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

Abstract

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 (Plt;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.