A prediction model for progressive disease in systemic sclerosis. Issue 1 (1st December 2015)

• Record Type: Journal Article
• Authors: Meijs, Jessica; Schouffoer, Anne A; Ajmone Marsan, Nina; Stijnen, Theo; Putter, Hein; Ninaber, Maarten K; Huizinga, Tom W J; de Vries-Bouwstra, Jeska K
• Is Part Of: RMD open. Volume 1:Issue 1(2015)
• Publication Date: 2015-12-01
• Subjects: Epidemiology -- Systemic Sclerosis -- Treatment; Musculoskeletal system -- Diseases -- Periodicals; Rheumatism -- Periodicals; 616.7005
• DOI: 10.1136/rmdopen-2015-000113
• Languages: English
• ISSNs: 2056-5933

Abstract

Abstract : Objective: To develop a model that assesses the risk for progressive disease in patients with systemic sclerosis (SSc) over the short term, in order to guide clinical management. Methods: Baseline characteristics and 1 year follow-up results of 163 patients with SSc referred to a multidisciplinary healthcare programme were evaluated. Progressive disease was defined as: death, ≥10% decrease in forced vital capacity, ≥15% decrease in diffusing capacity for carbon monoxide, ≥10% decrease in body weight, ≥30% decrease in estimated-glomerular filtration rate, ≥30% increase in modified Rodnan Skin Score (with Δ≥5) or ≥0.25 increase in Scleroderma Health Assessment Questionnaire. The number of patients with progressive disease was determined. Univariable and multivariable logistic regression analyses were used to assess the probability of progressive disease for each individual patient. Performance of the prediction model was evaluated using a calibration plot and area under the receiver operating characteristic curve. Results: 63 patients had progressive disease, including 8 patients who died ≤18 months after first evaluation. Multivariable analysis showed that friction rubs, proximal muscular weakness and decreased maximum oxygen uptake as % predicted, adjusted for age, gender and use of immunosuppressive therapy at baseline, were significantly associated with progressive disease. Using the prediction model, the predicted chance for progressive disease increased from a pretest chance of 37% to 67–89%. Conclusions: Using the prediction model, the chance for progressive disease for individual patients could be doubled. Friction rubs, proximal muscular weakness and maximum oxygen uptake as % predicted were identified as relevant parameters.