Frequency and Determinants of Flare and Persistently Active Disease in a Large Multinational Prospective Lupus Cohort

Hao Y 1 , Hansen D 2 , Louthrenoo W 3 , Chen YH 4 , Cho J 5 , Lateef A 6 Show all authors , Hamijoyo L 7 , Luo SF 8 , Wu YJ 8 , Navarra S 9 , Zamora L 9 , Li Z 10 , Sockalingam S 11 , Katsumata Y 12 , Harigai M 12 , Ji L 13 , Zhang Z 13 , Chan M 14 , Kikuchi J 15 , Takeuchi T 16 , Bae SC 17 , Goldblatt F 18 , O'Neill S 19 , Ng KPL 20 , Basnayake BMDB 21 , Tugnet N 22 , Ohkubo N 23 , Tanaka Y 23 , Tee C 24 , Tee M 24 , Lau CS 25 , Li N 26 , Golder V 27 , Hoi A 27 , Kandane-Rathnayake R 26 , Morand E 27 , Oon S 28 , Nikpour M 29 , Asia Pacific Lupus Collaboration

Affiliations 

  • 1 The University of Melbourne and St Vincent's Hospital Melbourne, Melbourne, Victoria, Australia, and Peking University First Hospital, Beijing, China
  • 2 St Vincent's Hospital Melbourne, Melbourne, Victoria, Australia
  • 3 Chiang Mai University Hospital, Chiang Mai, Thailand
  • 4 Taichung Veterans General Hospital, Taichung, Taiwan
  • 5 National University Hospital, Singapore
  • 6 Woodlands Health, Singapore
  • 7 Padjadjaran University/Hasan Sadikin General Hospital, Bandung, Indonesia
  • 8 Chang Gung Memorial Hospital, Taoyuan City, Taiwan
  • 9 University of Santo Tomas Hospital, Manila, Philippines
  • 10 People's Hospital Peking University Health Science Center, Beijing, China
  • 11 University of Malaya, Kuala Lumpur, Malaysia
  • 12 Tokyo Women's Medical University, Tokyo, Japan
  • 13 Peking University First Hospital, Beijing, China
  • 14 Tan Tock Seng Hospital, Singapore
  • 15 Keio University, Tokyo, Japan
  • 16 Keio University, Tokyo, and Saitama Medical University, Saitama, Japan
  • 17 Hanyang University, Seoul, South Korea
  • 18 Flinders Medical Centre, Adelaide, South Australia, Australia
  • 19 Liverpool Hospital, Liverpool, New South Wales, Australia
  • 20 Health New Zealand Waitemata, Auckland, New Zealand
  • 21 Teaching Hospital Kandy, Kandy, Sri Lanka
  • 22 Greenlane Clinical Centre, Auckland, New Zealand
  • 23 University of Occupational and Environmental Health, Kitakyushu, Japan
  • 24 University of the Philippines, Manila, Philippines
  • 25 The University of Hong Kong, Hong Kong
  • 26 Monash University, Melbourne, Victoria, Australia
  • 27 Monash University and Monash Health, Melbourne, Victoria, Australia
  • 28 The University of Melbourne and St Vincent's Hospital Melbourne, Melbourne, Victoria, Australia
  • 29 The University of Melbourne and St Vincent's Hospital Melbourne, Melbourne, Victoria, and University of Sydney and Royal Prince Alfred Hospital, Sydney, New South Wales, Australia
ACR Open Rheumatol, 2025 Mar;7(3):e70007.
PMID: 40012261 DOI: 10.1002/acr2.70007

Abstract

OBJECTIVE: In contrast to relapsing-remitting patterns, persistently active disease (PAD) is a disease activity pattern in patients with systemic lupus erythematosus (SLE) that is inadequately studied. We sought to identify the frequency and determinants of flare and PAD in SLE.

METHODS: Flare was defined using the Safety of Estrogens in Lupus Erythematosus National Assessment version of the Systemic Lupus Erythematosus Disease Activity Index (SELENA-SLEDAI flare index), and PAD was defined as an SLEDAI-2K score of ≥4, excluding serology only, on two or more consecutive visits with a maximum six-month interval. Multivariable logistic regression was used to develop predictive models for flare and PAD, which were tested in an independent validation subset.

RESULTS: Among 3,811 patients over 2.8 (interquartile range 1.0-5.3) years of follow-up, 2,142 (56.2%) experienced flare and 1,786 (46.9%) had PAD, with 368 (9.7%) experiencing PAD but not flare. The most common flare features were nephritis and arthritis, whereas PAD was most commonly characterized by renal or mucocutaneous activity. After adjusting for prednisone dose and use of antimalarials and immunosuppressants, low gross domestic product in country of residence, smoking, arthritis, nephritis, and low complement levels were predictive for flare, whereas being in a low disease activity state for ≥50% of follow-up time (LLDAS50) was a protective factor. Renal activity and higher time-adjusted mean SLEDAI-2K were predictive of PAD, whereas LLDAS50 was protective. The models developed gave 72.1% and 83.8% correct classification of flare and PAD, respectively, in the validation cohort.

CONCLUSION: Both flare and PAD are common disease activity patterns in SLE; both predict organ damage accrual but differ in disease features and predictive factors. Because 9.7% of patients experience PAD but not flare, flare measures alone do not adequately capture all patients in whom disease control is suboptimal.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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