Aims: To analyze the association between colchicine prescription and COVID-19-related hospital admissions in patients with rheumatic and musculoskeletal diseases (RMDs). Methods: Patients attending a rheumatology outpatient clinic from a tertiary care center in Madrid, Spain, from 1 September 2019 to 29 February 2020 were included. Patients were assigned as exposed or unexposed based on whether they were prescribed with colchicine in their last visit to the clinic during the 6 months before the start of the observation period. Treatment changes during the observation period were also considered. The primary outcome was COVID-19-related hospital admissions between 1 March and 20 May 2020. Secondary outcome included COVID-19-related mortality. Several weighting techniques for data balancing, based and non-based on the propensity score, followed by Cox regressions were performed to estimate the association of colchicine prescription on both outcomes. Discussion: The number of patients entered in the study was 9379, with 406 and 9002 exposed and unexposed follow-up periods, respectively. Generalized Boosted Models (GBMs) and Empirical Balancing Calibration Weighting (EBCW) methods showed the best balance for COVID-19-related hospital admissions. Colchicine prescription did not show a statistically significant association after covariable balancing ( p-value = 0.195 and 0.059 for GBM and EBCW, respectively). Regarding mortality, the low number of events prevented a success variable balancing and analysis. Conclusion: Colchicine prescription does not play a significant protective or risk role in RMD patients regarding COVID-19-related hospital admissions. Our observations could support the maintenance of colchicine prescription in those patients already being treated, as it is not associated with a worse prognosis. Plain language title: Colchicine influence in COVID-19-related hospital admissions

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Our objective is to develop and validate a predictive model based on the random forest algorithm to estimate the readmission risk to an outpatient rheumatology clinic after discharge. We included patients from the Hospital Clínico San Carlos rheumatology outpatient clinic, from 1 April 2007 to 30 November 2016, and followed-up until 30 November 2017. Only readmissions between 2 and 12 months after the discharge were analyzed. Discharge episodes were chronologically split into training, validation, and test datasets. Clinical and demographic variables (diagnoses, treatments, quality of life (QoL), and comorbidities) were used as predictors. Models were developed in the training dataset, using a grid search approach, and performance was compared using the area under the receiver operating characteristic curve (AUC-ROC). A total of 18,662 discharge episodes were analyzed, out of which 2528 (13.5%) were followed by outpatient readmissions. Overall, 38,059 models were developed. AUC-ROC, sensitivity, and specificity of the reduced final model were 0.653, 0.385, and 0.794, respectively. The most important variables were related to follow-up duration, being prescribed with disease-modifying anti-rheumatic drugs and corticosteroids, being diagnosed with chronic polyarthritis, occupation, and QoL. We have developed a predictive model for outpatient readmission in a rheumatology setting. Identification of patients with higher risk can optimize the allocation of healthcare resources.

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Background. The susceptibility of patients with rheumatic diseases, and the risks or benefits of immunosuppressive therapies for COVID-19 are unknown. Methods.We performed a retrospective study with patients under follow-up in rheumatology departments from seven hospitals in Spain. We matched updated databases of rheumatology patients with SARS-CoV-2 positive PCR tests performed in the hospital to the same reference populations. Incidences of PCR+ confirmed COVID-19 were compared among groups. Results.Patients with chronic inflammatory diseases had 1.32-fold higher prevalence of hospital PCR+ COVID-19 than the reference population (0.76% vs 0.58%). Systemic autoimmune or immune mediated diseases (AI/IMID) patients showed a significant increase, whereas inflammatory arthritis (IA) or systemic lupus erythematosus (SLE) patients did not. COVID-19 cases in some but not all diagnostic groups had older ages than cases in the reference population. IA patients on targeted-synthetic or biological disease-modifying antirheumatic drugs (ts/bDMARD), but not those on conventional-synthetic (csDMARD), had a greater prevalence despite a similar age distribution. Conclusion.Patients with AI/IMID show a variable risk of hospital diagnosed COVID-19. Interplay of aging, therapies, and disease specific factors seem to contribute. These data provide a basis to improve preventive recommendations to rheumatic patients and to analyze the specific factors involved in COVID-19 susceptibility.All rights reserved. No reuse allowed without permission.(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The emergence of Large Language Models (LLM) with remarkable performance such as ChatGPT and GPT-4, has led to an unprecedented uptake in the population. One of their most promising and studied applications concerns education due to their ability to understand and generate human-like text, creating a multitude of opportunities for enhancing educational practices and outcomes. The objective of this study is two-fold: to assess the accuracy of ChatGPT/GPT-4 in answering rheumatology questions from the access exam to specialized medical training in Spain (MIR), and to evaluate the medical reasoning followed by these LLM to answer those questions. A dataset, RheumaMIR, of 145 rheumatology-related questions, extracted from the exams held between 2010 and 2023, was created for that purpose, used as a prompt for the LLM, and was publicly distributed. Six rheumatologists with clinical and teaching experience evaluated the clinical reasoning of the chatbots using a 5-point Likert scale and their degree of agreement was analyzed. The association between variables that could influence the models' accuracy (i.e., year of the exam question, disease addressed, type of question and genre) was studied. ChatGPT demonstrated a high level of performance in both accuracy, 66.43%, and clinical reasoning, median (Q1-Q3), 4.5 (2.33-4.67). However, GPT-4 showed better performance with an accuracy score of 93.71% and a median clinical reasoning value of 4.67 (4.5-4.83). These findings suggest that LLM may serve as valuable tools in rheumatology education, aiding in exam preparation and supplementing traditional teaching methods.