Use of risk chart algorithms for the identification of psoriatic arthritis patients at high risk for cardiovascular disease: findings derived from the project CARMA cohort after a 7.5-year follow-up period.

OBJECTIVE: To assess the predictive value of four cardiovascular (CV) risk algorithms for identifying high-risk psoriatic arthritis (PsA) patients. METHODS: Evaluation of patients with PsA enrolled in the Spanish prospective project CARdiovascular in RheuMAtology. Baseline data of 669 PsA patients with no history of CV events at the baseline visit, who were followed in rheumatology outpatient clinics at tertiary centres for 7.5 years, were retrospectively analysed to test the performance of the Systematic Coronary Risk Assessment (SCORE), the modified version (mSCORE) European Alliance of Rheumatology Associations (EULAR) 2015/2016, the SCORE2 algorithm (the updated and improved version of SCORE) and the QRESEARCH risk estimator version 3 (QRISK3). RESULTS: Over 4790 years of follow-up, there were 34 CV events, resulting in a linearised rate of 7.10 per 1000 person-years (95% CI 4.92 to 9.92). The four CV risk scales showed strong correlations and all showed significant associations with CV events (p<0.001). SCORE, mSCORE EULAR 2015/2016 and QRISK3 effectively differentiated between low and high CV risk patients, although the cumulative rate of CV events observed over 7.5 years was lower than expected based on the frequency predicted by these risk scales. Additionally, model improvement was observed when combining QRISK3 with any other scale, particularly the combination of QRISK3 and SCORE2, which yielded the lowest Akaike information criterion (411.15) and Bayesian information criterion (420.10), making it the best predictive model. CONCLUSIONS: Risk chart algorithms are very useful for discriminating PsA at low and high CV risk. An integrated model featuring QRISK3 and SCORE2 yielded the optimal synergy of QRISK3's discrimination ability and SCORE2's calibration accuracy.

Combined use of QRISK3 and SCORE2 increases identification of ankylosing spondylitis patients at high cardiovascular risk: Results from the CARMA Project cohort after 7.5 years of follow-up.

OBJECTIVE: To establish the predictive value of the QRESEARCH risk estimator version 3 (QRISK3) algorithm in identifying Spanish patients with ankylosing spondylitis (AS) at high risk of cardiovascular (CV) events and CV mortality. We also sought to determine whether to combine QRISK3 with another CV risk algorithm: the traditional SCORE, the modified SCORE (mSCORE) EULAR 2015/2016 or the SCORE2 may increase the identification of AS patients with high-risk CV disease. METHODS: Information of 684 patients with AS from the Spanish prospective CARdiovascular in ReuMAtology (CARMA) project who at the time of the initial visit had no history of CV events and were followed in rheumatology outpatient clinics of tertiary centers for 7.5 years was reviewed. The risk chart algorithms were retrospectively tested using baseline data. RESULTS: After 4,907 years of follow-up, 33 AS patients had experienced CV events. Linearized rate=6.73 per 1000 person-years (95 % CI: 4.63, 9.44). The four CV risk scales were strongly correlated. QRISK3 correctly discriminated between people with lower and higher CV risk, although the percentage of accumulated events over 7.5 years was clearly lower than expected according to the risk established by QRISK3. Also, mSCORE EULAR 2015/2016 showed the same discrimination ability as SCORE, although the percentage of predicted events was clearly higher than the percentage of actual events. SCORE2 also had a strong discrimination capacity according to CV risk. Combining QRISK3 with any other scale improved the model. This was especially true for the combination of QRISK3 and SCORE2 which achieved the lowest AIC (406.70) and BIC (415.66), so this combination would be the best predictive model. CONCLUSIONS: In patients from the Spanish CARMA project, the four algorithms tested accurately discriminated those AS patients with higher CV risk and those with lower CV risk. Moreover, a model that includes QRISK3 and SCORE2 combined the best discrimination ability of QRISK3 with the best calibration of SCORE2.

Predictive clinical-genetic model of long-term non-response to tumor necrosis factor-alpha inhibitor therapy in spondyloarthritis.

AIM: Tumor necrosis factor inhibitors (TNFi) are effective in controlling disease activity in spondyloarthritis (SpA). However, in a proportion of patients these treatments are ineffective or lead to adverse events. Recently, alternative therapies, such as interleukin (IL)-17 or IL-23 inhibitors, have emerged in the treatment of these pathologies. This study aimed to determine clinical and genetic predictors of non-response to TNFi treatment in 118 spondyloarthritis patients diagnosed according to Assessment in SpondyloArthritis International Society (ASAS) criteria. METHOD: From the literature, 41 single nucleotide polymorphisms (SNPs) were selected that had previously been associated with TNFi treatment response in spondyloarthropathies, rheumatoid arthritis and psoriasis. A clinical non-response was defined as a decrease of <50% of initial Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) in axial involvement, or a reduction of less than 1.2 of initial Disease Activity Score of 28 joints-C-reactive protein (DAS28-CRP) in patients with only peripheral involvement. Univariate and multivariate hazard ratios (HR) were determined using Cox proportional hazard models to analyze the potential prognostic factors affecting non-response to TNFi treatment. RESULTS: The clinical factors that significantly increased the non-response rate were: global visual analog scale (VAS), CRP, BASDAI, Bath Ankylosing Spondylitis Functional Index (BASFI), and the number of TNFi used. Only rs11591741 SNP showed an association with non-response. In the multivariate analysis, females had a non-response rate 4.46 times higher than males; each one-point increase in the BASFI index increased the non-response rate by 75%, and being a genotype GG vs GC or CC carrier was associated with an almost 4 times greater non-response rate. CONCLUSION: We developed a clinical-genetic model to identify SpA patients with a long-term non-response to TNFi therapy.

Clinical and genetic characteristics of ankylosing spondylitis patients with peripheral arthritis at disease onset.

OBJECTIVES: The aim of this study was to assess the clinical and genetic characteristics associated with the presence of peripheral arthritis (PA) at disease onset in patients with ankylosing spondylitis (AS). METHODS: 456 Spanish AS patients, diagnosed according to the modified New York Criteria, who had at least ten years of follow-up since initial disease onset were selected from the National Spondyloarthropathies Registry (REGISPONSER). 18.9% of AS patients initially presented PA. Clinical variables and 384 single nucleotide polymorphisms (SNPs) distributed in 190 genes were analysed. SNP genotyping was performed using the Illumina GoldenGate genotyping platform. Association tests for allele frequencies and for categorical clinical variables were performed by the χ2 test and with the unpaired t-test for continuous variables. p-values of <0.05 were considered statistically significant. RESULTS: AS patients with PA showed an earlier age of disease onset (p=0.021), longer disease duration (p=0.020) and longer duration of AS symptoms from onset (p=0.034) than AS patients without PA. We found significant associations with the presence of PA at disease onset in 14 SNPs located in 10 genes: HLA-DQB2 (rs2857210 and rs9276615), HLA-DOB (rs2857151, rs2621332 and rs1383261), JAK2 (rs7857730), IL-23R (rs11209008 and rs10489630), CYP1B1 (rs1056836), NELL1 (rs8176786), KL (rs564481), and MEFV (rs224204), IL-2RB (rs743777) and IL-1A (rs1800587). CONCLUSIONS: Both clinical and genetic factors are associated with the presence of PA at disease onset in Spanish AS patients. The results suggest that this subset of AS patients with PA at disease onset might have differentiation factors involved in disease pathogenesis.