Use of platelet inhibitors for digital ulcers related to systemic sclerosis: EUSTAR study on derivation and validation of the DU-VASC model
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Use of platelet inhibitors for digital ulcers related to systemic sclerosis: EUSTAR study on derivation and validation of the DU-VASC model. / Garaiman, Alexandru; Steigmiller, Klaus; Gebhard, Catherine; Mihai, Carina; Dobrota, Rucsandra; Bruni, Cosimo; Matucci-Cerinic, Marco; Henes, Joerg; de Vries-Bouwstra, Jeska; Smith, Vanessa; Doria, Andrea; Allanore, Yannick; Dagna, Lorenzo; Anić, Branimir; Montecucco, Carlomaurizio; Kowal-Bielecka, Otylia; Martin, Mickael; Tanaka, Yoshiya; Hoffmann-Vold, Anna-Maria; Held, Ulrike; Distler, Oliver; Becker, Mike Oliver; EUSTAR Collaborators.
in: RHEUMATOLOGY, Jahrgang 62, Nr. SI, 06.02.2023, S. SI91-SI100.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Use of platelet inhibitors for digital ulcers related to systemic sclerosis: EUSTAR study on derivation and validation of the DU-VASC model
AU - Garaiman, Alexandru
AU - Steigmiller, Klaus
AU - Gebhard, Catherine
AU - Mihai, Carina
AU - Dobrota, Rucsandra
AU - Bruni, Cosimo
AU - Matucci-Cerinic, Marco
AU - Henes, Joerg
AU - de Vries-Bouwstra, Jeska
AU - Smith, Vanessa
AU - Doria, Andrea
AU - Allanore, Yannick
AU - Dagna, Lorenzo
AU - Anić, Branimir
AU - Montecucco, Carlomaurizio
AU - Kowal-Bielecka, Otylia
AU - Martin, Mickael
AU - Tanaka, Yoshiya
AU - Hoffmann-Vold, Anna-Maria
AU - Held, Ulrike
AU - Distler, Oliver
AU - Becker, Mike Oliver
AU - EUSTAR Collaborators
AU - Kötter, Ina
N1 - © The Author(s) 2022. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com.
PY - 2023/2/6
Y1 - 2023/2/6
N2 - 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.
AB - 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.
U2 - 10.1093/rheumatology/keac405
DO - 10.1093/rheumatology/keac405
M3 - SCORING: Journal article
C2 - 35904554
VL - 62
SP - SI91-SI100
JO - RHEUMATOLOGY
JF - RHEUMATOLOGY
SN - 1462-0324
IS - SI
ER -