Prevention of disease flares by risk-adapted stratification of therapy withdrawal in juvenile idiopathic arthritis: results from the PREVENT-JIA trial

Abstract

ObjectivesTo investigate the ability of high-sensitivity C-reactive protein (hsCRP) and S100A12 to serve as predictive biomarkers of successful drug withdrawal in children with clinical remission of juvenile idiopathic arthritis (JIA).MethodsThis multicentre trial (PREVENT-JIA) enrolled 119 patients with JIA in clinical remission, and 100 patients reached the intervention phase in which the decision whether to continue or stop treatment was based on S100A12 and hsCRP levels. Patients were monitored for 12 months after stopping medication for flares of disease. Results were compared with withdrawal of therapy without biomarker-based stratification in patients from the German Biologika in der Kinderrheumatologie (BiKeR) pharmacovigilance registry.ResultsIn the PREVENT-JIA group, 49 patients had a flare, and 45% of patients stopping medication showed flares within the following 12 months. All patients (n=8) continuing therapy due to permanently elevated S100A12/hsCRP at more than one visit flared during the observation phase. In the BiKeR control group, the total flare rate was 62%, with 60% flaring after stopping medication. The primary outcome, time from therapy withdrawal to first flare (cumulative flare rate after therapy withdrawal), showed a significant difference in favour of the PREVENT-JIA group (p=0.046; HR 0.62, 95% CI 0.38 to 0.99). As additional finding, patients in the PREVENT-JIA trial stopped therapy significantly earlier.ConclusionBiomarker-guided strategies of therapy withdrawal are feasible in clinical practice. This study demonstrates that using predictive markers of subclinical inflammation is a promising tool in the decision-making process of therapy withdrawal, which translates into direct benefit for patients.Trial registration numberISRCTN69963079.

Systemic juvenile idiopathic arthritis in French Afro-Caribbean children, a retrospective cohort study

“…Our patients were on therapy for a small part of the time they were being followed up by specialists, and a large majority of those who transitioned to adult care had been weaned from treatment for over a year. This probably illustrates the pediatricians’ doubts about the future outcomes of these patients [ 16 ]. Adult/child studies on the evolutionary profile of these patients are necessary to assess the risk of recurrence in adulthood.…”

Successful stopping of biologic therapy for remission in children and young people with juvenile idiopathic arthritis

“…Well-designed trials that include serological or imaging biomarkers for absence of inflammation [ 25–27 ] are key to identify those patients with truly inactive disease vs those with subclinical inflammation not detectable by routine examination and tests, but likely to become apparent following a decrease or cessation of biologic therapy. The PREVENT-JIA trial, using pro-inflammatory protein biomarkers (S100A12 and high-sensitivity CRP) as a decision tool for stopping biologic therapy in patients in clinical remission, found fewer flares in those where biomarker levels were considered prior to stopping and longer time from stopping medication until first flare [ 28 ]. While CRP was available at start of biologic therapy in the current analysis, it was unavailable at the time of biologic stop and thus excluded from the Cox model.…”

When to stop medication in juvenile idiopathic arthritis

“…However, Hinze et al [35] found that elevated S100A8/9 levels did not predict flare following TNFi withdrawal. Two studies found S100A12 to be a potentially useful predictive marker for stopping bDMARDs, although the correlation was moderate [35,36 ▪ ]. Antibodies to the nuclear oncoprotein DEK also show promise as a biomarker, with higher DEK autoantibody levels in patients who flared after TNFi discontinuation, although antibody levels at the time of discontinuation did not significantly differ between those who did and did not subsequently flare, limiting their current use as predictive biomarkers [37].…”