Quantitative chest computed tomography is associated with two prediction models of mortality in interstitial lung disease related to systemic sclerosis

Objective: In this multicentre study, we aimed to evaluate the capacity of a computer-assisted automated QCT method to identify patients with SSc-associated interstitial lung disease (SSc-ILD) with high mortality risk according to validated composite clinical indexes (ILD-Gender, Age, Physiology index and du Bois index). Methods: Chest CT, anamnestic data and pulmonary function tests of 146 patients with SSc were retrospectively collected, and the ILD-Gender, Age, Physiology score and DuBois index were calculated. Each chest CT underwent an operator-independent quantitative assessment performed with a free medical image viewer (Horos). The correlation between clinical prediction models and QCT parameters was tested. A value of P < 0.05 was considered statistically significant. Results: Most QCT parameters had a statistically different distribution in patients with diverging mortality risk according to both clinical prediction models (P < 0.01). The cut-offs of QCT parameters were calculated by receiver operating characteristic curve analysis, and most of them could discriminate patients with different mortality risk according to clinical prediction models. Conclusion: QCT assessment of SSc-ILD can discriminate between well-defined different mortality risk categories, supporting its prognostic value. These findings, together with the operator independence, strengthen the validity and clinical usefulness of QCT for assessment of SSc-ILD.