Remission and low disease activity matrix tools: results in real-world rheumatoid arthritis patients under anti-TNF therapy

Abstract

Background: Remission/ low disease activity (LDA) are the main treatment goals in rheumatoid arthritis (RA) patients. Two tools showing the ability to predict golimumab treatment outcomes in patients with RA were published. Objectives: To estimate the real-world accuracy of two quantitative tools created to predict RA remission and low disease activity. Methods: Multicenter, observational study, using data from the Rheumatic Diseases Portuguese Register (Reuma.pt), including biologic naïve RA patients who started an anti-TNF as first-line biologic and with at least 6 months of follow-up. The accuracy of two matrices tools was assessed by likelihood-ratios (LR), sensitivity (SN), specificity (SP), positive predictive value (PPV), negative predictive value (NPV) and area under the ROC curve (AUC). Results: 674 RA patients under first-line anti-TNF (266 etanercept, 186 infliximab, 131 adalimumab, 85 golimumab, 6 certolizumab pegol) were included. The median (IQR) age was 53.4 (44.7-61.1) years and the median disease duration was 7.7 (3.7-14.6) years. The majority were female (72%). Most patients were RF and/or ACPA positive (75.5%) and had erosive disease (54.9%); 58.6% had comorbidities. At 6-months, 157 (23.3%) patients achieved remission (DAS28 ESR < 2.6) and 269 (39.9%) LDA (DAS28 ESR ≤ 3.2). Area under the curve for remission in this real-world sample was 0.756 [IC 95% (0.713-0.799)] and for LDA was 0.724 [IC 95% (0.686 -0.763)]. The highest LR (8.23) for remission state was obtained at a cut-off ≥ 67%, with high specificity (SP) (99.6%) but low sensitivity (SN) (3.2%). A better balance of SN and SP (65.6% and 73.9%, respectively) was observed for a cut-off >30%, with a LR of 2.51, PPV of 43.3% and NPV of 87.6%. Conclusion: In this population, the accuracy of the prediction tool was good for remission and LDA. Our results corroborate the idea that these matrix tools could be helpful to select patients for anti-TNF therapy.