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LO-004 Frequency and determinants of flare and persistently active disease in a large multinational prospective lupus cohort
  1. Yanjie Hao1,
  2. Rangi Kandane-Rathnayake2,
  3. Ning Li2,
  4. Worawit Louthrenoo3,
  5. Yi-Hsing Chen4,
  6. Jiacai Cho5,
  7. Aisha Lateef5,
  8. Laniyati Hamijoyo6,
  9. Shue Fen Luo7,
  10. Yeong-Jian Wu7,
  11. Sandra Navarra8,
  12. Leonid Zamora8,
  13. Zhanguo Li9,
  14. Yuan An9,
  15. Sargunan Sockalingam10,
  16. Yasuhiro Katsumata11,
  17. Masayoshi Harigai11,
  18. Zhuoli Zhang12,
  19. Madelynn Chan13,
  20. Jun Kikuchi14,
  21. Tsutomu Takeuchi14,
  22. Sang-Cheol Bae15,
  23. Fiona Goldblatt16,
  24. Sean O’Neill17,
  25. Kristine Ng18,
  26. Annie Law19,
  27. Duminda Basnayake20,
  28. Nicola Tugnet21,
  29. Sunil Kumar22,
  30. Michael Tee23,
  31. Cherica Tee24,
  32. Yoshiya Tanaka25,
  33. CS Lau26,
  34. Vera Golder2,
  35. Alberta Hoi2,
  36. Eric Morand2,
  37. Shereen Oon1,27 and
  38. Mandana Nikpour1,27
  1. 1Department of Medicine at St. Vincent’s Hospital Melbourne, the University of Melbourne, Australia
  2. 2School of Clinical Sciences at Monash Health, Monash University, Australia
  3. 3Division of Rheumatology in Department of Internal Medicine, Chiang Mai University Hospital, Thailand
  4. 4Division of Allergy, Immunology and Rheumatology, Taichung Veterans General Hospital, Taiwan
  5. 5Rheumatology Division, University Medical Cluster, National University Hospital, Singapore
  6. 6Division of Rheumatology, Department of Internal Medicine, Padjadjaran University, Indonesia
  7. 7Department of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital, Taiwan
  8. 8Bone and Joint Center, University of Santo Tomas Hospital, Philippines
  9. 9Department of Rheumatology and Immunology, People’s Hospital Peking University Health Science Center, China
  10. 10Department of Medicine, University of Malaya, Malaysia
  11. 11Institute of Rheumatology, Tokyo Women’s Medical University, Japan
  12. 12Department of Rheumatology and Immunology, Peking University First Hospital, China
  13. 13Department of Rheumatology, Allergy and Immunology, Tan Tock Seng Hospital, Singapore
  14. 14Division of Rheumatology, Department of Internal Medicine, Keio University, Japan
  15. 15Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Republic of Korea
  16. 16Department of Rheumatology, Flinders Medical Centre and Royal Adelaide Hospital, Australia
  17. 17Rheumatology Department, Liverpool Hospital, Australia
  18. 18Department of Medicine, North Shore Hospital, New Zealand
  19. 19Department of Rheumatology and Immunology, Singapore General Hospital, Singapore
  20. 20Division of Nephrology, Teaching Hospital, SriLanka
  21. 21Department of Rheumatology, Greenlane Clinical Centre, New Zealand
  22. 22Department of Rheumatology, Middlemore Hospital, New Zealand
  23. 23Department of Physiology, Philippine General Hospital, University of the Philippines, Philippines
  24. 24Department of Pediatrics, Philippine General Hospital, University of the Philippines, Philippines
  25. 25The First Department of Internal Medicine, University of Occupational and Environmental Health, Japan
  26. 26Division of Rheumatology and Clinical Immunology, Department of Medicine, University of Hong Kong, Hong Kong
  27. 27Rheumatology Department, St. Vincent’s Hospital Melbourne, Australia


Background The current commonly used definitions of flare may not be able to capture patients with a persistently active disease (PAD) course. This study sought to identity the frequency and determinants of flare and PAD in an Asia-Pacific cohort.

Methods Data from Asia-Pacific SLE patients collected between 2013 and 2020 were included. Flare was assessed using the SELENA-SLEDAI flare index (SFI) and PAD was defined as a SLEDAI-2K score of >4, excluding serology, on >2 consecutive visits. Data from 2013 to 2015 were used to model flare and PAD in 2016 through logistic regression and model properties were tested for prediction of flare and PAD in 2020 using the data from 2017 to 2019.

Results During median 2.5 (1.0–5.1) years, 53.1% (2180/4106) of patients experienced at least one episode of flare (flare incidence 0.49 per patient-year). 1786 (43.5%) patients experienced PAD including 368 patients (9.0%) who did not achieve the definition of flare. In the predictive model for flare, being from a country with GDP<$20,000, current smoking, prior mucocutaneous involvement, arthritis, nephritis and low complements were risk factors, and achieving low disease activity state (LLDAS) for ≥50% of follow-up time during the previous three years was a protective factor. Prior nephritis and higher time-adjusted SLEDAI score in the previous three years were predictors for subsequent PAD while spending ≥50% of follow-up time in LLDAS during the previous three years was protectively associated with PAD (table 1). The two models gave 72% and 83.8% correct prediction of flare and PAD in 2020, respectively.

Conclusions Both flare and PAD were common disease activity patterns in SLE, with 9% of patients having PAD that was not captured by the SFI definition. Our predictive models may help identify patients at high risk of flare or PAD and enable targeted interventions to achieve better outcomes.

Abstract LO-004 Table 1

Multivariate logistic regression analysis for the predictors of flare and persistently active disease (PAD) in 2016 from the data of 2013–2015

  • systemic lupus erythematosus
  • flare
  • persistently active disease

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