The data generated from this study came from experts who volunteered to participate, after providing electronic consent on the survey provided to them. Each participant was informed and aware of his/her options to participate or decline participation. None of the data generated in the study came from patient participation.

The survey contained an introduction with a rationale for their participation, the conceptual framework for SSc-ILD subsets, and a collection of five baseline patient profiles and five baseline with follow-up profiles. Each baseline profile was classified by the expert on two dimensions: disease severity and risk of progression; each profile with follow-up was classified on one dimension: progression. For baseline profiles, the expert faced a forced choice for each profile with three options for severity (subclinical ILD, clinical ILD and unable to determine) and risk of progression (low risk, high risk and unable to determine). For follow-up profiles, the expert chose between four options for progression (stable ILD, progressive ILD, improved ILD or unable to determine). After classification, experts were required to identify factors influential in their classification decision, with a rank order preference with the top rank being the most influential, as previously done for SSc response criteria [19].

Experts were randomly selected to one of eight groups, where a minimum of four and up to 10 experts received a set of 10 profiles (five baseline and five baseline with follow-up surveys). The survey distribution discontinued when 80 profiles were fully adjudicated. A set was considered fully adjudicated when a minimum of four experts assessed the same set of profiles, with at least one expert being a rheumatologist and one being a pulmonologist. Consensus was defined as a concordance of ≥75% on a classification (e.g. three of four experts classified the profile the same way).

Agreement within and between disciplines (e.g. pulmonologists and rheumatologists) was determined by calculating the kappa coefficient for inter-rater reliability. Patient profiles were sent out in groups and rated by different sets of pulmonologists and rheumatologists. To calculate the kappa statistics among pulmonologists and the corresponding confidence intervals we used the following method. We first calculated the mean of pair-wise Cohen’s kappa statistics between all possible pairs of pulmonologists in each group. For example, for a group of profiles that was rated by three pulmonologists, we can derive the mean kappa statistics based on three pair-wise kappa statistics. We then calculated agreement among pulmonologists as the average of mean kappa among all groups. We used a bootstrap method to calculate the 95% confidence interval for the above kappa statistics. Kappa results were interpreted as follows: 0.01–0.20 as none, 0.21–0.39 as minimal, 0.40–0.59 as weak, 0.60–0.79 as moderate, 0.80–0.90 as strong, and above 0.91 as almost perfect agreement [20].

The χ² statistic was used for comparing distribution of categorical variables. P-values <0.05 were considered to be significant for all tests.

Results

Proposed conceptual framework

A preliminary proposed conceptual framework (Supplementary Table S1, available at Rheumatology online) was created after careful review of the existing literature. Our working definitions were based on literature focusing on disease severity, items that prognosticate outcome, assessment of disease impact and treatment recommendations.

Iteratively revised conceptual framework

Table 1 is an update of Supplementary Table S1, available at Rheumatology online and incorporates the proposed set of working definitions based on experts’ feedback. Four key concepts are illustrated in this revised conceptual framework. First, subclinical ILD was revised to include only asymptomatic patients regarding ILD; several experts clarified that subclinical ILD should be defined by the absence of symptoms attributable to ILD and that absence of symptoms is not synonymous with absence of disease. All experts agreed that detecting respiratory symptoms in patients with ILD is challenging for several reasons (e.g. diminished exercise capacity due to advancing cutaneous, musculoskeletal or pulmonary disease precluding effort that elicits dyspnoea), as is differentiating dyspnoea (e.g. secondary to ILD vs pulmonary hypertension or both). Second, in the context of a defined connective tissue disease, such as SSc, the radiographic changes seen in SSc patients, even if asymptomatic, are not included in the definition of interstitial lung abnormalities (ILAs), as agreed by a recently published expert statement [21, 22]. Third, experts commented that management of the disease should not be yoked to the SSc-ILD subset. In our original conception, subclinical ILD did not require treatment, clinical ILD generally did require treatment, and progressive ILD required change, escalation or addition of new therapies. The rationale for removing language about treatment was that this is a matter for empirical discovery; the classification of patients should not be determined by the behaviour of the treating physician. As an example, the recently completed phase III trial of tocilizumab shows a beneficial effect in a subset of patients who may have been characterized as subclinical ILD; in our original conception, this population would have fallen outside the scope of clinical ILD, not been treated and would not have benefitted from treatment [13]. Finally, progression should not be seen as a subset separate from subclinical or clinical ILD, but rather a property of either subset. In the original conception, progressive ILD was described as a state of advancing fibrotic disease on HRCT with escalation of respiratory symptoms and/or decline on serial lung physiology, gas exchange or both. In the revised version advancing symptoms, declining lung physiology and increased extent of ILD on HRCT mark the state of progression in either subclinical or clinical ILD. The critical revision here centres on recognizing that progressive SSc-ILD should be contextualized: a subclinical ILD patient with progression may not have the same disease mechanism or expected response to treatment as a clinical ILD patient with progression.

Table 1.

Revised conceptual framework

	Subclinical SSc-ILD	Clinical SSc-ILD
Clinical features	All variables should be met but there may be exceptions	Must have ≥1 feature
Demographics	N/A	N/A
Age, sex, race	N/A	N/A
SSc disease factors	N/A	N/A
SSc cutaneous classification
Disease duration
ANA status
SSc specific autoantibody
Modified Rodnan Skin Score
Respiratory symptoms	None	Present
Mahler Dyspnoea Index and Transitional Index
Leicester Cough Questionnaire
Patient Global Assessment
St George’s Respiratory Questionnaire
Spirometry with gas exchange	Normal-to-near normal	Deficits present
Forced vital capacity (% predicted)
Diffusion capacity of carbon monoxide (% predicted)
Desaturation on exercise	Normal-to-near normal	Deficits present
Oxygen desaturation during 6-min walk test
Quantitative HRCT	Minimal-to-mild	Mild-to-severe disease
Whole lung involvement (% of ground glass opacities, fibrotic reticulations and honeycombing)
Whole lung fibrosis (% of only the fibrotic reticulations)
Disease impact	All features should be met	Must have ≥1 feature
Feel	None	Yes
Function	None	Yes
Survive	N/A	Yes
Disease progression	Must have ≥1 feature for either category (attributable to ILD)
Respiratory symptoms	New onset dyspnoea or cough	Advancing dyspnoea or cough
Spirometry with gas exchange	New decline	Advancing decline
Desaturation on exercise or exercise limitation	New desaturation and/or limitation	Advancing desaturation and/or limitation
Quantitative HRCT	New, larger extent of disease burden	Advancing extent of disease burden

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CTD-ILD: CTD-interstitial lung disease; HRCT: high resolution CT; SSc-ILD: SSc associated interstitial lung disease.

Expert classification

Forty-four of 83 (53%) of invited experts from 12 countries completed the survey, representing the following disciplines: rheumatology, n = 26; pulmonary medicine, n = 16; and thoracic radiology, n = 2 (Supplementary Table S2, available at Rheumatology online).

A majority of profiles achieved consensus along the three dimensions. The highest degrees of concordance were seen in severity (75%, or 60 of 80 baseline profiles) and risk of progression (71%, or 57 of 80 baseline profiles). Fewer profiles reached consensus for progression (60%, or 24 of 40 follow-up profiles) (Table 2). For each dimension, the majority subsets achieving consensus were as follows: severity–clinical ILD (92%, or 55 of 60), risk of progression–low risk of progression (54%, or 31 of 57), and progression–stable (71%, or 17 of 24 follow-up profiles).

Table 2.

Profiles assessed by dimension

	Severity		Risk of progression		Progression
Number of profiles assessed	80		80		40
Profiles achieving consensus, n (%)^a	60 (75)		57 (71)		24 (60)
Subset	Subclinical	3	High Risk	26	Improved	3
	Clinical	55	Low Risk	31	Progressive	4
					Stable	17
Cannot classify (based on the given information)		2		0		0
Profiles not achieving consensus, n (%)	20 (25)		23 (29)		16 (40)

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A consensus was reached if ≥75% of experts in each group agreed.

Classification agreement between the two most common disciplines (e.g. pulmonology–rheumatology) did not differ in terms of the kappa statistic assessing inter-rater assessment for each of the three dimensions (Table 3). Agreement between pulmonologists and rheumatologists was not found to be different from the agreement within each discipline either. Kappa reported for severity was none whereas the risk of progression and progression were generally weak or moderate.

Table 3.

Agreement of classification by discipline, along dimensions of severity, risk of progression and progression

A. Determined by Kappa statistic
Kappa calculation	n (pair)^a	Average n (profile)^b	Mean	Bootstrapped mean (95% CI)^c
Severity
Between rheumatologists and pulmonologists	66	7.6	0.13	0.13 (0.00, 0.25)
Among rheumatologists	44	8.7	0.17	0.17 (−0.01, 0.45)
Among pulmonologists	17	6.6	0.20	0.18 (0, 0.25)
Risk of progression
Between rheumatologists and pulmonologists	66	6.6	0.61	0.59 (0.49, 0.69)
Among rheumatologists	44	8.3	0.70	0.66 (0.51, 0.86)
Among pulmonologists	17	5.9	0.48	0.4618 (0.26, 0.66)
Progression
Between rheumatologists and pulmonologists	66	3.1	0.56	0.51 (0.18, 0.70)
Among rheumatologists	44	3.5	0.78	0.70 (0.36, 0.95)
Among pulmonologists	17	3.1	0.29	0.24 (−0.00, 0.50)

B. Determined by χ² analysis
χ² calculation	Rheumatology	Pulmonology	P-value
Severity^d
Clinical ILD	205 (93.2%)	114 (89.8%)	0.26
Subclinical ILD	15 (6.8%)	13 (10.2%)	0.26
Risk of progression
High risk	97 (45.3%)	55 (46.2%)	0.88
Low risk	117 (54.7%)	64 (53.8%)	0.88
Progression
Progressive	17 (18.9%)	11 (20.0%)	0.20
Stable	57 (63.3%)	40 (72.7%)
Improved	16 (17.8%)	4 (7.3%)

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Number of paired used to calculate kappa statistics.

Average number of profile in each pair.

100 bootstrap datasets, randomly selecting based on profile with replacement.

‘Cannot tell’ was removed from this calculation.

For those profiles achieving consensus and only assessing the relationship between two disciplines (e.g. radiology was excluded due to the low representation in participation), a χ² analysis assessed the proportion of each domain’s outcomes (e.g. clinical ILD vs subclinical ILD) by the discipline (e.g. pulmonologist and rheumatologist), and did not show statistically disproportionate disagreement for each dimension (Table 3).

Table 4 reports the most frequently cited single item that experts used to influence their classification, as determined by the first item selected by the expert, representing their top choice in diagnostic importance. These data show that the items reported by experts were most influential in their classification for severity of ILD (in order of top ranked items) were forced vital capacity (FVC), HRCT quantitative total lung involvement (summed percentage of ground glass opacities, fibrotic reticulations and honeycombing), dyspnoea index (Baseline Dyspnoea Index/Transition Index), and diffusion capacity of carbon monoxide (DLCO). For progression, the top ranked items included FVC, HRCT total lung involvement, total lung fibrosis on HRCT, dyspnoea index, and DLCO. The highest ranked item used to assess risk of progression classification was a disease factor, specifically disease duration followed by FVC, HRCT total lung involvement and scleroderma-specific autoantibodies.

Table 4.

Importance based on percentage of items used in the classification of profiles along dimensions of severity, risk of progression, and progression

	Severity		Risk of Progression		Progression
Domain with items used in classification	Rank between domains	Importance based on percentage selected	Rank between domains	Importance based on percentage selected	Rank between domains	Importance based on percentage selected
Demographics	5	Least influential	4	Less influential	—	Not ranked
Age, %		0		1
Sex, %		0		1
Race, %		0		1
Disease factors	4	Less influential	1	Most influential	—	Not ranked
Systemic sclerosis subtype, %		3		7
Disease duration, %		2		31
ANA status, %		0		1
Systemic sclerosis autoantibody status, %		2		11
Modified Rodnan Skin Score, %		0		1
Patient reported outcome measures	3	Influential	5	Least influential	3	Least influential
Baseline Dyspnoea Index/Transition Index, %		19		1		6
Leicester Cough Questionnaire, %		1		0		0
Patient global assessment, %		1		0		1
St George’s Respiratory Questionnaire, %		3		1		2
Spirometry and gas exchange	1	Most influential	2	Very influential	1	Most influential
Forced vital capacity, %		29		17		48
Diffusion capacity of carbon monoxide, %		11		5		6
Quantitative high resolution chest CT	2	Very influential	3	Influential	2	Influential
Total lung involvement, %		25		15		29
Total lung fibrosis, %		5		6		8

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Discussion

To our knowledge, this is the first collaborative effort to establish a conceptual framework for SSc-ILD subsets. We created a literature-based, expert-informed rubric that characterizes SSc-ILD along three dimensions: disease severity, risk of progression and progression over time. This framework (i) was tested by having experts classify real-world patient profiles, (ii) reached agreement for all three dimensions, having a majority of patient profiles achieving consensus (≥75% concordance with other experts), and (iii) helped identify which items are most important in adjudicating between SSc-ILD subsets. Importantly, the framework does not include any specific values or cut-points in the definition of each subset. The goal of this work was to provide an inventory of clinical information necessary and general guidelines for implementation, to lead to a classification scheme along different dimensions. The result of this body of work is fundamental to the future development of classification criteria of SSc-ILD subsets and may provide a platform to expand to other fibrotic ILDs.

A majority of experts reached consensus on severity (75% of experts) and risk of progressive disease (71% of experts); this may reflect experts’ familiarity with the basis of the framework, the extensive literature focusing on disease severity (e.g. epidemiologic data, expert opinion on determining which patients should receive treatment, inclusion criteria for SSc-ILD clinical trials) and risk of progression (e.g. identifying prognostic items that identify those with a concerning clinical trajectory). The kappa statistic was poor for the severity classification (Table 3). The kappa statistic is known to be a chance-corrected statistic that is dependent on prevalence and in our case affected by the low prevalence of subclinical ILD classifications; for rare outcomes, very low kappa values do not necessarily reflect low rates of overall agreement [23]. Progressive SSc-ILD is perhaps a less well-defined concept in the literature, with few clinical trials providing clear operational definitions of progression in the form of inclusion/exclusion criteria. At the time the survey was conducted (January 2019–June 2019), the INBUILD trial, which focused on a population of patients with progressive fibrosing lung disease, had not yet been published (September 2019) [24]; this may provide insight as to why a smaller percentage of experts achieved consensus (60%). The exercise may also reflect the heterogeneous progressive nature of SSc-ILD, compared with severity or risk of progression.

Experts reported the FVC and extent of lung involvement on HRCT as the most important features used in classifying along severity and progression. The top priority on FVC and quantitative HRCT (whole lung involvement percentage) in this study likely reflects the impact of Goh et al.’s work and the subsequent data supporting the prognostic value in terms of disease severity and progression [25–29]. SSc-specific disease factors (e.g. factors describing SSc, without specific respiratory symptoms/lung function/imaging of the chest) were the most influential features in terms of determining risk of progression (accounting for 51% of all the items selected as the most important in classification), with disease duration as the most influential. This likely stems from the well-documented relationships to risk of progression, with shorter disease duration [30, 31] and presence of anti-SCL-70 (anti-topoisomerase I) increasing the risk for developing clinically significant SSc-ILD [32].

Classification agreement did not differ significantly between disciplines (e.g. pulmonology and rheumatology). The moderate degree of reliability between disciplines suggests that the invited authors all shared the same conceptual framework when completing the classification task for each dimension. One statistical consideration, given the relatively small number of evaluations per group, is the possibility that some profiles achieving or not achieving consensus could have been the result of chance alone and not a shared consensus.

Four limiting factors contextualize these results. First, the data in this initiative are generated from experts responsive to an invitation to participate; to avoid a selection bias, we invited a network broader than those with phone or email contact. Social media is playing a larger role in collaborative efforts in science [33, 34]. We broadcast this initiative using social media platforms and received interest from participants in several countries and from several disciplines. We selected only those respondents who have demonstrated considerable contribution to the field of ILD. Importantly, there were no expert participants from East Asian countries, although there was representation from South Asia. Pulmonologists who participated in this exercise (data shared by 13 of the 16) spend about half of their time dedicated to clinical practice (54%); of that clinical time, more than half (58%) is spent dedicated to fibrotic ILDs and about 40% is spent on general pulmonary medicine/critical care medicine. Input from general pulmonologists should also be considered in the future to evaluate the conceptual framework’s ease of use. Second, patients recruited from clinical trials tend to have more severe manifestations of lung disease than those not enrolled in trials. Knowledge that patient profiles were created from SLS-II patients may have biased experts to classify patients as ‘clinical’ rather than ‘subclinical’. We sought to offset that bias with patients from our institution who did not participate in clinical trials, to provide experts with a cache of SSc patients with minimal to mild ILD. Third, a major limitation of the presented conceptual framework supposes that patient reported outcomes are measuring symptoms (e.g. dyspnoea, exercise limitation) attributed to SSc-ILD not confounded by other causes (e.g. pulmonary hypertension, anaemia, musculoskeletal disease, diaphragmatic weakness, smoking, deconditioning). Future work will require classification exercises to be based on more granular detail of the cardiorespiratory status of patients with SSc-ILD; this may allow for more generalizable interpretations of symptom assessment in the setting of real-world, co-occurring and potentially confounding features. Finally, the framework is the product of expert discussion that reflects an understanding of SSc-ILD in a particular time-dependent context and will require revisions as our understanding of the disease progresses. This project was launched in 2019 when phase III focuSSced data were being analysed. Notably absent from the framework are acute phase reactants, which may now be considered a marker of a progressive phenotype demonstrated in the focuSSced population. The framework in its current form will be updated with acute phase reactants in subsequent iterations. Future efforts working towards developing formal classification criteria of SSc-ILD will dovetail with the American College of Rheumatology’s ongoing initiative to develop guidelines for screening and management of CTD-ILDs [35]. Additionally, there will need to be consideration for patient input in the classification to capture an element of lived experience with this disease not captured by patient reported outcome measures. There is an ongoing effort to get patients’ input as part of the OMERACT CTD-ILD working group.

Johnson et al. 2018 [36] have identified a need for new SSc subset criteria, with the advent of an improved understanding of the disease (e.g. biomarkers, autoantibody profiles, genetic markers), and early disease identification, in the era of personalized medicine [36, 37]. The impetus for developing working definitions of SSc-ILD subsets is based on the same principles; this effort is timely in light of two treatments approved for the indication of SSc-ILD by the FDA [38, 39]. These data form the basis for a multi-dimensional assessment of SSc-ILD (severity, risk of progression and progression over time) and are a step towards building classification criteria for these subsets. Future work will include validation of the conceptual framework in a separate cohort of patients.

Supplementary data

Supplementary data are available at Rheumatology online.

Supplementary Material

keac557_Supplementary_Data

Click here for additional data file.^{(38.7KB, docx)}

Acknowledgements

D.R. and D.K.: conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing- original draft, writing—review and editing, visualization, project administration, funding acquisition. K.K.B., E.K., D.T., S.A., F.M., A.U.W., G.R., C.D., L.C., A-M.H-V., O.D., K.J., Y.A., E.M., L.K-D., J.P., J.S., E.V., S.W., C.O., E.W., S.L.B., E.J.Ber., R.D., P.D., R.C., I.Ros., N.B., V.H., F.I., B.K., P.G., N.G., S.K., P.K., C.L., S.M., V.Str., T.D., V.Ste., D.Z., J.O-B., I.R-P., P.D.S., A.Lew., E.Bel., A.Les., V.N., T.M.: data acquisition, writing—review and editing, project administration. W.Y. and S.H.: methodology, formal analysis, data curation.

Contributor Information

David Roofeh, Department of Internal Medicine, Division of Rheumatology, Scleroderma Program, University of Michigan, Ann Arbor, MI, USA.

Kevin K Brown, Department of Medicine, National Jewish Health, Denver, CO, USA.

Ella A Kazerooni, Department of Internal Medicine, Division of Rheumatology, Scleroderma Program, University of Michigan, Ann Arbor, MI, USA; Department of Radiology, Division of Cardiothoracic Radiology, University of Michigan, Ann Arbor, MI, USA.

Donald Tashkin, Department of Medicine, Division of Pulmonary and Critical Care Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.

Shervin Assassi, Department of Internal Medicine, Division of Rheumatology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.

Fernando Martinez, Department of Internal Medicine, Division of Pulmonary Critical Care Medicine, Weill Cornell School of Medicine, New York, NY, USA.

Athol U Wells, Department of Internal Medicine, Division of Pulmonology, Royal Brompton Hospital and National Heart and Lung Institute, London, UK.

Ganesh Raghu, Department of Internal Medicine, Division of Pulmonology, Critical Care and Sleep Medicine, University of Washington School of Medicine, Seattle, WA, USA.

Christopher P Denton, Centre for Rheumatology, Division of Medicine, University College London, London, UK.

Lorinda Chung, Department of Internal Medicine, Division of Immunology and Rheumatology, Stanford University, and Palo Alto VA Health Care System, Palo Alto, CA, USA.

Anna-Maria Hoffmann-Vold, Department of Rheumatology, Oslo University Hospital, Oslo, Norway.

Oliver Distler, Department of Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.

Kerri A Johannson, Departments of Medicine and Community Health Sciences, Section of Respiratory Medicine, University of Calgary, Calgary, Canada.

Yannick Allanore, Department of Rheumatology A, Cochin Hospital, APHP, Université de Paris, Paris, France.

Eric L Matteson, Department of Internal Medicine, Division of Rheumatology, Mayo Clinic, Rochester, MN, USA.

Leticia Kawano-Dourado, HCor Research Institute, Hospital do Coração, São Paulo, Brazil; Pulmonary Division, Heart Institute (InCor), University of Sao Paulo Medical School, São Paulo, Brazil; INSERM 1152, University of Paris, Paris, France.

John D Pauling, Musculoskeletal Research Unit, Bristol Medical School, University of Bristol, Bristol, UK; Department of Rheumatology, North Bristol NHS Trust, Southmead, Bristol, UK.

James R Seibold, Scleroderma Research Consultants, Aiken, SC, USA.

Elizabeth R Volkmann, Department of Internal Medicine, Division of Rheumatology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.

Simon L F Walsh, National Heart and Lung Institute, Imperial College London, London, UK.

Chester V Oddis, Division of Rheumatology and Clinical Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.

Eric S White, Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT, USA.

Shaney L Barratt, Academic Respiratory Unit, School of Clinical Sciences, University of Bristol, Bristol, UK; Bristol Interstitial Lung Disease Service, North Bristol NHS Trust, Southmead, Bristol, UK.

Elana J Bernstein, Department of Internal Medicine, Division of Rheumatology, Columbia University School of Medicine, Vagelos College of Physicians and Surgeons, New York, NY, USA.

Robyn T Domsic, Department of Internal Medicine, Division of Rheumatology and Clinical Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.

Paul F Dellaripa, Department of Medicine, Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Richard Conway, Department of Internal Medicine, Division of Rheumatology, Trinity College Dublin, University of Dublin, Dublin, Ireland.

Ivan Rosas, Department of Internal Medicine, Division of Pulmonology, Critical Care and Sleep Medicine, Baylor College of Medicine, Houston, TX, USA.

Nitin Bhatt, Department of Internal Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA.

Vivien Hsu, Department of Internal Medicine, Division of Rheumatology, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, USA.

Francesca Ingegnoli, Department of Clinical Sciences and Community Health, Research Center for Adult and Pediatric Rheumatic Diseases, Università degli Studi di Milano, Milano, Italy.

Bashar Kahaleh, Department of Internal Medicine, Division of Rheumatology, University of Toledo Medical Center, Toledo, OH, USA.

Puneet Garcha, Department of Internal Medicine, Division of Pulmonology, Critical Care and Sleep Medicine, Baylor College of Medicine, Houston, TX, USA.

Nishant Gupta, Department of Internal Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, University of Cincinnati, Cincinnati, OH, USA.

Surabhi Khanna, Department of Internal Medicine, Division of Rheumatology, University of Cincinnati, Cincinnati, OH, USA.

Peter Korsten, Department of Nephrology and Rheumatology, University Medical Center Göttingen, Göttingen, Germany.

Celia Lin, Genentech, Inc, San Francisco, CA, USA.

Stephen C Mathai, Department of Internal Medicine, Division of Pulmonology, Critical Care and Sleep Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Vibeke Strand, Department of Internal Medicine, Division of Immunology and Rheumatology, Stanford University, Palo Alto, CA, USA.

Tracy J Doyle, Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Virginia Steen, Department of Internal Medicine, Division of Rheumatology, Georgetown University School of Medicine, Washington, DC, USA.

Donald F Zoz, Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT, USA.

Juan Ovalles-Bonilla, Department of Rheumatology, Hospital General Universitario Gregorio Marañón, Madrid, Spain.

Ignasi Rodriguez-Pinto, Autoimmune Disease Unit. Deaprtment of Internal Medicine. Hospital Mutua de Terrassa, University of Barcelona, Barcelona, Spain.

Padmanabha D Shenoy, Department of Rheumatology, Center for Arthritis and Rheumatism Excellence, Kochi, Kerala, India.

Andrew Lewandoski, Department of Internal Medicine, Division of Rheumatology, University of Michigan-Metro Health, Grand Rapids, MI, USA.

Elizabeth Belloli, Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI, USA.

Alain Lescoat, Department of Internal Medicine, Division of Rheumatology, Scleroderma Program, University of Michigan, Ann Arbor, MI, USA; Department of Internal Medicine and Clinical Immunology, Rennes University Hospital, Rennes, France; Univ Rennes, CHU Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail) - UMR_S 1085, Rennes, France.

Vivek Nagaraja, Department of Internal Medicine, Division of Rheumatology, Scleroderma Program, University of Michigan, Ann Arbor, MI, USA.

Wen Ye, Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.

Suiyuan Huang, Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.

Toby Maher, Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, University of Southern California, Los Angeles, CA, USA.

Dinesh Khanna, Department of Internal Medicine, Division of Rheumatology, Scleroderma Program, University of Michigan, Ann Arbor, MI, USA.

Data availability statement

The data underlying this article will be shared on reasonable request to the corresponding author.

Funding

D.K.’s work was supported by the NIH/National Institute of Arthritis and Musculoskeletal and Skin Diseases (K24-AR-063120 & 1R01-AR070470-01A1). D.R.’s work was supported by the NIH/NIAMS T32 grant (AR007080).

Disclosure statement: D.R. reports personal fees from Boehringer Ingelheim, outside the submitted work; S.L.B. reports personal fees from Boehringer Ingelheim, outside the submitted work; K.K.B. reports grants from NHLBI, serves on the board of the Open Source Imaging Consoritum (OSIC), and personal fees from Biogen, Galecto, Third Pole, Galapagos, Boehringer Ingelheim, Theravance, Pliant, Blade Therapeutics, Huitai Biomedicine, Lilly, Dispersol, DevPro Biopharma, Sanofi, Bristol Myers Squibb and Humanetics outside the submitted work; R.C. reports personal fees from Janssen Sciences, personal fees from Abbvie, personal fees from Pfizer, personal fees from Sanofi, personal fees from Roche, non-financial support from UCB, outside the submitted work; P.F.D. reports personal fees from up to date, other from FDA advisory board, outside the submitted work; C.P.D. reports personal fees from Actelion, grants and personal fees from GlaxoSmithKline, personal fees from Bayer, personal fees from Sanofi, personal fees from Galapagos, grants and personal fees from Inventiva, personal fees from Boehringer Ingelheim, grants and personal fees from CSL Behring, personal fees from Corbus, grants and personal fees from Arxx Therapeutics, personal fees from Horizon, grants from Servier, during the conduct of the study; O.D. has/had consultancy relationship with and/or has received research funding from or has served as a speaker for the following companies in the area of potential treatments for systemic sclerosis and its complications in the last three years: Abbvie, Acceleron, Alcimed, Amgen, AnaMar, Arxx, AstraZeneca, Baecon, Blade, Bayer, Boehringer Ingelheim, ChemomAb, Corbus, CSL Behring, Galapagos, Glenmark, GSK, Horizon (Curzion), Inventiva, iQvia, Kymera, Lupin, Medac, Medscape, Miltenyi Biotec, Mitsubishi Tanabe, Novartis, Prometheus, Roche, Roivant, Sanofi, Serodapharm, Topadur and UCB; and patent issued ‘mir-29 for the treatment of systemic sclerosis’ (US8247389, EP2331143). R.T.D. reports personal fees from Boehringer-Ingelheim, personal fees from Eicos Sciences, personal fees from Formation Biologics, outside the submitted work; A-M.H-V. reports personal fees and non-financial support from Actelion, personal fees from ARXX, personal fees from Bayer, grants, personal fees and non-financial support from Boehringer-Ingehleim, personal fees from Lilly, personal fees from Medscape, personal fees from MSD, personal fees from Roche, outside the submitted work; V.H. reports personal fees from Boehringer Ingelheim speaker, outside the submitted work; K.A.J. reports personal fees and other from Boehringer-Ingelheim, personal fees and other from Hoffman La Roche Ltd, personal fees and other from Theravance, personal fees and other from Blade Therapeutics, grants from Chest Foundation, grants from University of Calgary School of Medicine, grants from Pulmonary Fibrosis Society of Calgary, grants from UCB Biopharma SPRL, personal fees from Three Lakes Foundation, outside the submitted work; L.K-D. reports grants and personal fees from Bristol-Myers-Squibb, grants and personal fees from Boehringer Ingelheim, and personal fees from Roche, Lilly outside the submitted work; D.K. reports consulting fees from Acceleron, Actelion, Amgen, Bayer, Boehringer Ingelheim, Chemomab, CSL Behring, Genentech/Roche, Horizon, Paracrine Cell Therapy, Mitsubishi Tanabe Pharma, Prometheus, Theraly; D.K. is also Chief Medical Officer of Eicos Sciences, Inc., a subsidiary of CiviBioPharma and has stock options; P.K. reports grants from Glaxo Smith Kline, personal fees from Abbvie, personal fees from Boehringer-Ingelheim, personal fees from Gilead, personal fees from Lilly, personal fees from Glaxo Smith Kline, personal fees from Chugai, personal fees from Novartis, personal fees from Pfizer, personal fees from Otsuka, personal fees from Janssen-Cilag, personal fees from Bristol Myers Squibb, personal fees from Sanofi-Aventis, outside the submitted work; C.L. is employed by Genentech; T.M. has, via his institution, received industry-academic funding from Astra Zeneca and GlaxoSmithKline R&D and has received consultancy or speakers fees from Astra Zeneca, Bayer, Blade Therapeutics, Boehringer Ingelheim, Bristol-Myers Squibb, Galapagos, Galecto, GlaxoSmithKline R&D, IQVIA, Pliant, Respivant, Roche and Theravance; F.M. reports personal fees from GlaxoSmithKline, personal fees from AstraZeneca, personal fees from Boehringer Ingelheim, personal fees from Raziel, during the conduct of the study; personal fees and non-financial support from AstraZeneca, personal fees and non-financial support from Boehringer Ingelheim, non-financial support from ProterrixBio, personal fees, non-financial support and other from Genentech, personal fees and non-financial support from GlaxoSmithKline, personal fees from MD Magazine, personal fees from Methodist Hospital Brooklyn, personal fees and non-financial support from Miller Communications, personal fees and non-financial support from National Society for Continuing Education, personal fees from New York University, personal fees and non-financial support from PeerView Communications, personal fees and non-financial support from Chiesi, personal fees and non-financial support from Sunovion, personal fees from UpToDate, personal fees from WebMD/MedScape, other from Afferent/Merck, non-financial support from Gilead, non-financial support from Nitto, personal fees from Patara/Respivant, other from Biogen, other from Veracyte, non-financial support from Zambon, personal fees from American Thoracic Society, grants from NIH, personal fees and non-financial support from Physicians Education Resource, personal fees from Rockpointe, other from Prometic, grants from Rare Disease Healthcare Communications, personal fees and other from Bayer, other from Bridge Biotherapeutics, personal fees and non-financial support from Canadian Respiratory Network, grants from ProMedior/Roche, personal fees and non-financial support from Teva, personal fees from CME Outfitters, personal fees and non-financial support from Csl Behring, personal fees from Dartmouth University, personal fees from DevPro, from Gala, personal fees from Integritas, personal fees from IQVIA, personal fees from Projects in Knowledge, personal fees and non-financial support from Sanofi/Regeneron, from twoXAR, personal fees from Vindico, other from AbbVie, personal fees from Academy for Continuing Healthcare Learning, outside the submitted work; C.V.O. reports personal fees from Kezar and Pfizer, outside the submitted work; J.D.P received speakers honoraria and/or personal support from Janssen, Astra Zeneca, Permeatus Inc, Boehringher-Ingelheim and Sojournix Pharma; J.R.S reports personal fees from Atlantic (UK), Blade, Boehringer Ingelheim, Camurus, Corbus, Indalo, Prometheus, Sojournix, and Xenikos; V.Ste. reports grants and other from Boehringer Ingelheim, grants from Genentech, outside the submitted work; V.Str. is a member of the executive of OMERACT (1992–present), an organization that develops and validates outcome measures in rheumatology randomized controlled trials and longitudinal observational studies and receives arms-length funding from 36 sponsors; S.L.F.W. reports personal fees from Sanofi-Aventis, personal fees from Roche, grants and personal fees from Boehringer Ingelheim, personal fees from Galapagos, personal fees from OSIC, personal fees from Bracco, grants from NIHR, personal fees from Verocyte, outside the submitted work; E.S.W. and D.F.Z. are employed by Boehringer Ingelheim; E.R.V. reports personal fees from Boehringer Ingelheim, grants from Corbus, grants from Forbius, outside the submitted work; D.K. reports personal fees from Abbivie, Actelion, Boehinger Ingeheim, Galapagos, CSL Behring, Genentech/Roche, Prometheus, Horizon, Merck, Mistibushi Tanabe, and grants from Boehinger Ingeheim, Horizon, and Pfizer, and has stock options in Eicos Sciences, Inc.

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39. Khanna D, Lescoat A, Roofeh D. et al. Systemic sclerosis-associated interstitial lung disease: how to incorporate two food and drug administration‐approved therapies in clinical practice. Arthritis Rheumatol 2022;74:13–27. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

keac557_Supplementary_Data

Click here for additional data file.^{(38.7KB, docx)}

Data Availability Statement

The data underlying this article will be shared on reasonable request to the corresponding author.

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PERMALINK

Systemic sclerosis associated interstitial lung disease: a conceptual framework for subclinical, clinical and progressive disease

David Roofeh

Kevin K Brown

Ella A Kazerooni

Donald Tashkin

Shervin Assassi

Fernando Martinez

Athol U Wells

Ganesh Raghu

Christopher P Denton

Lorinda Chung

Anna-Maria Hoffmann-Vold

Oliver Distler

Kerri A Johannson

Yannick Allanore

Eric L Matteson

Leticia Kawano-Dourado

John D Pauling

James R Seibold

Elizabeth R Volkmann

Simon L F Walsh

Chester V Oddis

Eric S White

Shaney L Barratt

Elana J Bernstein

Robyn T Domsic

Paul F Dellaripa

Richard Conway

Ivan Rosas

Nitin Bhatt

Vivien Hsu

Francesca Ingegnoli

Bashar Kahaleh

Puneet Garcha

Nishant Gupta

Surabhi Khanna

Peter Korsten

Celia Lin

Stephen C Mathai

Vibeke Strand

Tracy J Doyle

Virginia Steen

Donald F Zoz

Juan Ovalles-Bonilla

Ignasi Rodriguez-Pinto

Padmanabha D Shenoy

Andrew Lewandoski

Elizabeth Belloli

Alain Lescoat

Vivek Nagaraja

Wen Ye

Suiyuan Huang

Toby Maher

Dinesh Khanna

Abstract

Objectives

Methods

Results

Conclusions

Rheumatology key messages.

Introduction

Methods

Proposed conceptual framework and iterative revisions

Development of patient profiles

Expert classification

Results

Proposed conceptual framework

Iteratively revised conceptual framework

Table 1.

Expert classification

Table 2.

Table 3.

Table 4.

Discussion

Supplementary data

Supplementary Material

Acknowledgements

Contributor Information

Data availability statement