Few studies compared adalimumab to other targeted therapies in head-to-head randomized clinical trials (RCTs) for rheumatoid arthritis (RA), but multiple comparisons are not available. This Bayesian Network Meta-Analysis evaluated which targeted therapy is more likely to achieve ACR50 response with good safety at 24 weeks of treatment in RA. A systematic literature review was conducted for head-to-head phase 3 RCTs that compared adalimumab to other targeted therapies in combination with methotrexate (MTX) or as monotherapy to treat RA patients, and searched through MEDLINE, EMBASE, Cochrane Library and Clinicaltrial.gov. The outcomes of interest were ACR50 response and withdrawals due to adverse events at 24 weeks. WinBUGS 1.4 software (MRC Biostatistics Unit, Cambridge, UK) was used to perform the analyses, using a random effect model. Sixteen studies were included in the analysis. The most favorable SUCRA for the ACR50 response rate at 24 weeks of treatment in combination with MTX was ranked by upadacitinib, followed by baricitinib, tofacitinib and filgotinib. As monotherapy, the highest probability was ranked by tocilizumab followed by sarilumab. No significant differences in safety profile among treatment options were found. Jak-inhibitors in combination with MTX and interleukin-6 antagonism as monotherapy showed the highest probability to achieve ACR50 response after 24 weeks of treatment. None of assessed targeted therapies were associated to risk of withdrawal due to adverse events. Key messages: Direct and indirect comparison between adalimumab and other targeted therapies demonstrated some differences in terms of efficacy that may help to drive RA treatment. Jak-inhibitors and interleukine-6 antagonists ranked as first in the probability to achieve ACR50 response after 24 weeks of treatment in combination with methotrexate or monotherapy, respectively.

Background/Objectives: Pulmonary arterial hypertension (PAH) is a complication of systemic sclerosis (SSc), and several screening algorithms have been proposed for the early detection of PAH in SSc. This study aimed to evaluate the predicting values of the DETECT algorithm for SSc-PAH screening in patients with SSc undergoing right heart catheterization (RHC) based on 2015 ESC/ERS echocardiographic criteria in a real-life setting. Methods: Patients fulfilling the 2013 ACR/EULAR classification criteria for SSc and with available data for PAH screening with the DETECT algorithm and the 2015 ESC/ERS echocardiographic criteria were retrospectively enrolled from January to June 2017 and then followed for 5 years. Baseline and annual clinical, laboratory, and instrumental data were collected. Results: A total of 33 out of 131 (25%) patients were selected based upon the ESC/ERS echocardiographic criteria, but 30 (23%) underwent RHC, while 51 (39%) patients with SSc were positive based on the DETECT algorithm. PAH diagnosis was confirmed in 28/30 cases (93.3%). The DETECT algorithm showed lower specificity and positive predictive value (PPV) (p < 0.0001) but higher sensitivity and negative predictive value (NPV) (p < 0.0001) than ESC/ERS criteria. Notably, patients with SSc with a negative DETECT screening at baseline had a low probability of developing PAH during a 5-year follow-up (OR 0.15, 95% CI 0.10–0.60—p < 0.0001). Conclusions: The DETECT algorithm has proven to be an easy, fast, and inexpensive tool for screening PAH in patients with SSc. Overall, a low probability of PAH using DETECT is highly predictive of a good prognosis.