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Background Cardiovascular involvement is one of the leading causes of mortality in systemic sclerosis (SSc) and is reported to be higher in men as compared with women. However, the cause of this difference is largely unknown. The objective of this study was to assess sex differences in echocardiographic characteristics, including left ventricular global longitudinal strain (LV GLS), as a potential explanation of sex differences in outcomes.Methods A total of 746 patients with SSc from four centres, including 628 (84%, 54±13 years) women and 118 (16%, 55±15 years) men, were evaluated with standard and advanced echocardiographic examinations. The independent association of the echocardiographic parameters with the combined endpoint of cardiovascular events-hospitalisation/death was evaluated.Results Men and women with SSc showed significant differences in disease characteristics and cardiac function. After adjusting for the most important clinical characteristics, while LV ejection fraction and diastolic function were not significantly different anymore, men still presented with more impaired LV GLS as compared with women (−19% (IQR −20% to −17%) vs −21% (IQR: −22% to −19%), p

Objectives To develop a prediction model to guide annual assessment of systemic sclerosis (SSc) patients tailored in accordance to disease activity.Methods A machine learning approach was used to develop a model that can identify patients without disease progression. SSc patients included in the prospective Leiden SSc cohort and fulfilling the ACR/EULAR 2013 criteria were included. Disease progression was defined as progression in ≥1 organ system, and/or start of immunosuppression or death. Using elastic-net-regularisation, and including 90 independent clinical variables (100% complete), we trained the model on 75% and validated it on 25% of the patients, optimising on negative predictive value (NPV) to minimise the likelihood of missing progression. Probability cutoffs were identified for low and high risk for disease progression by expert assessment.Results Of the 492 SSc patients (follow-up range: 2–10 years), disease progression during follow-up was observed in 52% (median time 4.9 years). Performance of the model in the test set showed an AUC-ROC of 0.66. Probability score cutoffs were defined: low risk for disease progression (0.223, NPV:0.78; 44%). The relevant variables for the model were: previous use of cyclophosphamide or corticosteroids, start with immunosuppressive drugs, previous gastrointestinal progression, previous cardiovascular event, pulmonary arterial hypertension, modified Rodnan Skin Score, creatine kinase and diffusing capacity for carbon monoxide.Conclusion Our machine-learning-assisted model for progression enabled us to classify 29% of SSc patients as ‘low risk’. In this group, annual assessment programmes could be less extensive than indicated by international guidelines.