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. 2023 Feb 6;62(SI):SI91-SI100.
doi: 10.1093/rheumatology/keac405.

Use of platelet inhibitors for digital ulcers related to systemic sclerosis: EUSTAR study on derivation and validation of the DU-VASC model

Alexandru Garaiman  1 , Klaus Steigmiller  2 , Catherine Gebhard  3 , Carina Mihai  1 , Rucsandra Dobrota  1 , Cosimo Bruni  1   4 , Marco Matucci-Cerinic  4   5   6 , Joerg Henes  7 , Jeska de Vries-Bouwstra  8 , Vanessa Smith  9   10 , Andrea Doria  11 , Yannick Allanore  12 , Lorenzo Dagna  5   6 , Branimir Anić  13 , Carlomaurizio Montecucco  14 , Otylia Kowal-Bielecka  15 , Mickael Martin  16 , Yoshiya Tanaka  17 , Anna-Maria Hoffmann-Vold  18 , Ulrike Held  2 , Oliver Distler  1 , Mike Oliver Becker  1 ; EUSTAR Collaborators
Collaborators, Affiliations

Use of platelet inhibitors for digital ulcers related to systemic sclerosis: EUSTAR study on derivation and validation of the DU-VASC model

Alexandru Garaiman et al. Rheumatology (Oxford). .

Abstract

Objective: To develop and validate the prognostic prediction model DU-VASC to assist the clinicians in decision-making regarding the use of platelet inhibitors (PIs) for the management of digital ulcers in patients with systemic sclerosis. Secondly, to assess the incremental value of PIs as predictor.

Methods: We analysed patient data from the European Scleroderma Trials and Research group registry (one time point assessed). Three sets of derivation/validation cohorts were obtained from the original cohort. Using logistic regression, we developed a model for prediction of digital ulcers (DUs). C-Statistics and calibration plots were calculated to evaluate the prediction performance. Variable importance plots and the decrease in C-statistics were used to address the importance of the predictors.

Results: Of 3710 patients in the original cohort, 487 had DUs and 90 were exposed to PIs. For the DU-VASC model, which includes 27 predictors, we observed good calibration and discrimination in all cohorts (C-statistic = 81.1% [95% CI: 78.9%, 83.4%] for the derivation and 82.3% [95% CI: 779.3%, 85.3%] for the independent temporal validation cohort). Exposure to PIs was associated with absence of DUs and was the most important therapeutic predictor. Further important factors associated with absence of DUs were lower modified Rodnan skin score, anti-Scl-70 negativity and normal CRP. Conversely, the exposure to phosphodiesterase-5 inhibitor, prostacyclin analogues or endothelin receptor antagonists seemed to be associated with the occurrence of DUs. Nonetheless, previous DUs remains the most impactful predictor of DUs.

Conclusion: The DU-VASC model, with good calibration and discrimination ability, revealed that PI treatment was the most important therapy-related predictor associated with reduced DU occurrence.

Keywords: SSc; digital ulcers; platelets inhibitors; prognostic prediction model.

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