New risk model is able to identify patients with a low risk of progression in systemic sclerosis

• Published in: Rheumatic & musculoskeletal diseases open Vol. 7; no. 2; p. e001524
• Main Authors: van Leeuwen, Nina Marijn, Maurits, Marc, Liem, Sophie, Ciaffi, Jacopo, Ajmone Marsan, Nina, Ninaber, Maarten, Allaart, Cornelia, Gillet van Dongen, Henrike, Goekoop, Robbert, Huizinga, Tom, Knevel, Rachel, De Vries-Bouwstra, Jeska
• Format: Journal Article
• Language: English
• Published: London BMJ Publishing Group LTD 31.05.2021; BMJ Publishing Group
• Series: Original research
• Subjects: Clinical medicine; Laboratories; Machine learning; Patients; Scleroderma; Stem cells; Systemic Sclerosis; Variables

ObjectivesTo develop a prediction model to guide annual assessment of systemic sclerosis (SSc) patients tailored in accordance to disease activity.MethodsA machine learning approach was used to develop a model that can identify patients without disease progression. SSc patients included in the prosp...