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417 Worldwide picture of renal involvement in systemic lupus erythematosus using a big data analytical approach
  1. M Pérez de Lis Novo1,
  2. M Gandía2,
  3. R Pérez-Álvarez3,
  4. P Brito-Zerón4,
  5. B Kostov5,
  6. A Siso-Almirall6,
  7. D Superville7,
  8. Y Shoenfeld8,
  9. M Ramos-Casals9 and
  10. MA Khamashta10
  1. 1Hospital Juan Canalejo, Anesthesiology and Critical Care, A Coruña, Spain
  2. 2Hospital Puerta del Mar, Rheumatology, Cadiz, Spain
  3. 3Hospital Álvaro Cunqueiro, Department of Autoimmune Diseases, Vigo, Spain
  4. 4Josep Font Laboratory of Autoimmune Diseases- CELLEX-Institut d’Investigacions Biomèdiques August Pi i Sunyer IDIBAPS-, Department of Autoimmune Diseases- ICMiD- Hospital Clínic, Barcelona, Spain
  5. 5Primary Care Research Group- Institut d’Investigacions Biomèdiques August Pi i Sunyer IDIBAP, Primary Care Centre Les Corts- CAPSE, Barcelona, Spain
  6. 6Primary Care Research Group- Institut d’Investigacions Biomèdiques August Pi i Sunyer IDIBAPS, Primary Care Centre Les Corts- CAPSE, Barcelona, Spain
  7. 7Massachusetts Institute of Technology, MIT, Cambridge- Massachusetts, USA
  8. 8Zabludowicz Centre for Autoimmune Diseases- Chaim Sheba Medical Center- Tel Hashomer- Israel, Incumbent of the Laura Schwarz-Kipp Chair for Research of Autoimmune Diseases- Sackler Faculty of Medicine- Tel-Aviv University, Tel-Aviv, Israel
  9. 9osep Font Laboratory of Autoimmune Diseases- CELLEX-Institut d’Investigacions Biomèdiques August Pi i Sunyer IDIBAPS-, Department of Autoimmune Diseases- ICMiD- Hospital Clínic, Barcelona, Spain
  10. 10St Thomas’ Hospital- King’s College University, Lupus Research Unit- The Rayne Institute, London, UK

Abstract

Background and aims Lupus nephritis is the most severe involvements of internal organs that may develop patients with systemic lupus erythematosus (SLE). Studying the worldwide distribution of this feature across geographic regions and ethnic groups using a big data-driven approach may help obtain a more high-definition resolution of lupus nephritis

Methods We made a text-word search in Google between 8th and 15th May 2015 using SLE and 100...100000000 patients. We selected the cohorts in which the frequency of renal involvement was detailed, and we analysed the influence on this frequency of the characteristics of the cohorts.

Results Of the 63 263 patients included, renal involvement was reported in 24 790 patients(38%).The rate of renal involvement was influenced by the predominant ethnicity:it was lower in cohorts in white patients in comparison with a multiethnic origin(p=0.008).The mean age at SLE diagnosis was higher in cohorts in which the mean age was <40 years(p=0.019).The rate of renal involvement was higher in cohorts with a frequency of positive antiDNA antibodies higher than 75%(p=0.031),cohorts with a frequency of positive antiSm antibodies higher than 25%(p=0.046), cohorts with a frequency of positive antiRNP antibodies higher than 25%(p=0.031) and cohorts with a frequency of positive anti-La/SSB antibodies higher than 15%(p=0.025).

Conclusions This big data analytical approach confirm that the rate of renal involvement is influenced by ethnicity, the mean age at SLE diagnosis and the frequency of positive autoantibodies.The higher rates of renal involvement being found in US and Asian studies,with the youngest patients at the time of SLE diagnosis and highest rates of immunopositive patients.

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