Do We Have Good Activity Indices in Systemic Sclerosis?

Background: No fully validated index is available for assessing overall disease activity in systemic sclerosis (SSc).

Objectives: To estimate the effect of disease activity as measured by different disease activity indices on the risk of subsequent organ damage.

Methods: The European Systemic sclerosis study group activity index (EScSG AI), the European Scleroderma Trials and Research Group Activity Index (r-EUSTAR AI), 12 point activity index proposed by Minier (12point AI) were calculated for 91 patients; the CRISS (The Composite Response Index for Systemic Sclerosis) for patients included after 2016. Data were analysed by parametric and non-parametric tests and logistic regression.

Results: EscSG AI, r-EUSTAR AI and 12point AI correlated with lung involvement. EScSG AI and r-EUSTAR AI correlated with diffuse skin involvement. EscSG AI correlated with digital ulcers and diffuse cutaneous involvement and r-EUSTAR AI with a renal crisis. Bivariate analysis showed an inverse correlation between the three disease activity scores and forced vital capacity (FVC) (p<0.001) and diffusing capacity for carbon monoxide (DLCO) (p<0.001) and positive correlation with pulmonary fibrosis (p<0.001), modified Rodnan skin score (mRSS) (p<0.001), health assessment questionnaire (HAQ) (p<0.001), systolic pulmonary pressure (sPAP) (p<0.001), C-reactive protein (CRP) (p<0.001) and capillaroscopy scoring (p<0.001) at both baseline visit and the 3-year follow-up visit. Logistic regression revealed that baseline EScSG AI adjusted for gender and age and that baseline 12-point AI both adjusted and unadjusted predicted worse skin involvement at 3-year follow-up; while adjusted EScSG AI predicted decreasing DLCO. Also, 12-point AI predicted a decline of FVC and higher HAQ scores at 3-year follow up; while baseline r-EUSTAR AI was able to predict muscular deterioration, decline of FVC and the increase of HAQ score during 3 years of following. An active disease according to EScSG AI at first visit predicted progression of joint involvement while an active disease at baseline showed by r- EUSTAR AI predicted muscular deterioration, FVC and DLCO worsening, as well as an increase in HAQ score during the follow-up period. r-EUSTAR AI was the only score to predict the decrease of FVC in a multiple regression prediction model (OR= 1.306 (1.025, 1.665), p=0.31) while baseline EScSG AI best predicted worsening of DLCO (OR=1.749 (1.104, 2.772), p=0.017).

Conclusion: Our study could not establish a gold standard to assess disease activity in SSc; especially EscSG AI and r-EUSTAR AI could quantify and predict major organ involvement in daily practice. CRISS can be useful as an outcome measure for patients with short disease duration included in clinical studies.