BackgroundBelimumab is used as add-on therapy in patients with refractory systemic lupus erythematosus (SLE). While shared features guide patient eligibility, whether clinical and laboratory variables may identify distinct candidate subgroups remain unclear.AimTo identify clinical clusters among SLE patients initiating belimumab and assess their relationship with clinical outcomes.MethodsData were derived from the Italian multicentric Lupus Italian REgistry (LIRE), including patients starting belimumab added to standard treatment. Factor Analysis of Mixed Data (FAMD) with 150 baseline variables was followed by Hierarchical Clustering on Principal Components using Ward's criterion and Euclidean distance to define clusters. Disease activity was assessed by SLEDAI-2 K, damage by SLICC Damage Index (SDI), and clinical response as cSLEDAI-2K = 0 at 6 and 12 months (T6, T12).ResultsAmong 125 patients, clustering identified four main groups. Cluster 1 (n = 55) exhibited lower SDI; Cluster 2 (n = 28) included patients with renal involvement (p < 0.001); Cluster 3 (n = 14) displayed neuropsychiatric/cardiovascular activity, highest baseline SDI (p = 0.006), enriched neurological/cardiovascular damage (p < 0.001), and lowest hydroxychloroquine use (78.6% vs. 95.2%, p = 0.05); Cluster 4 (n = 28) showed predominant hematological abnormalities. Clinical responses were comparable across clusters, though Cluster 2 showed numerically higher cSLEDAI-2K = 0 rates at T12 (70.8% vs. 52.3%, p = 0.082). Final SDI was highest in Cluster 3 (p < 0.01), while damage accrued faster in Cluster 1 [0 (0–2) vs. 1 (1, 2), p < 0.001].ConclusionUnsupervised clustering revealed clinical heterogeneity among belimumab-treated SLE patients. Renal/serologically active profiles showed a numerically higher response rate, whereas pre-existing damage and low hydroxychloroquine use associated with higher damage burden despite biologic therapy.

OBJECTIVE:Rheumatic and musculoskeletal diseases (RMDs) represent a significant public health challenge in Italy. RMDs are often chronic and lead to increased morbidity and mortality, partly due to an increased risk of infections. Patients with RMDs and those on immunosuppressive therapy show a higher susceptibility to vaccine-preventable diseases and to serious complications in case of infection. Therefore, vaccination is a crucial tool to reduce these risks. This guideline was developed by the Italian Society of Rheumatology and aimed to provide updated national recommendations for clinical practice on vaccinations in adult patients with RMDs. METHODS:The GRADE ADOLOPMENT approach to combine adoption, adaptation, or de novo development of recommendations was used, and the 2022 American College of Rheumatology (ACR) guideline on vaccinations in RMDs was used as reference. The development process included an updated systematic review of the available evidence and an assessment of the ACR guidelines and their adaptability to the Italian context, followed by a discussion with experts in rheumatology and public health and representatives of healthcare professionals and patients. RESULTS:A set of recommendations was developed, and special attention was given to the current vaccination schedule and to the adjustment of anti-rheumatic drugs to optimize the response to vaccines. CONCLUSIONS:This guideline is a step forward in enhancing management and clinical practice for RMD patients in Italy and provides specific and evidence-based indications for infection prevention through vaccination. Their use is intended to promote health and alleviate the burden of morbidity and mortality in this vulnerable population.

Background Gout is associated with cardiovascular diseases including tachyarrhythmia; however, the mechanism underlying this association is unclear. We aimed to investigate whether a new diagnosis of tachyarrhythmia is associated with a gout flare in the previous 120 days. Methods Nested case–control and self-controlled case-series analyses were conducted in England and Sweden using primary care data from the Clinical Practice Research Datalink (CPRD; Jan 1, 2007, to March 29, 2021) and the Western Sweden Regional Healthcare Registry (VEGA; Jan 1, 2007, to Dec 31, 2017). The source population included people aged 18 years or older who contributed data to CPRD or VEGA, with linkage to secondary care and mortality records, and were newly diagnosed with gout during the study period. People with a diagnosis of tachyarrhythmia before gout diagnosis were excluded. For the nested case–control studies, cases (defined as patients with newly diagnosed tachyarrhythmia) and controls (defined as those without a diagnosis of tachyarrhythmia) were matched by age, sex, and gout duration with up to four controls. Self-controlled case-series analyses were performed in this cohort in people newly diagnosed with tachyarrhythmia within a year before or after the first gout flare. The main outcome was a new diagnosis of tachyarrhythmia. Gout flare was the exposure. Associations were evaluated using multivariable conditional logistic regression and adjusted odds ratios (aORs) with 95% CIs in nested case–control studies and adjusted incidence rate ratios (aIRRs) with 95% CIs in self-controlled case-series. Findings Among 260 254 eligible patients with newly diagnosed gout, 20 157 patients with a new diagnosis of tachyarrhythmia (cases) were matched with 76 835 patients without tachyarrhythmia (controls) in the English cohort. Among 19 346 eligible patients with gout, 1995 cases were matched with 6086 controls in the Swedish cohort. For the matched English cohort, the mean age was 76·3 years (SD 11·1), 30 182 (31·1%) of 96 992 were female, and 66 810 (68·9%) of 96 992 were male. For the matched Swedish cohort, the mean age was 76·3 years (SD 11·0), 2547 (31·5%) of 8081 were female, and 5534 (68·5%) of 8081 were male. The mean follow-up between gout diagnosis and the index date was 4·1 years (SD 3·1) in the English cohort and 2·5 years (2·1) in the Swedish cohort. Compared with controls, cases had significantly higher odds of gout flare within the previous 0–30 days (aOR 1·41 [95% CI 1·07–1·85] for England; 1·71 [1·10–2·65] for Sweden), but there was no significant difference in the odds of gout flare within the previous 31–120 days, compared with no or remote (>120 days previously) flares. In self-controlled case series analyses (2893 participants in the English cohort and 150 participants in the Swedish cohort), the aIRR of tachyarrhythmia compared with the baseline period was 1·44 (95% CI 1·26–1·64) 0–30 days after a gout flare and 1·34 (1·17–1·54) 31–60 days after a gout flare in the English cohort and 4·15 (2·47–6·97) 0–30 days after a gout flare and 2·25 (1·13–4·48) 31–60 days after a gout flare in the Swedish cohort; no differences were seen for the other intervals versus baseline. Interpretation Gout flares are associated with a transient increase in the risk of tachyarrhythmia. Patients presenting with a gout flare warrant increased cardiovascular vigilance over the next 30–60 days. Funding Foundation for Research in Rheumatology, University of Nottingham, and Reumatikerförbundet.

Background: Previous global estimates of Inflammatory Rheumatic Diseases (IRD) mortality only extend to 2014 and focus on few conditions, omitting prevalent diseases such as gout and spondyloarthritis.We assessed global mortality trends of gout, rheumatoid arthritis (RA), spondyloarthritis (SpA), systemic lupus erythematosus (SLE), and systemic sclerosis (SSc) in 2000-2019. Methods: We used the open access WHO mortality database to evaluate global temporal trends of five IRDs in countries with data usability ≥60%—a measure of data quality.Age-and-sex-standardised mortality rate (as-SMR) and their 95% confidence intervals (95%CI) were computed. Compounded annual percentage changes (APCs) of as-SMRs rates were estimated using generalized additive models stratified by age, sex, geographical area, and income category. Results: 63 countries were included. Globally, gout mortality significantly increased, while RA and SpA mortality significantly declined and SLE and SSc showed modest significant increases.Large heterogeneity was observed across income strata. Gout mortality rose sharply in lower-middle income (LMI) countries (APC +5·45% [+3·61%, +7·34%]) but remained stable in high-income (HI) settings (–0·39% [–0·80%, +0·01%]). RA mortality declined in HI countries (–2·24% [–2·63%, –1·85%]) but increased in upper-middle income (UMI) countries (+1·57% [+0·48%, +2·67%]). SpA mortality decreased in HI countries but showed no significant change in LMI settings.SLE mortality declined in HI countries (–1·16% [–1·40%, –0·92%]) but increased in UMI (+1·53% [+1·16%, +1·89%]) and LMI countries (+2·86% [+2·24%, +3·48%]). Similarly, SSc mortality decreased in HI countries (–0·40% [–0·57%, –0·22%]), increased in UMI countries (+2·20% [+1·57%, +2·84%]), and remained unchanged in LMI settings. Interpretation: Overall, improvements in IRD mortality were largely confined to high-income countries, whereas stable or worsening trends predominated in resource-limited settings, indicating widening global health inequalities and the unmet need to develop strategies to improve mortality in these conditions in resource limited settings.

OBJECTIVES:The concept of Comprehensive Disease Control (CDC) underlines as the control of disease activity should be associated with damage inhibition. Recently, this concept has been proposed in Systemic Lupus Erythematosus (SLE) patients (LupusCDC) with long-standing disease. In the present analysis we evaluated the incidence of LupusCDC in a cohort of newly diagnosed patients. METHODS:We analysed data from the multicentre cohort of the Early Lupus Project. Disease activity was evaluated by ECLAM and chronic damage by SDI. At each available time point, the presence of remission condition was assessed, defined as: Complete remission in GCs (GCon): ECLAM=0, antimalarials and/or immunosuppressants, PDN ≤5 mg/day; and Complete remission without GCs (GCoff): ECLAM=0, antimalarials and/or immunosuppressants. The presence of LupusCDC was analysed, defined as remission in the absence of progression of chronic damage (LupusCDC-GCon and LupusCDC-GCoff). RESULTS:We included 239 patients [205F; mean±DS age 45.8±14.6 years] with a follow-up of 36 months. During this period, 33.08% of patients achieved LupusCDC-GCon, while 12.03% LupusCDC-GCoff in at least one evaluation. Univariate analysis showed the association between failure to achieve LupusCDC-GCon and musculoskeletal manifestations (p<0.001), activity in renal and neuropsychiatric domains (p=0.01, p<0.001, respectively), association confirmed by the multivariate analysis. CONCLUSIONS:CDC in early onset SLE is not uncommon. Indeed, one-third of patients achieved LupusCDC-GCon in at least one evaluation. More severe disease, characterised by active renal and neuropsychiatric manifestations represented a risk factor for failure to achieve LupusCDC. The lower incidence of LupusCDC-GCoff suggested the difficulty in discontinuing GC treatment in early disease phase.

Objective Conventional radiography (CR) and ultrasound (US) are used interchangeably for identification of calcium pyrophosphate deposition (CPPD). The aim of this study was to assess whether combining US and CR offers greater accuracy over either modality alone for the identification of CPPD. Methods Consecutive patients scheduled for knee replacement surgery for osteoarthritis were enrolled. Before surgery, patients underwent CR and US of the knee. Menisci and hyaline cartilage were collected and analyzed using polarized light microscopy to confirm the presence of CPPD (gold standard). CR and US were assessed for absence/presence of CPPD by expert radiologists and sonographers. Diagnostic performance statistics were calculated. Poisson models with robust variance estimators were used to determine the likelihood of identifying CPPD. Results Fifty-one patients (63% female, mean age 71.4 [SD 8] years) were enrolled. US demonstrated higher overall accuracy than CR for CPPD identification (0.78 vs 0.73). Sequential use of both modalities provided an advantage when only 1 knee site was positive in 1 of the 2 techniques; however, when 2 or 3 sites were positive, no additional advantage was observed. When US was negative, subsequent CR did not improve CPPD detection, but in cases of a negative CR, a positive US increased the likelihood of CPPD by 4.21 times, whereas a negative US substantially reduced the probability of CPPD, increasing the likelihood of its absence by 76%. Conclusion US was more accurate than CR for identification of CPPD. Performing both exams can be an added value for CPPD identification only in a few specific cases.

BACKGROUND:Allopurinol, the most prescribed urate-lowering drug, is a known cause of severe cutaneous adverse reactions. We aimed to develop and validate a model to assess the risk of allopurinol-induced severe cutaneous adverse reactions in adults newly prescribed allopurinol. METHODS:In this retrospective new-user cohort study, we developed and validated a prognostic model using primary care, hospitalisation, and mortality data extracted from the UK Clinical Practice Research Datalink (CPRD) primary care database, for the period Jan 1, 2001, to March 29, 2021. Data from CPRD Aurum was used for model development and data from and CPRD GOLD was used for model validation. Adults (aged ≥18 years) residing in England who were newly prescribed allopurinol were followed up for 100 days to assess whether a severe cutaneous adverse reaction was recorded in hospitalisation or mortality records. Risk predictors included in the model were age, sex, ethnicity, chronic kidney disease stage, initial allopurinol dose, ischaemic heart disease, and heart failure. The primary outcome was to predict the 100-day risk of allopurinol-induced severe cutaneous adverse reactions in people newly prescribed allopurinol. We developed the model using multivariable Cox regression and pseudo-values, followed by penalisation and external validation. We assessed calibration, discrimination, and clinical utility in the risk range of 0·0001 to 0·003. People with lived experience of allopurinol use or gout were not involved in developing this research question, but will be involved in the dissemination of results. FINDINGS:225 761 patients newly prescribed allopurinol were registered in the CPRD Aurum database (development cohort) and 173 812 were included in the study. 44 630 (25·7%) of 173 812 patients were female, 129 182 (74·3%) were male, 154 323 (88·8%) were White, and the mean age was 63·9 years (SD 15·0). Of the patients newly prescribed allopurinol with data in the CPRD GOLD database (validation cohort), 55 395 patients were screened and 41 610 were included in the study. 10 829 (26·0%) of 41 610 patients were female, 30 781 (74·0%) were male, 37 242 (89·5%) were White and the mean age was 64·4 years (SD 14·9). 63 (0·04%) severe cutaneous adverse events occurred in 173 812 patients in the development cohort and 16 (0·04%) occurred in 41 610 patients in the validation cohort. Age (adjusted hazard ratio 1·03 [95% CI 1·01-1·06]), chronic kidney disease stages 3, 4, and 5 (2·24 [1·20-4·17] for stage 3; 6·65 [2·90-15·23] for stage 4; 18·85 [6·32-56·19] for stage 5), initial allopurinol dose of 300 mg or higher (5·99 [3·56-0·08]), South Asian ethnicity (5·35 [2·37-12·07]), and other Asian ethnicity (5·63 [1·34-23·61]) were associated with the 100-day risk of allopurinol-induced severe cutaneous adverse reactions. In the development dataset, after optimism-adjustment, the model's explained variation (Royston and Sauerbrei's R2D) was 0·50 and Harrell's C was 0·82. In the validation dataset, the calibration slope was 0·93 (95% CI 0·18-1·68), the R2D was 0·44 (95% CI 0·20-0·62), and Harrell's C was 0·79 (95% CI 0·71-0·88). The model had clinical utility across the prespecified risk range. INTERPRETATION:We developed and validated a prognostic model for the 100-day risk of an allopurinol-induced severe cutaneous adverse reaction with good predictive performance and clinical utility. This model could be used to inform the choice of urate-lowering drugs. FUNDING:University of Nottingham.

Stage 3 chronic kidney disease (CKD) often remains undiagnosed until more severe symptoms appear. This study assessed awareness and management of CKD among Italian general practitioners (GPs), focusing on early detection and current practices. A nation-wide, retrospective observational study was conducted using data from The Health Improvement Network (THIN®) database. Each participant was required to have had at least one interaction with a GP for either medical or administrative purposes (considering the index date), and to have a minimum of three years of retrospective data available from January 2021 to June 2022. The study evaluated the proportion of individuals aged ≥ 40 years who underwent a second serum creatinine test after ≥ 90 days, referrals to nephrologists, and CKD diagnosis confirmation and categorization. Multivariable Poisson regression models analyzed data to identify associations between patient characteristics and outcomes, in both the overall cohort and in the sub-group with available urine albumin-to-creatinine ratio (uACR) measurement. Among 347,548 adults aged ≥ 40 years, 18,002 (5.2

PV227 / #356 Poster Topic:AS23 - SLE-Diagnosis, Manifestations, & Outcomes The definition of “early systemic lupus erythematosus (SLE)” is evolving as we recognize the importance of identifying symptoms and initiating treatment earlier to prevent organ damage and improve both short- and long-term outcomes.[1] Several definitions of early SLE have been proposed concerning the time elapsed since symptom onset, ranging from <6 months to <36 months, with no general agreement. The OVERSLEEP study was designed to investigate whether there is a critical window of opportunity to establish an early diagnosis to improve the chances of achieving remission in SLE and prevent further damage once treatment begins. OVERSLEEP is a multicenter (23 centers), prospective, observational study ideated by the Italian Society of Rheumatology’s study group on early SLE. Eligible individuals are newly diagnosed SLE patients, fulfilling at least 1 of the validated sets of classification criteria. Diagnostic delay is defined as the time when symptoms are first presented to a healthcare provider (eg, general practitioner, lab, specialist, emergency room) until a diagnosis is made. Visits at 6-month intervals, or earlier if needed, assess clinical and laboratory features. The primary endpoint is the achievement of remission after 6 months. Secondary endpoints are LLDAS, organ damage according to the SLICC/ACR damage index, flares, patient-reported outcomes, hospitalizations, and death. Primary statistical analysis will be performed by logistic regression with the primary endpoint as the dependent variable, including diagnostic delay as the exposure variable and several adjustment variables. This abstract reports on the selection process for the adjustment variables associated with delayed diagnosis calculated as the “diagnostic delay ratio” between groups with or without the reference variable (ie, Mean delay-time interest group/Mean delay-time reference group). Enrollment started in September 2020 and aims at a sample size of 420 patients. In October 2024, the study included 221 patients (84.1% female); the median age is 38.0 (IQR 25.0 – 48.0) years, and 90% are Caucasians. The primary endpoint of remission at 6 months since diagnosis is achieved by 45 patients (25.6%). Univariate analysis identified factors associated with delayed diagnosis (Table 1), which, if confirmed in the whole study sample, will be used as adjustment variables, including baseline treatment with the daily and cumulative dose of glucocorticoids. The directed acyclic graph in Figure 1 shows the potential causal relationship between variables, identifying variables that will be included in the final model for primary statistical analysis of the OVERSLEEP study. The ancestor of exposure and outcome are confounders and will be included in the final model as adjustment factors. Table 1. Baseline variables and diagnostic delay ratio between the reference group and the interest group. F variable sex, the reference class is “female,” and the interest class is male; the diagnostic delay ratio is 1.46, means that males have a 46% mean time delay in diagnosis compared to females. Figure 1. The OVERSLEEP study targets a population in which remission is observable within 6 months of diagnosis. The enrolled population showed factors associated with delayed diagnosis, which are of interest for modeling the analysis of the OVERSLEEP study and further investigation aiming at developing red flags for early SLE diagnosis. *Listed authors have enrolled at least 10 patients with completed primary endpoint.References:[1.] Piga M. Best Pract Res Clin Rheumatol 2023;37(4):101938.

Falls are a public health concern among older adults, particularly in nursing home (NH) residents, but in Italy data on fall incidence and risk factors remain limited. To estimate the incidence of falls, identify associated risk factors, and evaluate the predictive value of the Tinetti Performance-Oriented Mobility Assessment (T-POMA) in a large cohort of NH residents. We conducted a retrospective cohort study using electronic health records from 32 NHs managed by Kos Care company across Italy. Residents aged ≥ 65 years with at least one functional assessment were included. Fall incidence was calculated per 100 person-years. Cox proportional hazards regression models were used to identify predictors of time to first fall. Overall, 754 residents (19.2

INTRODUCTION Longer life expectancies and increasing prevalence of chronic diseases drive up demand for healthcare services and related costs. In Italy, 32% of people aged 65 and over, and 48% of those over 85, have major chronic conditions and multimorbidity [1]. In 2019, individuals aged 65 and over accounted for 46% of hospital admissions and 60% of pharmaceutical expenditures, highlighting the significant burden of aging on the healthcare system [2]. In terms of costs, population’s segments with high prevalence of chronic conditions account for a large portion of healthcare spending [3,4,5]. Accurate predictions of future costs for the whole population and for key segments is crucial for healthcare planning. AIMS To predict yearly direct healthcare costs based on data of past National Health Service (NHS) resources utilization for the whole population and for high impacting segments. As a motivating example, we applied our approach to the dialysis patients’ segment. METHODS Using administrative healthcare databases, we traced NHS resource utilization (i.e., access to inpatient and outpatient services, drug dispensations) and associated costs for each individual aged ≥18 assisted by the Health Protection Agency of Bergamo (Northern Italy) between 2011 and 2023. We analyzed total cost (TC) as the sum of all services and dispensations costs, total scheduled cost (TSC) as the sum of scheduled inpatient visits, all outpatient visits and dispensations costs, and scheduled services cost (SSC) as the sum of scheduled inpatient visits and all outpatient visits costs. In the present abstract we focused on TC prediction. We used a supervised machine learning approach, namely random forest (RF) algorithm with 500 trees, to address the prediction problem [6,7]. We trained the algorithm on the 70% of individuals’ data from 2011 to 2015 (n=815,553) with their TC in 2016 as outcome. The 373 input variables included demographic features (such as age and sex) and NHS utilization data over the 4-years period 2011-2014 and in 2015 alone, in order to assess if 2016 cost was more associated with subjects’ behavior over the preceding year or with their historic behavior. As test sets, we used the remaining 30% of the dataset (hereafter 2011-16 set) and the subsequent years’ datasets (2012-17, 2013-18, 2014-19, 2015-20, 2016-21, 2017-22, and 2018-23 sets). We considered variable importance, measured as the percent increase in mean squared error (MSE) when a given variable is permuted, as a measure of each predictor’s impact on the outcome. For each test set, actual and predicted TCs for the whole population were calculated as the sum of all individuals’ actual and predicted TCs, respectively. The ratio of the difference between predicted and actual population TCs to actual population TCs was used as measure of the prediction error (PE). PE=0% indicates a perfect prediction, PE >0% or <0% suggests overestimation or underestimation of the actual TC. Finally, we defined dialysis patients as those who had at least one access to outpatient dialysis services. For this segment, we calculated the mean and sum of predicted and actual TCs, and PE. Also, we derived a variability interval for the mean predicted TC based on the 2.5 and 97.5 quantiles of the distribution of the mean TCs predicted by each tree for subjects included in the segment. RESULTS The mean actual annual population TC in the period from 2011 to 2023 was €1,023,636,867 (range: 944,632,707 – 1,111,657,382). High-cost subjects (>€15,000 yearly), accounting for less than 1% of the annual population, absorbed more than 27% of annual TC. Top 3 most important variables in the RF were the number of outpatient accesses to dialysis over the preceding year, and the frequency of laboratory tests and outpatient services over the 4 preceding years. Figure 1 shows the PEs calculated across all test sets, overall and in the dialysis patients’ segment. Overall, PEs ranged from -3.1 to -1.9 across 2011-16 to 2014-19 sets (for 2014-19 set, actual annual population TC: €1,031,200,509; predicted annual population TC: €1,011,869,922), and widely increased from 2015-20 (range from -6.9 to 8.7; for 2015-20 set, actual annual population TC: €944,632,707; predicted annual population TC: €1,026,878,752) For the dialysis patients’ segment, the lowest PE (-0.7%) was observed in the 2011-16 set (actual mean TC: €38,536; predicted mean TC [variability interval]: €38,259 [35,542 – 41,112]), while the highest was -5.4% in the 2016-21 set (actual mean TC: €38,883; predicted mean TC [variability interval]: €36,785 [33,967 – 39,342]). CONCLUSIONS Using a machine learning approach, we predicted healthcare TCs based on individual data of past utilization of NHS for the whole population and a high impacting segment. Predictions based on the algorithm trained on data from 2011 to 2015 were consistent until 2019, understandable given the COVID-19 pandemic in 2020. Results highlight the pandemic’s impact on the model performance, leading to overestimation of the actual TC in 2020 and underestimations thereafter. Future steps include the identification of key segments and the update of the training algorithm on the subsequent years’ datasets. This is a useful tool to assist HPA in resource allocation, e.g. as an integration to the monitoring of chronic diseases in the population.

OBJECTIVES:As part of the ultrasound in psoriatic arthritis (PsA) treatment (Ultrasound in Psoriatic Arthritis Treatment-NCT03330769) study, 2 ultrasound scores for PsA (UPsA)-the UPsA activity score and the UPsA damage score-were developed and internally validated to assess musculoskeletal inflammation and structural damage in PsA. In addition, a simplified UPsA activity score (sUPsA) was also derived to enhance feasibility and applicability in routine clinical practice. METHODS:Baseline and 6-month data from patients with PsA across 19 Italian centres were analysed. Clinical evaluations included joint counts, enthesitis, dactylitis, and patient-reported outcomes. Ultrasound assessments, performed by trained sonographers, covered 42 joints, 36 tendons, 12 entheses, and 2 bursae. Factor analysis was used to derive composite scores (range 0-10). Construct validity was assessed by Spearman's correlations with clinical variables, and sensitivity to change was evaluated using the standardised response mean (SRM). RESULTS:A total of 312 patients with PsA were enrolled. The mean UPsA activity score was 3.7 (SD 1.86), correlating with Disease Activity for Psoriatic Arthritis (rs = 0.42), 68-tender joint count (rs = 0.31), and 66-swollen joint count (rs = 0.45; all P < .001). The UPsA damage score averaged 4.1 (SD 2.26), correlating with the modified Sharp-van der Heijde score (rs = 0.36, P < .001). The UPsA activity score showed moderate sensitivity to change overall (SRM = 0.63) and high responsiveness in patients achieving minimal disease activity (SRM = 1.03). The sUPsA retained 90% of the information from the full score while substantially improving feasibility. CONCLUSIONS:The UPsA scores underwent internal validation and demonstrated responsiveness, representing valuable tools to assess PsA activity and damage.

OBJECTIVES:To develop and evaluate the performance of multicriteria decision analysis (MCDA)-driven candidate classification criteria for antisynthetase syndrome (ASSD). METHODS:A list of variables associated with ASSD was developed using a systematic literature review and then refined into an ASSD key domains and variables list by myositis and interstitial lung disease (ILD) experts. This list was used to create preferences surveys in which experts were presented with pairwise comparisons of clinical vignettes and asked to select the case that was more likely to represent ASSD. Experts' answers were analysed using the Potentially All Pairwise RanKings of all possible Alternatives method to determine the weights of the key variables to formulate the MCDA-based classification criteria. Clinical vignettes scored by the experts as consensus cases or controls and real-world data collected in participating centres were used to test the performance of candidate classification criteria using receiver operating characteristic curves and diagnostic accuracy metrics. RESULTS:Positivity for antisynthetase antibodies had the highest weight for ASSD classification. The highest-ranked clinical manifestation was ILD, followed by myositis, mechanic's hands, joint involvement, inflammatory rashes, Raynaud phenomenon, fever, and pulmonary hypertension. The candidate classification criteria achieved high areas under the curve when applied to the consensus cases and controls and real-world patient data. Sensitivities, specificities, and positive and negative predictive values were >80%. CONCLUSIONS:The MCDA-driven candidate classification criteria were consistent with published ASSD literature and yielded high accuracy and validity.

OBJECTIVE:The role of antiseizure medications (ASMs) in patients with post-stroke epilepsy (PSE) is still debated. Although a few studies have compared the efficacy of different ASMs on mortality in patients with PSE, overall evidence on the impact of ASM use on survival is limited. This study aimed to evaluate the association between ASM use and all-cause mortality in patients with PSE. METHODS:A cohort study was conducted using health care administrative database of Health Protection Agency of Bergamo (Italy). Individuals with a diagnosis of stroke followed by epilepsy onset between January 1, 2014 and December 31, 2017 were included. The date of epilepsy was considered as Index date (ID). Patients were followed from the ID until death, disenrollment, or end of follow-up, whichever occurred first. Exposure to ASMs was defined as at least one dispensing within 30 days of the ID; patients without ASM dispensing during this period were considered non-exposed. All-cause mortality was analyzed using Cox proportional hazards models, with non-exposure as the reference. Two analytical approaches were adopted: an intention-to-treat analysis and a time-dependent analysis. RESULTS:A total of 145 patients met the inclusion criteria: 107 ASM users and 38 non-users. In the intention-to-treat analysis, ASM use was associated with a lower risk of all-cause mortality (adjusted hazard ratio [HR]: 0.56; 95% confidence interval [CI]: 0.33-0.95). Consistent findings were observed in the time-dependent analysis (adjusted HR: 0.39; 95% CI: 0.23-0.65). The sensitivity analyses confirmed the robustness of the results. SIGNIFICANCE:In this population-based cohort study, ASM use in patients with PSE was associated with a significantly reduced risk of all-cause mortality compared to non-use. These findings support the hypothesis that ASM treatment might be associated with positive effect in this high-risk population.

In systemic lupus erythematosus (SLE), treatment decisions are guided by clinical judgment based on disease manifestations and safety profiles rather than standardized protocols, particularly for extra-renal involvement. In this study of 356 SLE patients, 190 receiving non-biologic immunosuppressants and 166 receiving biologics, we investigated the association of clinical phenotypes with treatment initiation and the impact of damage (SLICC-DI), comorbidities, and hospitalization history on these choices. Dominant clinical phenotypes were defined qualitatively using BILAG and SLEDAI domains. Logistic regression models with Simes-Hochberg correction revealed that renal phenotypes were strongly associated with mycophenolate initiation (OR = 4.09, p < 0.001), musculoskeletal phenotypes with methotrexate (OR = 4.86, p < 0.001). Belimumab was preferentially initiated in patients with musculoskeletal involvement and high SLEDAI scores (OR = 1.84, p = 0.03; OR = 2.03, p = 0.03, respectively). Notably, the association between mycophenolate and the renal phenotype persisted in the presence of comorbidities but was not observed in patients with SLICC-DI > 0 or more than one hospitalization in the previous year. Similarly, methotrexate and belimumab associations were diminished in patients with a Charlson comorbidity index > 1 or damage (SLICC-DI > 0). This study offers novel insights into the clinical determinants of immunosuppressive therapy selection in SLE and underscore the potential for tailoring treatment strategies to individual patient profiles.

Objective. In the absence of national and European guidelines on the treatment of rheumatoid arthritis (RA) with interstitial lung disease (ILD), the Italian Society of Rheumatology decided to develop national clinical practice guidelines on the management of patients with RA-ILD in accordance with the requirements of the National Guideline System of the National Institute of Health. Methods. The development process included a systematic review of the available evidence and its adaptability to the Italian context, followed by a consultation with experts in rheumatology, respiratory diseases, radiology, and representatives of the health professions and patients. Results. The panel decided to develop recommendations in three main scenarios. The first section of recommendations is focused on drugs indicated for RA to assess their safety and efficacy in RA-ILD. The second set of recommendations covered the drugs indicated for the treatment of ILD in patients with RA-ILD (to assess their efficacy and safety in patients with RA). The third part of these guidelines dealt with drugs indicated for the treatment of RA-ILD upon first-line failure. Moreover, the lack or absence of scientific evidence in literature on certain topics, such as the value of a multidisciplinary treatment approach and lung transplantation, led to the decision to proceed through expert consensus to develop good clinical practice guidelines. Conclusions. These guidelines represent a fundamental step towards improving the health management of patients with rheumatological diseases in Italy by providing specific and evidence-based guidelines for the management of RA-ILD. Their use is intended to promote health and reduce the burden of morbidity and mortality in this vulnerable population.