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AB0608 Predictive factors for long-term survival and disease progression of systemic sclerosis – a longitudinal analysis
  1. AM Gheorghiu1,2,
  2. A Radu1,2,
  3. R Oneata1,2,
  4. A Soare1,2,
  5. R Dobrota1,2,
  6. S Magda2,3,
  7. T Constantinescu2,4,
  8. R Jurcut2,5,
  9. R Sfrenţ-Cornăţeanu2,6,
  10. M Bojincă1,2,
  11. V Stoica1,2,
  12. C Mihai1,2
  1. 1Internal Medicine and Rheumatology, Cantacuzino Hospital
  2. 2Carol Davila University of Medicine and Pharmacy
  3. 3Emergency University Hospital
  4. 4Marius Nasta Institute of Pneumology
  5. 5C.C. Iliescu Institute for Cardiovascular Diseases
  6. 6Physiopathology and Immunology Department, Bucharest, Romania

Abstract

Background Systemic sclerosis (ScS) has unpredictable course and high mortality. Generalised Estimating Equations (GEE) is a technique useful for longitudinal data analysis, using data from all time points and adjusting for within-patient correlation,i.e. correlation between time points within the same patient. GEE does not require a normal distribution of dependent variables, making it attractive for analyzing SSc data.

Objectives To identify predictive factors for death and unfavorable outcomes.

Methods Data of SSc patients with ≥2 visits in our EUSTAR centre in 2004–2016 were analyzed. GEE investigated the relationship over time between outcomes (death, digital ulcers (DUs), forced vital capacity (FVC), modified Rodnan skin score (mRSS)) and potential predictors (age, gender, disease duration, cutaneous subset, mRSS at baseline, DUs history, DLCO, left ventricle ejection fraction (LVEF), proteinuria), separately for each predictor and in combined models.

Results 89 patients (12.4%males, mean±SD age 49.2±12.2years, disease duration 4.1±7.5years) were included, with a follow-up of up to 13years. There were 14deaths, most due to lung involvement (7/14). In multivariable GEE analysis (Table 1), predictors of death were a shorter disease duration, DUs history, and a lower LVEF. Predictors for FVC decrease over time were difuse cutaneous subset (dcSSc), younger age and lower DLCO. Younger age, shorter disease duration and higher baseline mRSS were the most important predictors for higher mRSS at follow-up. The only predictor for the development of new DUs was a history of DUs.

Table 1.

Prediction factors for death and for evolution over time of parameters reflecting disease severity in SSc

Conclusions Patients with shorter disease duration, dcSSc, higher mRSS, lower DLCO and LVEF and a history of DUs had a more unfavorable course. GEE is a robust technique for longitudinal data analysis, excellent for identifying prediction factors in SSc.

Acknowledgements This abstract is part of the QUANTICAP project, UEFIS-CDI PN-II- PT-PCCA-2013–4-1589 grant.

Disclosure of Interest None declared

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