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. 2023 Jun 15:14:1200732.
doi: 10.3389/fimmu.2023.1200732. eCollection 2023.

Risk and Factors associated with disease manifestations in systemic lupus erythematosus - lupus nephritis (RIFLE-LN): a ten-year risk prediction strategy derived from a cohort of 1652 patients

Shirley C W Chan  1 , Yong-Fei Wang  2   3 , Desmond Y H Yap  4 , Tak Mao Chan  4 , Yu Lung Lau  2 , Pamela P W Lee  2 , Wai Ming Lai  5 , Shirley K Y Ying  6 , Niko K C Tse  5 , Alexander M H Leung  7 , Chi Chiu Mok  8 , Ka Lai Lee  9 , Teresa W L Li  1 , Helen H L Tsang  1 , Winnie W Y Yeung  1 , Carmen T K Ho  1 , Raymond W S Wong  1 , Wanling Yang  2 , Chak Sing Lau  1 , Philip H Li  1
Affiliations

Risk and Factors associated with disease manifestations in systemic lupus erythematosus - lupus nephritis (RIFLE-LN): a ten-year risk prediction strategy derived from a cohort of 1652 patients

Shirley C W Chan et al. Front Immunol. .

Abstract

Objectives: Lupus nephritis (LN) remains one of the most severe manifestations in patients with systemic lupus erythematosus (SLE). Onset and overall LN risk among SLE patients remains considerably difficult to predict. Utilizing a territory-wide longitudinal cohort of over 10 years serial follow-up data, we developed and validated a risk stratification strategy to predict LN risk among Chinese SLE patients - Risk and Factors associated with disease manifestations in systemic Lupus Erythematosus - Lupus Nephritis (RIFLE-LN).

Methods: Demographic and longitudinal data including autoantibody profiles, clinical manifestations, major organ involvement, LN biopsy results and outcomes were documented. Association analysis was performed to identify factors associated with LN. Regression modelling was used to develop a prediction model for 10-year risk of LN and thereafter validated.

Results: A total of 1652 patients were recruited: 1382 patients were assigned for training and validation of the RIFLE-LN model; while 270 were assigned for testing. The median follow-up duration was 21 years. In the training and validation cohort, 845 (61%) of SLE patients developed LN. Cox regression and log rank test showed significant positive association between male sex, age of SLE onset and anti-dsDNA positivity. These factors were thereafter used to develop RIFLE-LN. The algorithm was tested in 270 independent patients and showed good performance (AUC = 0·70).

Conclusion: By using male sex, anti-dsDNA positivity, age of SLE onset and SLE duration; RIFLE-LN can predict LN among Chinese SLE patients with good performance. We advocate its potential utility in guiding clinical management and disease monitoring. Further validation studies in independent cohorts are required.

Keywords: lupus nephritis; model; prediction; risk assessment; systemic lupus erythematosus.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Incidence of LN among 1382 patients in the training and validation cohort of RIFLE-LN.
Figure 2
Figure 2
Log rank test of factors associated with LN development: (A) – antids-DNA positivity; (B) – male sex; (C) – age of SLE onset.
Figure 3
Figure 3
Graphical RIFLE-LN risk prediction model for LN development.
Figure 4
Figure 4
Performance of RIFLE-LN in the testing cohort of 270 independent patients.
See this image and copyright information in PMC

References

    1. Aringer M, Costenbader K, Daikh D, Brinks R, Mosca M, Ramsey-Goldman R, et al. . European League against Rheumatism/American college of rheumatology classification criteria for systemic lupus erythematosus. Arthritis Rheumatol (2019) 71(9):1400–12. doi: 10.1002/art.40930 - DOI - PMC - PubMed
    1. Hochberg MC. Updating the American college of rheumatology revised criteria for the classification of systemic lupus erythematosus. Arthritis Rheum (1997) 40(9):1725. doi: 10.1002/art.1780400928 - DOI - PubMed
    1. Petri M, Orbai AM, Alarcon GS, Gordon C, Merrill JT, Fortin PR, et al. . Derivation and validation of the systemic lupus international collaborating clinics classification criteria for systemic lupus erythematosus. Arthritis Rheum (2012) 64(8):2677–86. doi: 10.1002/art.34473 - DOI - PMC - PubMed
    1. Li PH, Wong WH, Lee TL, Lau CS, Chan TM, Leung AMH, et al. . Relationship between autoantibody clustering and clinical subsets in SLE: cluster and association analyses in Hong Kong Chinese. Rheumatol (Oxford) (2013) 52(2):337–45. doi: 10.1093/rheumatology/kes261 - DOI - PubMed
    1. To CH, Mok CC, Tang SS, Ying SK, Wong RW, Lau CS. Prognostically distinct clinical patterns of systemic lupus erythematosus identified by cluster analysis. Lupus (2009) 18(14):1267–75. doi: 10.1177/0961203309345767 - DOI - PubMed

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