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Updated: Mar 3, 2026
Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease
Published on: June 16, 2020
Clinical algorithms for the diagnosis and prognosis of interstitial lung disease in systemic sclerosis
Vanessa Hax1, Markus Bredemeier2, Ana Laura Didonet Moro3
1Department of Internal Medicine, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, Brazil; Division of Rheumatology, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.
Insights
Clinical algorithms accurately predict interstitial lung disease (ILD) in systemic sclerosis (SSc) and its mortality risk. These tools are valuable for diagnosis and prognosis, especially when high-resolution computed tomography (HRCT) is unavailable.
Area of Science:
- Pulmonology
- Rheumatology
- Radiology
Background:
- Interstitial lung disease (ILD) is a major cause of mortality in systemic sclerosis (SSc).
- High-resolution computed tomography (HRCT) is the standard for SSc-ILD diagnosis.
- Clinical algorithms are emerging for SSc-ILD prediction.
Purpose of the Study:
- To validate clinical algorithms for SSc-ILD diagnosis and prognosis.
- To assess the link between ILD extent and mortality in SSc patients.
Main Methods:
- Retrospective cohort study of 177 SSc patients.
- Utilized clinical evaluation, lab tests, pulmonary function tests, and HRCT.
- Applied three clinical algorithms and analyzed mortality using Cox models.
Main Results:
- ILD prevalence was 57.1%, with 44.6% mortality over 11.1 years.
- Algorithms showed high sensitivity for detecting ILD extent (≥10% and ≥20%).
- Algorithm C and Goh et al.'s extensive disease criteria significantly predicted mortality (HR > 3.4).
Conclusions:
- Clinical algorithms demonstrate good diagnostic and prognostic performance for SSc-ILD.
- Non-HRCT algorithms offer utility when HRCT is inaccessible.
- This study validates the Goh et al. prognostic algorithm in a developing country context.
Introduction:
Interstitial lung disease (ILD) is currently the primary cause of death in systemic sclerosis (SSc). Thoracic high-resolution computed tomography (HRCT) is considered the gold standard for diagnosis. Recent studies have proposed several clinical algorithms to predict the diagnosis and prognosis of SSc-ILD.
Objective:
To test the clinical algorithms to predict the presence and prognosis of SSc-ILD and to evaluate the association of extent of ILD with mortality in a cohort of SSc patients.
Methods:
Retrospective cohort study, including 177 SSc patients assessed by clinical evaluation, laboratory tests, pulmonary function tests, and HRCT. Three clinical algorithms, combining lung auscultation, chest radiography, and percentage predicted forced vital capacity (FVC), were applied for the diagnosis of different extents of ILD on HRCT. Univariate and multivariate Cox proportional models were used to analyze the association of algorithms and the extent of ILD on HRCT with the risk of death using hazard ratios (HR).
Results:
The prevalence of ILD on HRCT was 57.1% and 79 patients died (44.6%) in a median follow-up of 11.1 years. For identification of ILD with extent ≥10% and ≥20% on HRCT, all algorithms presented a high sensitivity (>89%) and a very low negative likelihood ratio (<0.16). For prognosis, survival was decreased for all algorithms, especially the algorithm C (HR = 3.47, 95% CI: 1.62-7.42), which identified the presence of ILD based on crackles on lung auscultation, findings on chest X-ray, or FVC <80%. Extensive disease as proposed by Goh et al. (extent of ILD > 20% on HRCT or, in indeterminate cases, FVC < 70%) had a significantly higher risk of death (HR = 3.42, 95% CI: 2.12-5.52). Survival was not different between patients with extent of 10% or 20% of ILD on HRCT, and analysis of 10-year mortality suggested that a threshold of 10% may also have a good predictive value for mortality. However, there is no clear cutoff above which mortality is sharply increased.
Conclusion:
Clinical algorithms had a good diagnostic performance for extents of SSc-ILD on HRCT with clinical and prognostic relevance (≥10% and ≥20%), and were also strongly related to mortality. Non-HRCT-based algorithms could be useful when HRCT is not available. This is the first study to replicate the prognostic algorithm proposed by Goh et al. in a developing country.
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