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SAT0443 A Prediction Model for Progressive Disease in Systemic Sclerosis
  1. J. Meijs1,
  2. A. Schouffoer1,
  3. N. Ajmone Marsan2,
  4. T. Stijnen3,
  5. M. Ninaber4,
  6. T. Huizinga1,
  7. J. de Vries-Bouwstra1
  1. 1Rheumatology
  2. 2Cardiology
  3. 3Medical Statistics
  4. 4Pulmonology, LUMC, Leiden, Netherlands

Abstract

Background Several algorithms have been developed to predict 5-year survival in systemic sclerosis (SSc) (1-3). Ideally, in order to guide individualized management of SSc patients, a model combining outcome parameters for several organ systems and mortality predicting disease course at short term, should be available.

Objectives To develop a model that assesses the risk for progressive disease in SSc patients at short term, in order to guide clinical management.

Methods Baseline characteristics and one year follow-up results of 163 SSc patients referred to a multidisciplinary health care program 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 ≥3), 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 determine 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 Sixty-seven patients had progressive disease, including 8 patients who died ≤18 months after first evaluation. Multivariable analysis showed that decreased maximum oxygen uptake as % predicted, adjusted for age, gender and use of immunosuppressive therapy at baseline, was significantly associated with progressive disease (Table 1). Using the prediction model, the predicted chance for progressive disease increased from a pretest chance of 39% to 61-83%.

Table 1.

Independent predictive variables for progressive disease based on multivariable logistic regression analysis

Conclusions Using the prediction model the chance for progressive disease for individual patients could be doubled. Maximum oxygen uptake as % predicted was identified as relevant parameter, indicating that exercise testing should be incorporated in baseline screening.

References

  1. Beretta L, Santaniello A, Cappiello F, Chawla NV, Vonk MC, Carreira PE, et al. Development of a five-year mortality model in systemic sclerosis patients by different analytical approaches. Clin Exp Rheumatol 2010 March;28(2 Suppl 58):S18-S27.

  2. Bryan C, Knight C, Black CM, Silman AJ. Prediction of five-year survival following presentation with scleroderma: development of a simple model using three disease factors at first visit. Arthritis Rheum 1999 December;42(12):2660-5.

  3. Fransen J, Popa-Diaconu D, Hesselstrand R, Carreira P, Valentini G, Beretta L, et al. Clinical prediction of 5-year survival in systemic sclerosis: validation of a simple prognostic model in EUSTAR centres. Ann Rheum Dis 2011 October;70(10):1788-92.

Disclosure of Interest None declared

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