Article Text
Abstract
Objective The aim of our work is to analyze the clinical and demographic features and nailfold capillary changes in patients with SLE-related PAH compared to a group of SLE patients without PAH.
Methods We identified and selected 20 patients with SLE and type I PAH and collected demographic, clinical and laboratory features from 8 rheumatology centers across Europe. We could perform NVC on 9 patients. We selected as controls 68 patients with SLE who underwent cardiopulmonary screening to exclude PAH: we collected demographic, clinical and laboratory features and performed NVC. The presence of SD pattern was assessed according to Smith et al (Autoimmunity Reviews 2019). Patients satisfied the 2019 EULAR/ACR SLE classification criteria. We excluded patients with a diagnosis of mixed tissue disease and overlap syndrome.
Results All patients with SLE-PAH were female; age and disease duration were not different from SLE patients without PAH. LAC+ and anti-RNP+ was more prevalent in patients with SLE-PAH. No differences were observed for anti-Sm, anti-Ro, anti-La and anti-phospholipid antibodies. Of clinical features, skin and CNS involvement were more prevalent in patients with SLE-PAH than in SLE controls. Raynaud’s phenomenon was more prevalent in patients with SLE-PAH than in SLE controls. In patients with SLE-PAH we observed a significantly higher prevalence of scleroderma pattern at NVC than in SLE controls: patients with SLE-PAH showed a lower number of capillary density and a higher frequency of megacapillaries. In multivariate analysis, Raynaud phenomenon and anti-RNP are predictors of PAH in patients with SLE. The McFadden’s R-squared for the model is 0.30.
Conclusions Our data show that LAC+, RNP+, Raynaud’s, Skin and CNS involvement and a SD pattern at NVC is more prevalent in patients with SLE PAH than in patients with SLE without PAH. Our results point to a generalized microvascular involvement and a hypercoagulation state in patients with SLE-PAH. The variables we identified could be used to implement a screening algorithm to identify patients with SLE with a high risk of developing PAH.
Acknowledgements The study was supported by SLEuro.
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