Predictors of remission, erosive disease and radiographic progression in a Colombian cohort of early onset rheumatoid arthritis: a 3-year follow-up study

Abstract

The objective of the study is to find predictors of remission, radiographic progression (RP), and erosive disease in a cohort of patients with early onset rheumatoid arthritis (EORA) that followed a therapeutic protocol aiming at remission, in a real world tight-control setting. EORA patients were enrolled in a 3-year follow-up study. Clinical, biological, immunogenetic, and radiographical data were analyzed. Radiographs were scored according to Sharp-van der Heijde (SvdH) method. RP was defined by an increase of 3 units in 36 months. Remission was defined as DAS28 <2.6. A stepwise multiple logistic regression model was used to identify independent predictors of the three target outcomes. One hundred twenty-nine patients were included. Baseline disease activity was high. Significant overall improvement was observed, but only 33.3 % achieved remission. At 36 month, 50.4 % (65) of patients showed erosions. RP was observed in 62.7 % (81) of cases. Statistical analysis showed that baseline SvdH score was the only predictive factor associated with the three outcomes evaluated. Lower HAQ-DI and absence of autoantibodies were predictive of remission. Higher levels of ESR and presence of erosions at entry were predictive of RP. Independent baseline predictors of incident erosive disease were anti-CCP and RF positivity, symptom duration at baseline >3 months, and presence of HLA-DRB1 shared epitope. Radiographic damage at baseline was the main predictor of outcomes. Autoantibodies, HAQ and ESR at baseline, symptom duration before diagnosis, and HLA-DRB1 status had influence on clinical course and development of structural joint damage in Colombian RA patients.

Predictors of severe radiographic progression in patients with early rheumatoid arthritis: A Prospective observational cohort study

“…Moreover, this variable was the predictor with the highest contribution to the predictive power of the final model and the strongest predictor of severe radiographic progression in the multivariable model ( P = 0.004). These results support previous findings that baseline SHS is an independent predictor of radiographic progression . In a recent study, baseline SHS was the main predictor of outcome, such as remission and erosive disease as well as radiographic progression .…”

“…These results support previous findings that baseline SHS is an independent predictor of radiographic progression. 2,15,[20][21][22] In a recent study, baseline SHS was the main predictor of outcome, such as remission and erosive disease as well as radiographic progression. 22 These results also suggest that patients with severe radiographic damage at baseline may require more aggressive management, even if their pain is mild, their levels of inflammatory markers are low and their disease durations are short.…”

Clinical Significance of Serum Levels of ROM (Reactive Oxygen Metabolites) in Patients With Rheumatoid Arthritis Treated With Biologic Agents as a Predictor for the CDAI-, SDAI-, and Boolean-Remission

“…Previous studies have demonstrated that the baseline HAQ was associated with remission after treatment. Quintana-Duque et al showed that a lower HAQ-disability index (DI) score and absence of autoantibodies were predictive of remission [10].…”

“…To date, several factors have been reported to be associated with clinical remission during treatment [10][11][12]. Furthermore, novel biomarkers for predicting remission were identi ed [13,14]; however, their clinical signi cance in the treatment of RA remains unclear.…”

Polymorphisms in STAT4, PTPN2, PSORS1C1 and TRAF3IP2 Genes Are Associated with the Response to TNF Inhibitors in Patients with Rheumatoid Arthritis

“…Predictive biomarkers of response to therapy with bDMARDs could enable selection of the optimal treatment for the individual patients. Evidence assessed the value of age, gender, concomitant drugs, body mass index, or smoking status for predicting response to treatment [5–7]. Moreover, RA disease duration, disease activity, functional status, presence of autoantibodies [rheumatoid factor (RF) and anti-citrullinated peptide antibodies (ACPA)], and previous therapies can influence drug response [8–11].…”