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Klotho is a transmembrane and soluble glycoprotein that governs vascular integrity. Previous studies have demonstrated reduced serum klotho concentrations in patients with systemic sclerosis (SSc), and it is known that klotho deficiency can impair the healing of digital ulcers related to microvessel damage. The aim of this study was to evaluate the association between serum klotho levels and nailfold capillaroscopic abnormalities in SSc patients. We retrospectively enrolled 54 consecutive patients with SSc diagnosed on the basis of the 2013 EULAR/ACR criteria [11 with diffuse SSc; 47 females; median age 68.0 years (IQ 18); median disease duration 11.0 years (IQ 7)]. Serum klotho concentrations were determined by means of an enzyme-linked immunosorbent assay. On the basis of the 2000 classification of Cutolo et al., 14 patients had normal nailfold capillaroscopic findings, 8 had an early scleroderma pattern, 21 an active scleroderma pattern, and 11 a late scleroderma pattern. The median serum klotho concentration was 0.29 ng/mL (IQ 1). Regression analysis of variation showed an inverse correlation between serum klotho concentrations and the severity of the capillaroscopic pattern (p=0.02; t -2.2284), which was not influenced by concomitant treatment. Logistic regression did not reveal any significant association between the risk of developing digital ulcers and nailfold capillaroscopic patterns, serum klotho levels, or concomitant medications. The presence of avascular areas significantly correlated with calcinosis (p=0.006). In line with previous studies, our findings confirm that klotho plays a role in preventing microvascular damage detected with nailfold capillaroscopy.

BackgroundSjogren’s syndrome (SSj) is a systemic disease with autoimmune pathogenesis, with prevalent involvement of the lacrimal and salivary exocrine glands, classified as primary or secondary on the basis of the association with other rheumatic diseases.The epidemiological data in the literature are variable due to the heterogeneity of the populations examined and the different classification criteria used.In a recent systematic review by Baodon Qin et al. In a recent systematic review by Baodon Qin et al. the average incidence rate was 7 cases every 100,000 people/year (range 5-9) with higher values in the Caucasian and Asian populations, with a women/men ratio of 9.3 (95% CI 3.35 to 13.18), while the prevalence rate was 61 cases per 100,000 inhabitants (range 10-90) with a female/male ratio of 10.7 (95% CI 7.35-15.62).ObjectivesThe aim of the study was to estimate the prevalence rate of primary SSj in real-life, through analysis of administrative data.MethodsAn epidemiological research was carried out as at 31 December 2020, through the analysis of all adult patients residing in Piedmont (northwest of Italy) with exemption code (EC) for SSj (code 030) and any association with other ECs for rheumatological pathologies or for pathologies considered according to the 2016 EULAR/ACR classification criteria; specifically, the EC were considered for rheumatoid arthritis, ulcerative colitis and Crohn’s disease, chronic hepatitis (active), HIV infection, affected subjects from malignant neoplastic pathologies, tumors of uncertain behavior, systemic lupus erythematosus, Hashimoto’s thyroiditis, undifferentiated connective tissue disease; mixed cryoglobulinemia, antiphospholipid antibody syndrome (primary form), primary and familial amyloidosis, hereditary/familial autoinflammatory syndromes, microscopic polyangiitis, polyarteritis nodosa, sarcoidosis, dermatomyositis, polymyositis, mixed connective tissue disease, progressive systemic sclerosis.ResultsThere were 3889 patients with EC for SSj as at 31 December 2020, of which 2611 with active EC.The number of assisted people alive with the aforementioned exemption was 2601, while those with other exemptions in the list were 1020. Of these patients, 1581 had only the exemption from code 030 as at 31 December 2020; the distribution of the other pathologies analyzed is shown in the tables below (Table 1).Of the total number of patients with active exemption, 2442 were female, while 159 were male, with a female/male ratio of 15.3; the distribution by gender is shown in Figure 1.Considering that the Piedmont population on 31 December 2020 was 4 274 945 inhabitants (ref: National Institute of Statistics), the estimated prevalence rate was 4.7 cases per 10 000 inhabitants.Table 1.Distribution of EC in patients residing in Piedmont as at 31/12/2020Diseases with ECN of patientsRheumatoid Arthritis250Ulcerative colitis and Crohn’s disease18chronic hepatitis52HIV infection2Systemic Lupus Erythematosus122Subjects affected by malignant neoplastic pathologies330Hashimoto’s thyroiditis280Undifferentiated Connective Tissue Sisease86Mixed Cryoglobulinemia6Antiphospholipid Antibody Syndrome8Primary And Familial Amyloidosis3Hereditary/Familial Autoinflammatory Syndromes1Microscopic Polyangiitis2Polyarteritis Nodosa1Sarcoidosis5Dermatomyositis2Polymyositis5Mixed connective tissue disease25Progressive systemic sclerosis56Figure 1.Distribution by gender of patients residing in Piedmont with exemption for Sjogren’s Syndrome. F: Female; M: MaleConclusionThis research has some limitations, including the retrospective design and the use of administrative data which may be affected by the aforementioned difficulty of classification as well as incorrect attributions of exemption, both for SSj and for associated pathologies.Our research highlights a number of patients with primary SSj lower than the prevalence figure that defines a rare disease, i.e. a prevalence of less than 5 cases per 10,000 inhabitants.Further studies are needed to confirm these preliminary data.Reference[1]Baodong Qin et al. Ann Rheum Dis. 2015 Nov;74(11):1983-9.Acknowledgements:NIL.Disclosure of InterestsNone Declared.

Hydroxychloroquine (HCQ) has been used safely for over 60 years in rheumatic patients. However, following its recent use in covid-19 disease, its safety has been questioned, following controversial reports of cardiac toxicity1, possibly related to a prolongation of the QT interval2. To explore the influence of chronic treatment with hydroxychloroquine on QT interval in rheumatic patients, and the possible effects of drug-to-drug interference3. 12-lead electrocardiogram tracings were recorded with standard equipment in 229 ambulatory patients (SLE = 53, RA = 52, SSc = 56, UCTD = 38, Others = 30). The present analysis was performed on corrected QT intervals (QTc) calculated according to Framingham formula (QTc = QT+0.154 (1−RR)), with ULN = 449 ms in males, and 467 ms in females. Estimated glomerular filtrate rate (eGFR) was calculated from serum creatinine with the CKD-EPI equation. The influence on QTc values of demographic variables, chronic (≥3 months) HCQ treatment, and of the use of selected comedications -Statins, Angiotensin Converting Enzyme inhibitors (ACEi), Angiotensin Receptor Blockers (ARBs), Selective Serotonin Reuptake Inhibitors (SSRIs), Proton-Pump Inhibitors (PPI), Calcium Channel Blockers (CCBs) – were evaluated by parametric or non parametric statistical methods, as appropriate. All statistic al analyses were performed with the IBM SPSS statistical package version 25. QTc duration was not associated with the use of Statins, ACEi, ARBs, or SSRIs (p = 0.454, 0.276, 0.475, and 0.131 respectively), but was significantly prolonged in patients treated with HCQ (421.26 ± 19.19 vs 410.55 ± 21.18 msec, p < 0.001), PPIs (420.57 ± 21.45 vs 410.89 ± 18.12 ms, p < 0.001), and CCBs (424.22 ± 25.97 vs 415.59 ± 19.62 ms, p < 0.033). Furthermore, as reported in Fig. 1, our data show a trend - albeit not statistically significant - towards an additive effect on QT prolongation of the association of PPIs and CCBs with HCQ, even more evident in the case of association of the 3 drug classes. [Display omitted] In this study, the QTc interval was significantly prolonged in patients treated with hydroxychloroquine as compared to controls, although significant prolongation was extremely infrequent. Furthermore, our data revealed signs of drug-drug interference, suggesting that regular monitoring of the electrocardiogram is advisable in these patients, often undergoing cotreatment with multiple drugs. [1]Imad M. Tleyjeh, et al. The Cardiac Toxicity of Chloroquine or Hydroxychloroquine in COVID-19 Patients: A Systematic Review and Meta-regression Analysis. Mayo Clin Proc Innov Qual Outcomes. 2020 Nov 2 doi: 10.1016/j.mayocpiqo.2020.10.005 [Epub ahead of print]. [2]Teodoro J. Oscanoa, et al. Frequency of Long QT in Patients with SARS-CoV-2 Infection Treated with Hydroxychloroquine: A Meta-analysis. Int J Antimicrob Agents. [3]Byung Jin Choi, et al. Risk of QT prolongation through Drug-drug Interactions between Hydroxychloroquine and Concomitant Drugs Prescribed in Real-world Practice. Preprint from Research Square, 22 Sep 2020 DOI: 10.21203/rs.3.rs-79572/v1 PPR: PPR217328. None declared Table 1Demographic and clinical variables in patients treated with HCQ (HCQ+) and in controls (HCQ-).NAgeYrs±SDFemaleN%eGFRmL/min/1.73m2StatinsN%ACEiN%ARBN%SSRIN%PPIN%CCBN%All22958.02±14.3620690.087.1418.962912.74821.8198.3146.113860.33013.1HCQ+13258.71±14.4912292.487.0020.041813.63224.2118.396.88060.61712.9HCQ-9757.51±14.308486.687.3217.471111.31616.588.255.25859.81313.4p0.5320.1830.8970.6900.1891.0000.7821.0001.000Demographic variables, and the use of evaluated comedications were not different in HCQ+ and HCQ- patients (Table 1). In the whole population, the QTc mean duration was 416.72 ± 20.70 ms, and was correlated with age (r = 0.215, p= 0.001), but not with gender (p = 0.548), eGFR (r = -0.93, p = 0.163), or disease (p = 0.092). In only 4 patients (HCQ+: 3 (2.3%) – HCQ-: 1 (1%), p = 0.639) QTc duration was above ULN.

Rheumatoid Arthritis (RA) is associated with increased cardiovascular (CV) morbidity and mortality and osteometabolic alterations risk, associated with chronic inflammation, the use of glucocorticoids (GC) and the reduced physical exercise.[1] The objective of the study is to cross-sectionally estimate cardiovascular risk and osteometabolic status in patients (pts) with RA and to evaluate the association with some disease parameters such as positivity of autoantibodies, disease activity and steroid therapy. At the current time, 61 consecutive pts with diagnosis of RA, admitted to the Rheumatology Unit of the University Hospital of Turin, were prospectively recruited and assessed for cardiometabolic risk by the Endocrinology Unit, by undergoing laboratory and instrumental tests. The following prevalences were observed: arterial hypertension (52%), type 2 diabetes mellitus (7%), dyslipidemia (56%), osteoporosis (42%), and vertebral fracture (30%). At the univariate analysis, the enrolled pts were divided according to serodiagnosis, GC therapy and disease remission. No statistically significant results were highlighted stratifying population by serodiagnosis. Pts with high disease activity showed lower bone mineral density (BMD) values [BMD femoral trochanter: 0.53± 0.08 vs 0.60 ± 0.08 (g/m2), p=0.031] and T-score value on bone densitometry [T-score Femoral total: -1.88 ± 0.53 vs -1.07 ± 0.83, p=0.005], higher percentage of osteoporosis [67% vs 27%, p=0.047] and vertebral fractures [60% vs 12%, p=0.001], and higher sarcopenia score [SARC-F: 5 (3-7) vs 2 (2-4), p=0.020], in comparison with pts with remission disease. These differences were not confirmed when the population was divided according to the use of GC therapy. For CV risk factors, disease activity group showed a trend of higher prevalence compared to remission group, but without reaching statistical significance. At the multivariate analysis, advanced age (p=0.001), GC therapy (p=0.021) and copeptin (p=0.002) showed an inverse association and lumbar T-score (p=0.002) a direct one with lumbar trabecular bone score (TBS). Moreover, male gender (p=0.001) revealed a direct and significant association, while copeptin (p=0.086) an inverse and not significant one with percentage of lean mass on total densitometry, correcting for advanced age, duration of disease, GC therapy, and disease activity. In the last model, advanced age (p<0.001) and copeptin (p<0.001) showed a direct and significant association with HeartSCORE, correcting for parameters of disease while serodiagnosis, duration of disease, GC therapy, and disease activity. At univariate analysis osteometabolic alterations were associated with disease activity, but not with GC therapy and serodiagnosis. At the multivariate analysis, the association of disease activity and TBS values, did not reach the statistical significance, probably for the loss of statistical power. However, GC therapy, as well as advanced age, low lumbar T-score and high value of copeptin, remained independently associated with lower TBS value. Disease parameters were not associated with lower percentage of lean mass at total body densitometry and higher HeartSCORE values, while advanced age and copeptin were associated with bone health and cardiovascular risk. [1] Mackey RH et al. Rheum Dis Clin North Am 2018. NIL. None Declared. Table 1Multivariate linear regression analysisCovariates associated with lumbar TBSB-coefficientCI 95%p-valueAge-0.005(-0.007- -0.002)0.001GC-0.068(-0.125- -0.011)0.021T-Score0.049(0.020-0.079)0.002Copeptin-0.014(-0.023- -0.006)0.002Covariates associated with HeartSCORE cardiovascular risk scoreB-coefficientCI 95%p-valueAge0.242(0.180-0.304)<0.001Duration of disease-0.045(-0.099-0.008)0.096RF and/or ACPA +-0.224(-1.855-1.407)0.782GC0.840(-0.454-2.135)0.195Copeptin0.345(0.172-0.518)<0.001Remission0.502(-0.848-1.853)0.454

Giant cell arteritis (GCA) is the most common primary systemic vasculitis in western countries with the highest incidence among persons 70–79 years of age. Treatment has been with glucocorticoids (GCs) alone for many decades but recently Tocilizumab (TCZ) has demonstrated efficacy in reducing GC dose and flare rates in patients with GCA. Therefore, both early diagnosis and regular monitoring are necessary for the correct management of GCA. The COVID-19 pandemic has led to decisions by the governments of the countries involved, aimed above all at reducing the contagion. This has also led to reductions in health activities, limiting them to those of urgency by reducing or canceling checkups involving the risk of a time gap which for the GCA meant the interruption of clinical monitoring and therapeutic adjustment. At the same time, the pandemic situation has stimulated remote monitoring activities, through telephone contacts or video calls carried out by the rheumatologist. EULAR identified a minimal data set aimed at research and for clinical use, which includes the main clinical and instrumental data to be taken into consideration in monitoring the patient. For many data a clinical examination is not necessary but an interview is sufficient. We activated the TELEMACOV protocol (TELEmedicine and Management of the patient affected by giant cell arteritis during the COVid-19 pandemic) monitoring the follow-up of patients affected by GCA through telemedicine tools in order to maintain an effective and risk-free follow-up in a pathology with a high risk of relapse. The purpose of the study is to evaluate the effectiveness of telemedicine in the follow-up of the patient with GCA. We evaluated patients (pts) with a clinical diagnosis of GCA (received in previous periods) who were admitted to the our Rheumatology Unit. They were monitored monthly by telephone from 9 March to 9 June 2020 (during lockdown). All patients were asked questions divided according to the sub-groups listed below: - Onset of new symptoms or their recurrence - Exams carried out - Current therapy - Satisfaction of telephone call We performed 148 remote monitoring visits in 37 pts. The cohort was mainly composed by female (77,8%) and had a mean age of 71,85 ± 9,25 years. They were affected by GCA, with a mean duration of 5,3±2,3 months. The characteristics of these pts and the course of the disease are reported in Table 1. Pts treated with TCZ reduced their GC dose more than patients treated with GC alone (p: 0.032). Only one patient (treated with GC alone) had an ocular flare with the need to increase the dosage of the GC with good response and rapid improvement. Furthermore, all patients considered this type of monitoring very satisfactory according to the Likert scale (1-5) with mean 4,4±0,2. Our study has shown how telemedicine can be well used in pts with GCA as a possible alternative, for a limited period, to traditional visits, especially in a fragile population such as the elderly and more exposed to the risk of SARS-COV2 infection. [1]Ehlers L et al. Ann Rheum Dis 2019 Sep;78(9):1160-1166 [2]Wagner E et al. Prim Care. 2012 Jun;39(2):241-59. None declared Table 1Disease course and remote monitoringParametersBaseline (at diagnosis)Pre-lock downFirst InterviewSecond InterviewPatients treated with only GC (19 pts)MeanSDMeanSDMeanSDMeanSDESR74,0823,9513,028,212,756,429,846,63CRP mg/l47,442,21,011,11,552,291,562,24Hgb mg/dl11,62,613,22,2412,753,1913,582,12PGA (1-10)7,552,052,711,792,241,592,151,04EGA (1-10)6,991,852,191,421,971,131,430,97GC mg52,7218,310,16,957,64,516,673,1Patients treated with GC and TCZ (18 pt)ESR74,5636,5811,33,859,894,713,374,4CRP mg/l47,841,71,130,651,580,851,620,73Hgb mg/dl11,363,8812,984,9112,434,313,255,15PGA (1-10)7,143,112,472,151,890,41,930,72EGA (1-10)6,932,720,950,730,680,430,410,72GC mg51,7618,858,044,385,883,924,164,32PGA: Patient Global Assessment EGA: Evaluator Global Assessment;

Systemic Sclerosis (SSc) is an autoimmune rheumatic disease characterized by excessive production and accumulation of collagen in the skin and internal organs and by injuries to small arteries. Impairment of the musculoskeletal system is one of the main causes of disability in SSc, indeed, about 90% of these patients have a loss of hand function. To date, the degree of skin involvement is evaluated through a semi-quantitative method called Rodnan Skin Score (RSS) or Modified-RSS (MRSS). However, MRSS is a method that has limitations related to the operator and his experience and does not provide information on joint mobility. Arduino® is an open source integrated online platform based on easy to use hardware and software. It is a system for creating interactive projects by inserting a special configuration code, using the Arduino® development environment. Through this platform it is possible to create electronic devices with specific purposes to lead the possibility of integrating different kits (eg types of sensors) in relation to the object of study. We have therefore created an electronic instrument (Sclerodermic Hand Sensor - SHS) independent operator and easily reproducible in order to measure the degree of mobility (flexion) of the hand in patients with SSc (Fig.1). The aim was to evaluate whether the SHS was able to highlight significant differences between patients with SSc and healthy patients. We recruited 20 female patients with SSc according to ACR criteria with a mean age of 50.8 ± 15.5 years and 20 healthy (HC) patients with a mean age of 44.3 ± 10.8 years (Tab.1), in order to test the effectiveness and sensitivity of the SHS tool. The results showed a significant difference between the two groups of patients (SSc vs HC) independent of the measurement method used as expected (Goniometer SSc / HC: Δ45.80 ° p: 0.003 SHS SSc / HC: Δ65.17 ° p: 0.002, Fig.1c), however the device created with Arduino® proved to be more sensitive than the goniometric measurement in detecting the degree of joint flexion (p: 0.002). The flexion sensor, indeed, unlike the goniometer, evaluates the simultaneous articular excursion of the entire finger (MCF, IFP and IFD) and not just one segment (Fig.1). This technology application, thanks to the creation of dedicated electronic devices, allows the physicians to be supported in clinical practice with independent operating tools. The tool we propose could be a valid support in accurately assessing the joint and indirectly skin involvement of sclerodactyly in this type of patient, especially in the context of a clinical trial to evaluate the efficacy of a treatment. Further studies are needed to compare with other methods to assess hand disability in SSc such as the use of HAMIS (Hand Mobility in Scleroderma) test. [1]Sandqvist G et al. J Rheumatol. 2016 Jul;43(7):1356-62. [2]https://www.arduino.cc. [Display omitted] None declared Table 1SSc Patients CharacteristicsChararcteristicsSScPatients (n°, subset D=Diffuse; L=Limited)20 (9D/11L)Age, mean ± SD years50.8 ± 15.5Duration of Raynaud's Phenomenon (mean ± SD years)12.8 ± 4.4Duration of SSc (mead ± SD years)8.4 ± 3.6MRSS (mean ± SD years)15.9 ± 5.3

Background:In the last years, there was a dramatic increase of advanced DMARDs for psoriatic arthritis (PsA). There are many b/tsDMARDs with different mechanisms of action (MoA) to consider after the failure of the first advanced line of therapy. As a consequence, the rheumatologists can implement a cycling (same MoA) or swap strategy.Objectives:The aim of this real world based study is to investigate if, after the failure of a TNF inhibitor (TNFi), another TNFi or an interleukin 17 inhibitor (IL17i) is the most effective choice (from a clinical practice point of view).Methods:For this retrospective observational multicenter study, PsA (according to CASPAR criteria) patients who failed a first line TNFi and subsequently received a TNFi or IL17i were enrolled. For each subject, the following characteristics were recorded: anamnestic (age, sex) and disease-related (duration, presence of axial PsA, DAPSA, advanced therapies carried out, csDMARDs and steroids association) data, treatment duration and cause of failure. Patients were divided in two groups according to the strategy used: cycling (i.e. from TNFi to TNFi) (CG) or swap (i.e from TNFi to IL17i) (SG). The Kaplan-Meier curves compared the effectiveness of the two strategies, while multivariable Cox analysis (stepwise) identified the risk factors affecting treatment retention rate.Results:347 subjects were enrolled (M:F 160:187; age 56 years IQR 47-63). In CG and SG were 217 and 130 patients, respectively. The 5 years retention rate in SG was higher than in CG (57,8% vs 45,2%; p=0.1) (Figure 1). Taking into account the whole cohort, the factors predictive of treatment interruption were DAPSA (HR 1.03 CI95% 1.01-1.05; p=0.0009) and swap strategy (HR 0.49 CI95% 0.27-0.90; p=0.02).Conclusion:The swap strategy (i.e. from TNFi to IL17i) was the best choice in PsA patients who failed the first line of advanced treatment. This finding supports the hypothesis that changing the MoA can improve the chances to identify the most effective PsA treatment.Figure 1.REFERENCES:NIL.Acknowledgements:NIL.Disclosure of Interests:Alarico Ariani Amgen, Janssen, Federica Lumetti: None declared, Simone Parisi: None declared, Olga Addimanda: None declared, BERND RAFFEINER: None declared, Alberto Lo Gullo: None declared, Rosario Foti: None declared, Antonella Farina: None declared, Francesco Girelli: None declared, Maddalena Larosa Abbvie, Amgen, UCB, Romina Andracco: None declared, Marino Paroli: None declared, Patrizia Del Medico: None declared, Aldo Molica Colella: None declared, Marta Priora: None declared, Aurora Ianniello: None declared, Francesca Ometto: None declared, Elena Bravi: None declared, Alessandra Bezzi: None declared, Palma Scolieri: None declared, Rosetta Vitetta: None declared, Alessandro Volpe: None declared, Massimo Reta: None declared, Mirco Magnani: None declared, Elisa Visalli: None declared, Giorgio Amato: None declared, Francesco De Lucia: None declared, Roberta Foti: None declared, Simone Bernardi: None declared, Dario Camellino: None declared, Gerolamo Bianchi: None declared, Natalia Mansueto: None declared, Giulio Ferrero: None declared, Rosalba Caccavale: None declared, Veronica Franchina: None declared, FRANCESCO MOLICA COLELLA: None declared, Gilda Sandri: None declared, Carlo Salvarani: None declared, Dilia Giuggioli: None declared, Francesca Serale: None declared, Valeria Nucera: None declared, Cecilia Giampietro: None declared, Eleonora Di Donato: None declared, Giuditta Adorni: None declared, Gianluca Lucchini: None declared, Daniele Santilli: None declared, Ilaria Platè: None declared, Eugenio Arrigoni: None declared, Maria Cristina Focherini: None declared, Fabio Mascella: None declared, Vincenzo Bruzzese: None declared, Enrico Fusaro: None declared, Maria Chiara Ditto: None declared, Alessia Fiorenza: None declared, Guido Rovera: None declared, Antonio Marchetta: None declared, Andrea Becciolini: None declared.

Background:After the publication of the ORAL Surveillance trial1, the European Medical Agency (EMA) recently recommended prescribing JAK inhibitors (JAKi) in patients with rheumatoid arthritis (RA) only after an adequate risk/benefit assessment. Indeed, according to EMA recommendations, patients ≥65 year-old, former or current smokers, at high risk of developing major cardiovascular events (MACEs), thromboembolic events or cancers should receive JAKi only when other treatments, such as TNF-inhibitors, are not appropriate. Unlike randomised controlled trials, real-world data seem to be reassuring promising and, in fact, the prescription rate of JAKi has dramatically risen in the last years.Objectives:The primary objective was to assess whether being at high risk according to EMA recommendations can impact JAKi’s discontinuation rate. The secondary aim was to find out other possible predictors of JAKi’s discontinuation.Methods:This was a retrospective Italian study carried out in 22 Italian centres since 2017. All patients with RA on treatment with JAKi were included in the dataset. This cohort was subdivided into two groups: “high risk patients” and “low risk” according to EMA recommendations. The first group included patients ≥65 year-old, past or current smokers and having at least one cardiovascular or neoplastic comorbidity. The following variables were collected at baseline: sex, age, disease duration (years), smoking habit, BMI, comorbidities (diabetes, hypertension, dyslipidaemia, cancer, previous MACEs), positive RF/ACPA, associated conventional DMARDs, concomitant use of prednisone and dosage (mg/day), previous use of JAKi, discontinuation reasons, time to discontinuation (days), b/tsDMARDS naïve patients, DAS28-ESR at baseline.Results:A total of 693 patients were enrolled. 48 patients were excluded due to missing data. Overall features of our cohort are summarised in Table 1 (N=645). 372 (57.7%) patients received baricitinib, 135 (20.9%) tofacitinib, 86 (13.3%) upadacitinib and 52 (8.11%) filgotinib. 21 (3.2%). 141 (21.9%) patients discontinued JAKi after a median time of 366 days (IQR 155-914). “High-risk patients according to EMA” were the majority of our cohort (n=384, 59.5%) vs 261 “low-risk patients” (40.5%).Reasons for discontinuation were primary inefficacy (n=48, 7.4%), secondary inefficacy (n=25, 3.9%), infections (n=8, 1.2%), pulmonary embolism/deep venous thrombosis (n=6, 0.9%), cancer (n=5, 0.8%), deaths (n=2, 0.3%), and other causes (n=21, 3.3%) including remission status in 1 patient. Notably, VZV infection determined JAKi’s withdrawal in 3 patients and pulmonary embolism/deep venous thrombosis in 6 patients (all treated with baricitinib). At multivariate stepwise Cox analysis, predictors of discontinuation were: prednisone dosage [Hazard Ratio -(HR)- 1.1, 95% confidence interval- (CI)- 1.05-1.17], use of selective JAKis(HR 4.1, 95% CI 1.80-9.10), and absence of RF/ACPA (HR 0.46, 95% CI 0.26-0.83). Finally, being classified as “high risk” according to EMA was not statistically associated with JAKIs’ withdrawal (HR 1.96, 95% CI 0.96-4-01).Conclusion:Our study shows that only a minority of patients discontinued JAKi (21.9%). Notably, among discontinuation‘s causes, no MACE’s were found. Being classified at “high-risk” according to EMA was not associated with JAKi’s discontinuation. Conversely, higher prednisone dosages, treatment with selective JAKi and absence of RF/ACPA were predictors of JAKi’s withdrawal.REFERENCES:[1] Ytterberg SR, et al. Cardiovascular and cancer risk with tofacitinib in rheumatoid arthritis. New Engl J Med 2022;386(4):316-326.Acknowledgements:NIL.Disclosure of Interests:Maddalena Larosa UCB, ABBVIE, AMGEN, Andrea Becciolini: None declared, Simone Parisi: None declared, Eleonora Di Donato: None declared, Dario Camellino Astrazeneca, Boerhringer INgelheim, GSK, Janssen, Giuditta Adorni: None declared, Gianluca Lucchini: None declared, Daniele Santilli: None declared, Enrico Fusaro: None declared, Maria Chiara Ditto: None declared, Alberto Lo Gullo: None declared, Marino Paroli: None declared, Rosalba Caccavale: None declared, Alessandro Volpe: None declared, Antonio Marchetta: None declared, Bernd Raffeiner: None declared, Eleonora Celletti: None declared, Myriam Di Penta: None declared, Emanuela Sabatini: None declared, Francesco Cipollone: None declared, Massimo Reta: None declared, Olga Addimanda: None declared, Mirco Magnani: None declared, Elisa Visalli: None declared, Rosario Foti: None declared, Giorgio Amato: None declared, Francesco De Lucia: None declared, Roberta Foti: None declared, Antonella Farina: None declared, Francesco Girelli: None declared, Simone Bernardi: None declared, Matteo Colina: None declared, Romina Andracco: None declared, Natalia Mansueto: None declared, Giulio Ferrero: None declared, Patrizia Del Medico: None declared, Aldo Molica Colella: None declared, Veronica Franchina: None declared, Francesco Molica Colella: None declared, Federica Lumetti: None declared, Gilda Sandri: None declared, Carlo Salvarani: None declared, Dilia Giuggioli: None declared, Marta Priora: None declared, Francesca Serale: None declared, Aurora Ianniello: None declared, Valeria Nucera: None declared, Francesca Ometto: None declared, Cecilia Giampietro: None declared, Elena Bravi: None declared, Ilaria Platè: None declared, Eugenio Arrigoni: None declared, Fabio Mascella: None declared, Maria Cristina Focherini: None declared, Alessandra Bezzi: None declared, Palma Scolieri: None declared, Vincenzo Bruzzese: None declared, Viviana Ravagnani: None declared, Guido Rovera: None declared, Alessia Fiorenza: None declared, Rosetta Vitetta: None declared, Alarico Ariani Amgen, Janssen.