POS0734 EXTRAPOLATION OF LONG-TERM OUTCOMES IN SYSTEMIC LUPUS ERYTHEMATOSUS: REPLICATING A HOPKINS LUPUS COHORT ANALYSIS WITH THE SYSTEMIC LUPUS INTERNATIONAL COLLABORATING CLINICS (SLICC) INCEPTION COHORT
Authors: A. E. Clarke, Y. St-Pierre, V. Paly, I. N. Bruce, C. Malmberg, A. Briggs, Y. Zhang, J. Choi, A. Brennan
Journal: Annals of the Rheumatic Diseases (2021)

Background: A disease model of systemic lupus erythematosus (SLE) that predicts short-term outcomes (disease activity and prednisone use) and links them to long-term outcomes (accrual of organ damage and mortality) was previously developed in a single center SLE cohort (Johns Hopkins [JH]) to support health economic analyses (Watson 2015), which has not been comprehensively replicated in other cohorts or contexts. Objectives: As part of an effort to develop and refine this existing disease model, the aim of this study was to replicate the previously estimated network of risk equations for short- and long-term outcomes in the SLICC Inception Cohort, an international cohort of patients (33 centers,11 countries). Methods: The SLICC Inception Cohort enrolled patients fulfilling ACR Classification Criteria for SLE within 15 months of diagnosis from 1999-2011 with annual follow-up through April 2020. The network of risk equations included two linear random effects models to predict (1) change in annual average Systemic Lupus Disease Activity Index (SLEDAI) score based on patient characteristics and the presence of renal, hematological, and immunological involvement in the prior year and (2) average annual prednisone dose based on SLEDAI score in the same year. These equations were then linked to parametric survival models that predicted time to the occurrence of organ damage (system-specific based on the ACR/SLICC Damage Index) and mortality. We compared model performance between the SLICC Cohort and the original analysis from the JH Cohort. Results: In comparison to the JH cohort (N=1354), the SLICC cohort (N=1697) had a smaller fraction of patients of African descent (39% vs 17%) and shorter disease duration at entry (4.8 vs 0.5 years). In the first equation predicting change in annual SLEDAI score, predictors were generally aligned with the same direction and significance, with the exception of renal involvement in the prior period, which had a positive association with change in SLEDAI in the SLICC cohort but was negatively associated in the JH cohort (Table 1). The second equation predicting prednisone dose was also consistent with the original analysis showing a significant positive association between higher disease activity and prednisone use. In all of the parametric survival analyses (individual organ damage and mortality models), coefficients were generally in the same direction and magnitude, though some were no longer significant in the SLICC cohort. Conclusion: The relationships identified in the original analysis were broadly replicated in the SLICC Inception Cohort. Observed differences may reflect differences in the patient populations, structure of the two cohorts (prevalent vs inception), and frequency of visits (quarterly visits in the JH cohort vs annual visits with the SLICC cohort may more closely capture a decrease in SLEDAI associated with treatment specifically related to renal involvement). Additional analyses relaxing the requirement to completely align with the original structure are underway to further assess the predictive accuracy of these models. References: [1]Watson P, et al. Rheumatology (Oxford). 2015;54(4):623-32. JH Cohort (N=1354 ) SLICC Cohort (N=1697 ) Female, % 92.9 88.8 African descent, % 38.8 16.7 Disease duration at entry, mean (SD), years 4.8 (6.3) 0.5 (0.3) SLEDAI at first visit, mean (SD) 3.7 (4.1) 5.4 (5.4) Change in average annual SLEDAI Coefficient Coefficient Constant 1.491 * 5.762 * Annual average SLEDAI in prior period −0.460 * −0.755 * Male gender −0.080 −0.207 Log transformation of age −0.241 * −1.134 * Renal involvement in prior period −0.301 * 0.627 * African descent 0.383 * 0.126 Increased DNA binding in prior period 0.276 * 0.939 * Low complement in prior period 0.484 * 0.775 * Hematological involvement in prior period 0.104 −0.025 Anemia in prior period 0.152 ** 0.144 Associated annual average prednisone dose (mg/day ) Constant 3.475 * 2.738 * SLEDAI in same period 0.777 * 0.648 * *p&lt;0.001; **p&lt;0.05 Acknowledgements: We acknowledge the support on this abstract of the following investigators of the Systemic Lupus International Collaborating Clinics: John Hanly - john.hanly@nshealth.ca Caroline Gordon - p.c.gordon@bham.ac.uk Sang-Cheol Bae - scbae@hanyang.ac.kr Juanita Romero-Diaz - juanita.romerodiaz@gmail.com Jorge Sanchez-Guerrero - jorge.sanchez-guerrero@uhn.ca Sasha Bernatsky - sasha.bernatsky@mcgill.ca Ann Clarke - aeclarke@ucalgary.ca Daniel Wallace - dwallace@ucla.edu/danielwallac@gmail.com David Isenberg - d.isenberg@ucl.ac.uk Anisur Rahman - anisur.rahman@ucl.ac.uk Joan Merril - JTMmail@aol.com Paul Fortin - paul.fortin@crchudequebec.ulaval.ca Dafna Gladman - dafna.gladman@utoronto.ca Murray Urowitz - m.urowitz@utoronto.ca Ian Bruce - ian.bruce@manchester.ac.uk Michelle Petri - mpetri@jhmi.edu Ellen Ginzler - ellen.ginzler@downstate.edu MA Dooley - Mary_Dooley@med.unc.edu Rosalind Ramsey-Godman - rgramsey@northwestern.edu Susan Manzi - susan.manzi@ahn.org; Susanmanzi@gmail.com Andreas Jonsen - andreas.jonsen@med.lu.se Graciela Alarcon - galarcon@uab.edu Ronald van Vollenhoven - r.vanvollenhoven@amsterdamumc.nl Cynthia Aranow - CAranow@Northwell.edu Meggan Mackay – mmackay@northwell.edu Guillermo Ruiz-Irastorza - r.irastorza@outlook.es Sam Lim - sslim@emory.edu Murat Inanc - drinanc@istanbul.edu.tr; minanc2008@gmail.com Kenneth Kalunian - kkalunian@ucsd.edu Soren Jacobsen - sj@dadlnet.dk Christine Peschken - christine.peschken@umanitoba.ca Diane Kamen - kamend@musc.edu Anca Askanase - ada20@columbia.edu Disclosure of Interests: Ann E Clarke Consultant of: BMS, AstraZeneca, GSK, and Exagen Diagnostics., Yvan St-Pierre: None declared, Victoria Paly: None declared, Ian N. Bruce Speakers bureau: GSK, UCB, Consultant of: BMS, Eli Lilly, GSK, Astra Zeneca, Merck Serono; UCB, ILTOO, Aurinia, Grant/research support from: Genzyme/Sanofi, GSK, Roche, UCB, Chiara Malmberg: None declared, Andrew Briggs Speakers bureau: Alexion, AstraZeneca, Bayer, BMS, Daiichi Sankyo, Eisai, Gilead, GSK, Kite, Merck, Novartis, Rhythm, Roche, Sanofi, Takeda, Consultant of: Alexion, AstraZeneca, Bayer, BMS, Daiichi Sankyo, Eisai, Gilead, GSK, Kite, Merck, Novartis, Rhythm, Roche, Sanofi, Takeda, Yuanhui Zhang Shareholder of: Bristol Myers Squibb., Employee of: Bristol Myers Squibb., Jiyoon Choi Shareholder of: JNJ., Employee of: BMS, Alan Brennan Consultant of: Alan Brennan is a paid consultant on advisory boards regarding cost-effectiveness modelling., Grant/research support from: Alan Brennan received research grants.