• Tiffany A Winstone, Deborah Assayag, Pearce G Wilcox, James V Dunne, Cameron J Hague, Jonathon Leipsic, Harold R Collard, and Christopher J Ryerson.
• Department of Medicine, University of British Columbia, Vancouver, BC, Canada.
• Chest. 2014 Aug 1; 146 (2): 422-436.

BackgroundInterstitial lung disease (ILD) is the leading cause of morbidity and mortality in patients with systemic sclerosis (SSc); however, prognostication of SSc-associated ILD (SSc-ILD) remains challenging. We conducted a systematic review to identify variables that predict mortality and ILD progression in SSc-ILD.MethodsThree databases were searched to identify all studies relating to predictors of mortality or ILD progression in SSc-ILD. Studies were eligible if they were published in English and included ≥ 10 adults with SSc-ILD. Two authors independently reviewed and extracted data from acceptable studies.ResultsThe initial search identified 3,145 unique citations. Twenty-seven studies, including six abstracts, met the inclusion criteria. A total of 1,616 patients with SSc-ILD were included. Patient-specific, ILD-specific, and SSc-specific variables predicted mortality and progression; however, most predictors were identified in only one study. Most studies did not fully account for potential confounders, and none of the studies included a validation cohort. Older age, lower FVC, and lower diffusing capacity of carbon monoxide predicted mortality in more than one study. Male sex, extent of disease on high-resolution CT (HRCT) scan, presence of honeycombing, elevated KL-6 values, and increased alveolar epithelial permeability were identified as predictors of both mortality and ILD progression on unadjusted analysis. The extent of disease on HRCT scan was the only variable that independently predicted both mortality and ILD progression.ConclusionsMortality and ILD progression were predicted by several patient-specific, ILD-specific, and SSc-specific factors. Additional prospective studies are required to validate these preliminary findings and to identify combinations of variables that accurately predict the prognosis of SSc-ILD.

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