Real-World Sarilumab Use and Rule Testing to Predict Treatment Response in Patients with Rheumatoid Arthritis: Findings from the RISE Registry

Rheumatol Ther. 2023 Aug;10(4):1055-1072. doi: 10.1007/s40744-023-00568-8. Epub 2023 Jun 22.


Introduction: Clinical trial findings may not be generalizable to routine practice. This study evaluated sarilumab effectiveness in patients with rheumatoid arthritis (RA) and tested the real-world applicability of a response prediction rule, derived from trial data using machine learning (based on C-reactive protein [CRP] > 12.3 mg/l and seropositivity [anticyclic citrullinated peptide antibodies, ACPA +]).

Methods: Sarilumab initiators from the ACR-RISE Registry, with ≥ 1 prescription on/after its FDA approval (2017-2020), were divided into three cohorts based on progressively restrictive criteria: Cohort A (had active disease), Cohort B (met eligibility criteria of a phase 3 trial in RA patients with inadequate response/intolerance to tumor necrosis factor inhibitors [TNFi]), and Cohort C (characteristics matched to the phase 3 trial baseline). Mean changes in Clinical Disease Activity Index (CDAI) and Routine Assessment of Patient Index Data 3 (RAPID3) were evaluated at 6 and 12 months. In a separate cohort, predictive rule was tested based on CRP levels and seropositive status (ACPA and/or rheumatoid factor); patients were categorized into rule-positive (seropositive with CRP > 12.3 mg/l) and rule-negative groups to compare the odds of achieving CDAI low disease activity (LDA)/remission and minimal clinically important difference (MCID) over 24 weeks.

Results: Among sarilumab initiators (N = 2949), treatment effectiveness was noted across cohorts, with greater improvement noted for Cohort C at 6 and 12 months. Among the predictive rule cohort (N = 205), rule-positive (vs. rule-negative) patients were more likely to reach LDA (odds ratio: 1.5 [0.7, 3.2]) and MCID (1.1 [0.5, 2.4]). Sensitivity analyses (CRP > 5 mg/l) showed better response to sarilumab in rule-positive patients.

Conclusions: In real-world setting, sarilumab demonstrated treatment effectiveness, with greater improvements in the most selective population, mirroring phase 3 TNFi-refractory and rule-positive RA patients. Seropositivity appeared a stronger driver for treatment response than CRP, although optimization of the rule in routine practice requires further data.

Keywords: ACPA; CDAI; CRP; RISE registry; Real-world; Rheumatoid arthritis; Sarilumab; Seronegative; Seropositive; Treatment response.

Plain language summary

Rheumatoid arthritis (RA) is a condition that may cause joint damage, if untreated. Sarilumab is an advanced medication, approved for treating moderate-to-severe RA in patients not responding to initial standard medicines. Clinical trials have shown that sarilumab improves RA symptoms; however, some people do not respond. This is a common problem in RA treatment. Physicians measure proteins in people’s blood (called biomarkers; e.g., anticyclic citrullinated peptide antibodies [ACPA], C-reactive protein [CRP], and rheumatoid factor [RF]) to predict a medicine’s response. A previous study showed that people with positive blood tests for ACPA and CRP (> 12.3 mg/l) responded well to sarilumab; this study was based on machine learning (a branch of science using computers) and identified factors that could be linked to treatment benefits. The present study analyzed routine data of 2949 people from the ACR-RISE Registry and showed an improvement in RA symptoms after 6 and 12 months of sarilumab, with a greater improvement noted in patients previously treated with other medicines. Biomarkers were tested in 205 people to check whether they could predict treatment response in day-to-day life. People were called rule-positive if they tested positive for RF and/or ACPA with CRP > 12.3 mg/l, and otherwise rule-negative. After 24 weeks of treatment, rule-positive people had a greater chance of disease improvement than rule-negative people. These results showed the benefits of sarilumab in RA in routine care and suggested the usefulness of machine learning in identifying biomarkers that physicians can use to make treatment decisions.