Early ILD Recognition in SARD/SSc: Drivers of Progression and Workflow Solutions

Interstitial lung disease (ILD) is a prevalent and severe complication of systemic autoimmune rheumatic diseases (SARD), with a reported incidence varying significantly across different SARD subtypes. For instance, ILD affects approximately 70-80% of patients with systemic sclerosis (SSc) during their disease course, making it the leading cause of SSc-related mortality.¹ Other SARDs, such as rheumatoid arthritis (RA), Sjögren's syndrome, and idiopathic inflammatory myopathies (IIM), also carry a substantial risk of ILD development, ranging from 5% to 60% depending on the specific condition and diagnostic criteria.² The challenge for clinicians lies in the often asymptomatic nature of early ILD, where significant lung damage can occur before overt respiratory symptoms manifest. This necessitates a proactive approach to screening and monitoring.

The progression of ILD in SARD/SSc is influenced by a complex interplay of genetic predispositions, specific autoantibody profiles, and environmental factors. For example, anti-topoisomerase I (anti-Scl-70) antibodies in SSc are strongly associated with a higher risk of developing severe and progressive ILD.³ Similarly, anti-synthetase antibodies in IIM are a known risk factor for rapidly progressive ILD.⁴ Other identified drivers of progression include male sex, older age at SARD onset, and specific HRCT patterns, such as usual interstitial pneumonia (UIP).⁵ Understanding these risk factors is crucial for stratifying patients and tailoring surveillance strategies. The goal is to identify patients at high risk of progression before irreversible fibrosis occurs, when therapeutic interventions are most effective. Current evidence supports that early initiation of antifibrotic or immunosuppressive therapies can slow the decline in forced vital capacity (FVC) in selected patient populations.⁶

Workflow Solutions for Early Recognition

Effective early recognition of ILD in SARD/SSc requires structured workflow solutions that integrate screening, diagnosis, and management across multiple specialties. A key component is the implementation of routine pulmonary function tests (PFTs), including FVC and diffusing capacity for carbon monoxide (DLCO), as part of standard SARD/SSc follow-up.⁷ Baseline and serial HRCT scans are also essential, particularly in high-risk patients or those with new respiratory symptoms.⁸ The frequency of these assessments should be guided by individual patient risk factors and disease activity. For instance, patients with rapidly progressive SSc or specific autoantibodies might warrant more frequent monitoring.⁹

Multidisciplinary team (MDT) discussions, involving rheumatologists, pulmonologists, radiologists, and pathologists, are fundamental for accurate diagnosis and optimal management planning.¹⁰ These discussions facilitate the interpretation of complex clinical, radiological, and pathological data, leading to a consensus diagnosis and personalized treatment strategy. The establishment of dedicated ILD clinics or referral pathways can streamline this process, ensuring that patients receive timely access to specialized expertise. Digital health solutions, such as electronic health record (EHR) alerts for at-risk patients or integrated imaging platforms, can further enhance early detection by flagging abnormal results and facilitating communication among specialists.¹¹ The aim is to reduce diagnostic delays, which have been shown to correlate with worse long-term pulmonary outcomes.¹²

While the benefits of early recognition are clear, challenges remain. These include the variability in access to specialized imaging and PFT facilities, particularly in rural areas, and the need for standardized training for clinicians in interpreting early signs of ILD. Further research is needed to refine predictive biomarkers for ILD progression and to evaluate the cost-effectiveness of different screening strategies in diverse healthcare settings. The development of artificial intelligence (AI) tools for automated HRCT analysis holds promise for improving diagnostic accuracy and efficiency.¹³