Prevalence and management of tuberculosis infection in Apulian rheumatologic patients treated with biologics: An observational cohort 10-year study from the BIOPURE registry.

INTRODUCTION: The objective of this study was to assess the 10-year prevalence of latent tuberculosis infection (LTBI) among Apulian patients with rheumatic diseases (RDs). Secondary endpoint was to record new cases of active TB disease and LTBI among patients treated with biologic disease-modifying antirheumatic drugs (bDMARDs). METHODS: We analysed the results from the patients included in the BIOPURE registry from 2009 to 2018, who underwent QuantiFERON-TB Gold In-tube (QFT-GIT) test as screening before bDMARDs treatment. Demographic and clinical data were recorded at the time of the first QFT-GIT test. Administration of preventive therapy and bDMARD treatments were recorded for patients with positive QFT-GIT test. All new tuberculosis infections were recorded during the follow-up. RESULTS: The final study population included 3028 patients (855 rheumatoid arthritis, 1001 psoriatic arthritis, 833 spondyloarthritis, 130 connective tissue diseases, 33 systemic vasculitis and 176 other inflammatory rheumatic conditions), more frequently female (67.2%), with a mean age of 52 ± 18 years. Patients with QFT-GIT-positive test were elderly people, predominantly male with higher prevalence of diabetes as comorbidity. The 10-year prevalence of LTBI was 10.8%. Of note, no cases of TB reactivation were recorded in patients who completed preventive therapy treatment. Three thousand and sixteen patients were followed over time (42.6 ± 30 months), and five (.2%) developed active TB, which corresponds to .47 cases per 1000 person-years. CONCLUSIONS: In the 10-year observation, the use of bDMARDs seems to be safe in rheumatologic patients with positive QFT-GIT test treated according to current recommendations. Nevertheless, cases of primary TB disease did occur during treatment with biologicals.

Development and implementation of the AIDA International Registry for patients with Periodic Fever, Aphthous stomatitis, Pharyngitis, and cervical Adenitis syndrome.

Objective: Aim of this paper is to illustrate the methodology, design, and development of the AutoInflammatory Disease Alliance (AIDA) International Registry dedicated to patients with the Periodic Fever, Aphthous stomatitis, Pharyngitis, and cervical Adenitis (PFAPA) syndrome. Methods: This is a physician-driven, non-population- and electronic-based registry proposed to gather real-world demographics, clinical, laboratory, instrumental and socioeconomic data from PFAPA patients. Data recruitment is realized through the on-line Research Electronic Data Capture (REDCap) tool. This registry is thought to collect standardized information for clinical research leading to solid real-life evidence. The international scope and the flexibility of the registry will facilitate the realization of cutting-edge study projects through the constant updating of variables and the possible merging and transfer of data between current and future PFAPA registries. Results: A total of 112 centers have already been involved from 23 countries and 4 continents starting from August 24th, 2021, to April 6th, 2022. In total 56/112 have already obtained the formal approval from their local Ethics Committees. The platform counts 321 users (113 principal investigators, 203 site investigators, two lead investigators, and three data managers). The registry collects retrospective and prospective data using 3,856 fields organized into 25 instruments, including PFAPA patient's demographics, medical histories, symptoms, triggers/risk factors, therapies, and impact on the healthcare systems. Conclusions: The development of the AIDA International Registry for PFAPA patients will enable the on-line collection of standardized data prompting real-life studies through the connection of worldwide groups of physicians and researchers. This project can be found on https://clinicaltrials.gov NCT05200715.

A Machine Learning Approach to Predict Remission in Patients With Psoriatic Arthritis on Treatment With Secukinumab.

Background: Psoriatic Arthritis (PsA) is a multifactorial disease, and predicting remission is challenging. Machine learning (ML) is a promising tool for building multi-parametric models to predict clinical outcomes. We aimed at developing a ML algorithm to predict the probability of remission in PsA patients on treatment with Secukinumab (SEC). Methods: PsA patients undergoing SEC treatment between September 2017 and September 2020 were retrospectively analyzed. At baseline and 12-month follow-up, we retrieved demographic and clinical characteristics, including Body Mass Index (BMI), disease phenotypes, Disease Activity in PsA (DAPSA), Leeds Enthesitis Index (LEI) and presence/absence of comorbidities, including fibromyalgia and metabolic syndrome. Two random feature elimination wrappers, based on an eXtreme Gradient Boosting (XGBoost) and Logistic Regression (LR), were trained and validated with 10-fold cross-validation for predicting 12-month DAPSA remission with an attribute core set with the least number of predictors. The performance of each algorithm was assessed in terms of accuracy, precision, recall and area under receiver operating characteristic curve (AUROC). Results: One-hundred-nineteen patients were selected. At 12 months, 20 out of 119 patients (25.21%) achieved DAPSA remission. Accuracy and AUROC of XGBoost was of 0.97 ± 0.06 and 0.97 ± 0.07, overtaking LR (accuracy 0.73 ± 0.09, AUROC 0.78 ± 0.14). Baseline DAPSA, fibromyalgia and axial disease were the most important attributes for the algorithm and were negatively associated with 12-month DAPSA remission. Conclusions: A ML approach may identify SEC good responders. Patients with a high disease burden and axial disease with comorbid fibromyalgia seem challenging to treat.

Development and Implementation of the AIDA International Registry for Patients With Undifferentiated Systemic AutoInflammatory Diseases.

Objective: This paper points out the design, development and deployment of the AutoInflammatory Disease Alliance (AIDA) International Registry dedicated to pediatric and adult patients affected by Undifferentiated Systemic AutoInflammatory Diseases (USAIDs). Methods: This is an electronic registry employed for real-world data collection about demographics, clinical, laboratory, instrumental and socioeconomic data of USAIDs patients. Data recruitment, based on the Research Electronic Data Capture (REDCap) tool, is designed to obtain standardized information for real-life research. The instrument is endowed with flexibility, and it could change over time according to the scientific acquisitions and potentially communicate with other similar tools; this platform ensures security, data quality and data governance. Results: The focus of the AIDA project is connecting physicians and researchers from all over the world to shed a new light on heterogeneous rare diseases. Since its birth, 110 centers from 23 countries and 4 continents have joined the AIDA project. Fifty-four centers have already obtained the approval from their local Ethics Committees. Currently, the platform counts 290 users (111 Principal Investigators, 179 Site Investigators, 2 Lead Investigators, and 2 data managers). The Registry is collecting baseline and follow-up data using 3,769 fields organized into 23 instruments, which include demographics, history, symptoms, trigger/risk factors, therapies, and healthcare information access for USAIDs patients. Conclusions: The development of the AIDA International Registry for USAIDs patients will facilitate the online collection of real standardized data, connecting a worldwide group of researchers: the Registry constitutes an international multicentre observational groundwork aimed at increasing the patient cohort of USAIDs in order to improve our knowledge of this peculiar cluster of autoinflammatory diseases. NCT05200715 available at https://clinicaltrials.gov/.

Development and Implementation of the AIDA International Registry for Patients with Non-Infectious Scleritis.

INTRODUCTION: This article points out the design, methods, development and deployment of the international registry promoted by the AutoInflammatory Disease Alliance (AIDA) Network with the aim to define and assess paediatric and adult patients with immune-mediated scleritis. METHODS: This registry collects both retrospective and prospective real-world data from patients with non-infectious scleritis through the Research Electronic Data Capture (REDCap) tool and aims to promote knowledge and real-life evidence from patients enrolled worldwide; the registry also allows the collection of standardised data, ensuring the highest levels of security and anonymity of patients' data and flexibility to change according to scientific acquisitions over time. The communication with other similar registries has been also ensured in order to pursue the sustainability of the project with respect to the adaptation of collected data to the most diverse research projects. RESULTS: Since the launch of the registry, 99 centres have been involved from 20 countries and four continents. Forty-eight of the centres have already obtained a formal approval from their local ethics committees. At present, the platform counts 259 users (95 principal investigators, 160 site investigators, 2 lead investigators, and 2 data managers); the platform collects baseline and follow-up data using 3683 fields organised into 13 instruments, including patient's demographics, history, symptoms, trigger or risk factors, therapies and healthcare utilization. CONCLUSIONS: The development of the AIDA International Registry for patients with non-infectious scleritis will allow solid research on this rare condition. Real-world evidence resulting from standardised real-life data will lead to the optimisation of routine clinical and therapeutic management, which are currently limited by the rarity of this ocular inflammatory condition.

Estimated 10-year cardiovascular risk in a large Italian cohort of rheumatoid arthritis patients: Data from the Cardiovascular Obesity and Rheumatic DISease (CORDIS) Study Group.

BACKGROUND: Several cardiovascular (CV) risk algorithms are available to predict CV events in the general population. However, their performance in patients with rheumatoid arthritis (RA) might differ from the general population. This cross-sectional multicentre study aimed to estimate the 10-year CV risk using two different algorithms in a large RA cohort and in patients with osteoarthritis (OA). METHODS: In a consecutive series of RA patients and matched OA controls without prior CV events, clinical and serologic data and traditional CV risk factors were recorded. The 10-year CV risk was assessed with the Systematic COronary Risk Evaluation (SCORE) and the "Progetto Cuore" algorithms. RESULTS: 1,467 RA patients and 342 OA subjects were included. RA patients were more frequently diabetic (9.9% vs 6.4%; p=0.04) and smokers (20.4% vs 12.5%; p=0.002) but had lower prevalence of obesity (15% vs 21%; p=0.003). Dyslipidaemia was more prevalent in OA (32.5% vs 21.7%; p<0.0001). The 10-year estimated CV risk was 1.6% (95%CI 1.3-1.9) in RA and 1.4% (95%CI 1.3-1.6) in OA (p=0.002) according to SCORE and 6.5% (95%CI 6.1-6.9) in RA and 4.4% (95%CI 3.9-5.1) in OA (p<0.001) according to "Progetto Cuore". Regardless of the score used, RA patients had a 3- to-4-fold increased 10-year risk of CV events compared to OA subjects. CONCLUSION: RA patients have a significantly higher 10-year risk of CV events than OA subjects. In addition to effective disease control and joint damage prevention, specific protective measures targeting modifiable traditional CV risk factors should be implemented in RA.