Prompt engineering: The next big skill in rheumatology research.

Large language models (LLMs) like GPT-4 and Claude are catalyzing transformation across medical research including rheumatology. This review examines their applications, highlighting the pivotal role of prompt engineering in effectively guiding LLMs. Key aspects explored include literature synthesis, data analysis, manuscript drafting, coding assistance, privacy considerations, and generative artificial intelligence integrations. While LLMs accelerate workflows, reliance without apt prompting jeopardizes accuracy. By methodically constructing prompts and gauging model outputs, researchers can maximize relevance and utility. Locally run open-source models also offer data privacy protections. As LLMs permeate rheumatology research, developing expertise in strategic prompting and assessing model limitations is critical. With proper oversight, LLMs markedly boost scholarly productivity.

Safety profile of recombinant adjuvanted anti-herpes zoster vaccine (RZV) in high-risk groups: Data from active surveillance program. Puglia (Italy), 2021-23.

BACKGROUND: Since 2021 a recombinant adjuvanted anti-Herpes Zoster vaccine(Recombinant Zoster Vaccine, RZV) is offered in Italy to high-risk patients. Few real-life data about RZV safety are available in target populations. OBJECTIVES: This study investigates Adverse Events Following Immunization(AEFIs), baseline disease flare-ups, and Herpes Zoster (HZ) episodes occurring after RZV administration in a heterogeneous population of fragile patients to design its safety profile. METHODS: This is a retrospective population-based study. RZV-vaccinated patients at Bari Policlinico General Hospital vaccination clinic from October 1st, 2021, to March 31st, 2023, were enrolled. Subjects were screened for reason of RZV eligibility and baseline chronic pathologies. AEFIs occurred in the first 7-days post-vaccination period were collected, and baseline disease flare-ups and post-vaccination HZ episodes were assessed via a 3-month follow-up. RESULTS: Five-hundred-thirty-eight patients were included and total of 1,031 doses were administered. Most patients were vaccinated due to ongoing immunosuppressive therapy(54.65 %); onco-hematological and cardiovascular conditions were the most common chronic baseline pathologies. Out of 1,031 follow-ups, 441 AEFI cases were reported(42.7/100). The most common symptoms were injection site pain/itching(35.60/100), asthenia/malaise(11.44/100), and fever (10.09/100). Four serious AEFIs occurred(0.38/100). Older age, male sex, and history of cardiovascular diseases(OR:0.71; 95CI:0.52-0.98; p-value <0.05) were found to decrease AEFIs risk, while endocrine-metabolic illnesses(OR:1.61; 95CI:1.15-2.26; p-value <0.05) increased it. Twelve patients(2.23 %) reported a flare-up/worsening of their baseline chronic condition within the first three months after vaccination(mean interval 31.75 days, range 0-68 days). Patients with rheumatological illnesses had a higher risk of relapse(OR:16.56; 95CI:3.58-76.56; p-value <0.001), while male sex behaved as a protective factor. Twelve patients who completed the vaccination cycle(2.43%) had at least one HZ episode by the long-term follow-up. CONCLUSIONS: The study demonstrates RZV safety in a significant number of high-risk patients. Hence, RZV should be actively offered as part of tailored vaccination programs to decrease the burden of HZ in fragile populations.

Explainable artificial intelligence for microbiome data analysis in colorectal cancer biomarker identification.

Background: Colorectal cancer (CRC) is a type of tumor caused by the uncontrolled growth of cells in the mucosa lining the last part of the intestine. Emerging evidence underscores an association between CRC and gut microbiome dysbiosis. The high mortality rate of this cancer has made it necessary to develop new early diagnostic methods. Machine learning (ML) techniques can represent a solution to evaluate the interaction between intestinal microbiota and host physiology. Through explained artificial intelligence (XAI) it is possible to evaluate the individual contributions of microbial taxonomic markers for each subject. Our work also implements the Shapley Method Additive Explanations (SHAP) algorithm to identify for each subject which parameters are important in the context of CRC. Results: The proposed study aimed to implement an explainable artificial intelligence framework using both gut microbiota data and demographic information from subjects to classify a cohort of control subjects from those with CRC. Our analysis revealed an association between gut microbiota and this disease. We compared three machine learning algorithms, and the Random Forest (RF) algorithm emerged as the best classifier, with a precision of 0.729 ± 0.038 and an area under the Precision-Recall curve of 0.668 ± 0.016. Additionally, SHAP analysis highlighted the most crucial variables in the model's decision-making, facilitating the identification of specific bacteria linked to CRC. Our results confirmed the role of certain bacteria, such as Fusobacterium, Peptostreptococcus, and Parvimonas, whose abundance appears notably associated with the disease, as well as bacteria whose presence is linked to a non-diseased state. Discussion: These findings emphasizes the potential of leveraging gut microbiota data within an explainable AI framework for CRC classification. The significant association observed aligns with existing knowledge. The precision exhibited by the RF algorithm reinforces its suitability for such classification tasks. The SHAP analysis not only enhanced interpretability but identified specific bacteria crucial in CRC determination. This approach opens avenues for targeted interventions based on microbial signatures. Further exploration is warranted to deepen our understanding of the intricate interplay between microbiota and health, providing insights for refined diagnostic and therapeutic strategies.

Unraveling the microbiome-metabolome nexus: a comprehensive study protocol for personalized management of Behçet's disease using explainable artificial intelligence.

The presented study protocol outlines a comprehensive investigation into the interplay among the human microbiota, volatilome, and disease biomarkers, with a specific focus on Behçet's disease (BD) using methods based on explainable artificial intelligence. The protocol is structured in three phases. During the initial three-month clinical study, participants will be divided into control and experimental groups. The experimental groups will receive a soluble fiber-based dietary supplement alongside standard therapy. Data collection will encompass oral and fecal microbiota, breath samples, clinical characteristics, laboratory parameters, and dietary habits. The subsequent biological data analysis will involve gas chromatography, mass spectrometry, and metagenetic analysis to examine the volatilome and microbiota composition of salivary and fecal samples. Additionally, chemical characterization of breath samples will be performed. The third phase introduces Explainable Artificial Intelligence (XAI) for the analysis of the collected data. This novel approach aims to evaluate eubiosis and dysbiosis conditions, identify markers associated with BD, dietary habits, and the supplement. Primary objectives include establishing correlations between microbiota, volatilome, phenotypic BD characteristics, and identifying patient groups with shared features. The study aims to identify taxonomic units and metabolic markers predicting clinical outcomes, assess the supplement's impact, and investigate the relationship between dietary habits and patient outcomes. This protocol contributes to understanding the microbiome's role in health and disease and pioneers an XAI-driven approach for personalized BD management. With 70 recruited BD patients, XAI algorithms will analyze multi-modal clinical data, potentially revolutionizing BD management and paving the way for improved patient outcomes.

Beyond the horizon: Innovations and future directions in axial-spondyloarthritis.

Axial spondyloarthritis (axSpA) is a chronic inflammatory disease of the spine and sacroiliac joints. This review discusses recent advances across multiple scientific fields that promise to transform axSpA management. Traditionally, axSpA was considered an immune-mediated disease driven by human leukocyte antigen B27 (HLA-B27), interleukin (IL)-23/IL-17 signaling, biomechanics, and dysbiosis. Diagnosis relies on clinical features, laboratory tests, and imaging, particularly magnetic resonance imaging (MRI) nowadays. Management includes exercise, lifestyle changes, non-steroidal anti-inflammatory drugs and if this is not sufficient to achieve disease control also biological and targeted-synthetic disease modifying anti-rheumatic drugs. Beyond long-recognized genetic risks like HLA-B27, high-throughput sequencing has revealed intricate gene-environment interactions influencing dysbiosis, immune dysfunction, and aberrant bone remodeling. Elucidating these mechanisms promises screening approaches to enable early intervention. Advanced imaging is revolutionizing the assessment of axSpA's hallmark: sacroiliac bone-marrow edema indicating inflammation. Novel magnetic resonance imaging (MRI) techniques sensitively quantify disease activity, while machine learning automates complex analysis to improve diagnostic accuracy and monitoring. Hybrid imaging like synthetic MRI/computed tomography (CT) visualizes structural damage with new clarity. Meanwhile, microbiome analysis has uncovered gut ecosystem alterations that may initiate joint inflammation through HLA-B27 misfolding or immune subversion. Correcting dysbiosis represents an enticing treatment target. Moving forward, emerging techniques must augment patient care. Incorporating patient perspectives will be key to ensure innovations like genetics, microbiome, and imaging biomarkers translate into improved mobility, reduced pain, and increased quality of life. By integrating cutting-edge, multidisciplinary science with patients' lived experience, researchers can unlock the full potential of new technologies to deliver transformative outcomes. The future is bright for precision diagnosis, tightly controlled treatment, and even prevention of axSpA.

COVID-19 Vaccine Knowledge, Attitude, Acceptance and Hesitancy among Pregnancy and Breastfeeding: Systematic Review of Hospital-Based Studies.

The risk of unfavourable outcomes for SARS-CoV-2 infection is significant during pregnancy and breastfeeding. Vaccination is a safe and effective measure to lower this risk. This study aims at reviewing the literature concerning the anti-SARS-CoV-2 vaccine's acceptance/hesitancy among pregnant and breastfeeding women attending hospital facilities. A systematic review of literature was carried out. Hospital-based observational studies related to vaccination acceptance, hesitancy, knowledge and attitude among pregnant and breastfeeding women were included. Determinants of acceptance and hesitancy were investigated in detail. Quality assessment was done via the Johann Briggs Institute quality assessment tools. After literature search, 43 studies were included, 30 of which only focused on pregnant women (total sample 25,862 subjects). Sample size ranged from 109 to 7017 people. Acceptance of the SARS-CoV-2 vaccine ranged from 16% to 78.52%; vaccine hesitancy ranged between 91.4% and 24.5%. Fear of adverse events for either the woman, the child, or both, was the main driver for hesitancy. Other determinants of hesitancy included religious concerns, socioeconomic factors, inadequate information regarding the vaccine and lack of trust towards institutions. SARS-CoV-2 vaccine hesitancy in hospitalized pregnant women appears to be significant, and efforts for a more effective communication to these subjects are required.

Fibrosing Progressive Interstitial Lung Disease in Rheumatoid Arthritis: A Multicentre Italian Study.

BACKGROUND: The INBUILD study demonstrated the efficacy of nintedanib in the treatment of progressive fibrosing interstitial lung disease different to idiopathic pulmonary fibrosis, including rheumatoid arthritis (RA)-related ILD. Nevertheless, the prevalence of RA-ILD patients that may potentially benefit from nintedanib remains unknown. OBJECTIVES AND METHODS: The aim of the present multicentre study was to investigate the prevalence and possible associated factors of fibrosing progressive patterns in a cross-sectional cohort of RA-ILD patients. RESULTS: One hundred and thirty-four RA-ILD patients with a diagnosis of RA-ILD, who were confirmed at high-resolution computed tomography and with a follow-up of at least 24 months, were enrolled. The patients were defined as having a progressive fibrosing ILD in case of a relative decline in forced vital capacity > 10% predicted and/or an increased extent of fibrotic changes on chest imaging in a 24-month period. Respiratory symptoms were excluded to reduce possible bias due to the retrospective interpretation of cough and dyspnea. According to radiologic features, ILD was classified as usual interstitial pneumonia (UIP) in 50.7% of patients, nonspecific interstitial pneumonia in 19.4%, and other patterns in 29.8%. Globally, a fibrosing progressive pattern was recorded in 36.6% of patients (48.5% of patients with a fibrosing pattern) with a significant association to the UIP pattern. CONCLUSION: We observed that more than a third of RA-ILD patients showed a fibrosing progressive pattern and might benefit from antifibrotic treatment. This study shows some limitations, such as the retrospective design. The exclusion of respiratory symptoms' evaluation might underestimate the prevalence of progressive lung disease but increases the value of results.

Effectiveness of biological targeted therapies may discriminate seronegative from seropositive rheumatoid arthritis.

OBJECTIVE: To assess the real-world effectiveness of targeting biologic drugs (bDMARD) in rheumatoid arthritis (RA) patients negative for rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPA). METHODS: We retrospectively selected 81 seronegative and 404 seropositive RA patients receiving treatment with abatacept, anti-tumor necrosis factor (TNF) alpha, or tocilizumab. Effectiveness was evaluated by analyzing drug survival using Kaplan-Meyer analysis over 10-year follow-up. Survival rates were compared by log rank test, and hazard ratios (HRs) of therapy discontinuation were estimated through multivariate Cox-regression. RESULTS: Clinical characteristics were similar between the two groups, except for a significantly higher percentage of inadequate responders to prior bDMARDs in the seronegative RA patients (p= 0.02). Among seronegative RA, tocilizumab demonstrated a survival rate of 73.9% with a mean survival time (MST) of 76.8 months (95% CI 61-92), which was significantly higher than abatacept (37.5%, MST 37.1 months (95% CI 22-51; p= 0.01). Anti-TNF alpha therapy fell in the middle (50.0%, MST 63.5 months (95% CI 47-79) but the difference was not significant. Nevertheless, seropositive RA patients did not show significantly different drug survival rates. Negative predictors of drug discontinuation were RF/ACPA positivity (HR 0.56) and sex male (HR 0.58), but treatment with abatacept (HR 1.88) or anti-TNF alpha (HR 1.79), no co-therapy with cDMARD (HR 1.74), absence of bone erosions (HR 1.41), and higher HAQ (HR 1.58) were positive predictors. CONCLUSIONS: To confirm these preliminary findings and to explore the hypothesis of a distinctive therapeutic algorithm in seronegative RA, prospective studies on larger cohorts are needed.

AI am a rheumatologist: a practical primer to large language models for rheumatologists.

Natural language processing (NLP), a subclass of artificial intelligence, large language models (LLMs), and its latest applications, such as Generative Pre-trained Transformers (GPT), ChatGPT, or LLAMA, have recently become one of the most discussed topics. Up to now, artificial intelligence and NLP ultimately impacted several areas, such as finance, economics and diagnostic/scoring systems in healthcare. Another area that artificial intelligence has affected and will continue to affect increasingly is academic life. This narrative review will define NLP, LLMs and their applications, discuss the opportunities and challenges that components of academic society will experience in rheumatology, and discuss the impact of NLP and LLMs in rheumatology healthcare.

Rheumatoid Arthritis from Easy to Complex Disease: From the "2022 GISEA International Symposium".

Rheumatoid Arthritis (RA) is a systemic disease with many different clinical phenotypes. RA could be classified according to disease duration, seropositivity for rheumatoid factor (RF) and/or anti-citrullinated protein antibodies (ACPA), joint subtype, clinical behaviourbehavior and many other subgroups. In this review, we summarize and discuss the multifaceted aspects of RA, focusing on the relationship between autoimmunity status and clinical outcome, achievement of remission and influence on treatment response, from the 2022 International GISEA/OEG Symposium.

Uncovering the Underworld of Axial Spondyloarthritis.

Axial spondyloarthritis (axial-SpA) is a multifactorial disease characterized by inflammation in sacroiliac joints and spine, bone reabsorption, and aberrant bone deposition, which may lead to ankylosis. Disease pathogenesis depends on genetic, immunological, mechanical, and bioenvironmental factors. HLA-B27 represents the most important genetic factor, although the disease may also develop in its absence. This MHC class I molecule has been deeply studied from a molecular point of view. Different theories, including the arthritogenic peptide, the unfolded protein response, and HLA-B27 homodimers formation, have been proposed to explain its role. From an immunological point of view, a complex interplay between the innate and adaptive immune system is involved in disease onset. Unlike other systemic autoimmune diseases, the innate immune system in axial-SpA has a crucial role marked by abnormal activity of innate immune cells, including γδ T cells, type 3 innate lymphoid cells, neutrophils, and mucosal-associated invariant T cells, at tissue-specific sites prone to the disease. On the other hand, a T cell adaptive response would seem involved in axial-SpA pathogenesis as emphasized by several studies focusing on TCR low clonal heterogeneity and clonal expansions as well as an interindividual sharing of CD4/8 T cell receptors. As a result of this immune dysregulation, several proinflammatory molecules are produced following the activation of tangled intracellular pathways involved in pathomechanisms of axial-SpA. This review aims to expand the current understanding of axial-SpA pathogenesis, pointing out novel molecular mechanisms leading to disease development and to further investigate potential therapeutic targets.

Immunogenicity and Safety of Adjuvanted Recombinant Zoster Vaccine in Rheumatoid Arthritis Patients on Anti-Cellular Biologic Agents or JAK Inhibitors: A Prospective Observational Study.

Rheumatoid arthritis (RA) patients on JAK inhibitors (JAKi) have an increased HZ risk compared to those on biologic DMARDs (bDMARDs). Recently, the Adjuvanted Recombinant Zoster Vaccine (RZV) became available worldwide, showing good effectiveness in patients with inflammatory arthritis. Nevertheless, direct evidence of the immunogenicity of such a vaccine in those on JAKi or anti-cellular bDMARDs is still lacking. This prospective study aimed to assess RZV immunogenicity and safety in RA patients receiving JAKi or anti-cellular bDMARDs that are known to lead to impaired immune response. Patients with classified RA according to ACR/EULAR 2010 criteria on different JAKi or anti-cellular biologics (namely, abatacept and rituximab) followed at the RA clinic of our tertiary center were prospectively observed. Patients received two shots of the RZV. Treatments were not discontinued. At the first and second shots, and one month after the second shot, from all patients with RA, a sample was collected and RZV immunogenicity was assessed and compared between the treatment groups and healthy controls (HCs) receiving RZV for routine vaccination. We also kept track of disease activity at different follow-up times. Fifty-two consecutive RA patients, 44 females (84.61%), with an average age (±SD) of 57.46 ± 11.64 years and mean disease duration of 80.80 ± 73.06 months, underwent complete RZV vaccination between February and June 2022 at our center. At the time of the second shot (1-month follow-up from baseline), anti-VZV IgG titer increased significantly in both groups with similar magnitude (bDMARDs: 2258.76 ± 897.07 mIU/mL; JAKi: 2059.19 ± 876.62 mIU/mL, p < 0.001 for both from baseline). At one-month follow-up from the second shot, anti-VZV IgG titers remained stable in the bDMARDs group (2347.46 ± 975.47) and increased significantly in the JAKi group (2582.65 ± 821.59 mIU/mL, p = 0.03); still, no difference was observed between groups comparing IgG levels at this follow-up time. No RA flare was recorded. No significant difference was shown among treatment groups and HCs. RZV immunogenicity is not impaired in RA patients on JAKi or anti-cellular bDMARDs. A single shot of RZV can lead to an anti-VZV immune response similar to HCs without discontinuing DMARDs.

Evolution of Rheumatoid-Arthritis-Associated Interstitial Lung Disease in Patients Treated with JAK Inhibitors: A Retrospective Exploratory Study.

BACKGROUND: The aim of this multicenter retrospective study was to investigate the effectiveness and safety of the available JAK-inhibitors (JAKi) in patients with rheumatoid arthritis (RA) and interstitial lung disease (ILD). METHODS: We retrospectively analyzed patients with classified RA and RA-ILD undergoing JAKi in 6 Italian tertiary centers from April 2018 to June 2022. We included patients with at least 6 months of active therapy and one high-resolution chest tomography (HRCT) carried out within 3 months of the start of JAKi treatment. The HRCT was then compared to the most recent one carried out within 3 months before the last available follow-up appointment. We also kept track of the pulmonary function tests. RESULTS: We included 43 patients with RA-ILD and 23 males (53.48%) with a median age (interquartile range, IQR) of 68.87 (61.46-75.78) treated with JAKi. The median follow-up was 19.1 months (11.03-34.43). The forced vital capacity remained stable in 22/28 (78.57%) patients, improved in 3/28 (10.71%) and worsened in 3/28 (10.71%). The diffusing capacity of lung for carbon monoxide showed a similar trend, remaining stable in 18/25 (72%) patients, improving in 2/25 (8%) and worsening in 5/25 (20%). The HRCT remained stable in 37/43 (86.05) cases, worsened in 4/43 (9.30%) and improved in the last 2 (4.65%). DISCUSSION: This study suggests that JAKi therapy might be a safe therapeutic option for patients with RA-ILD in a short-term follow-up.

Machine learning applied to MRI evaluation for the detection of lymph node metastasis in patients with locally advanced cervical cancer treated with neoadjuvant chemotherapy.

PURPOSE: Concurrent cisplatin-based chemotherapy and radiotherapy (CCRT) plus brachytherapy is the standard treatment for locally advanced cervical cancer (LACC). Platinum-based neoadjuvant chemotherapy (NACT) followed by radical hysterectomy is an alternative for patients with stage IB2-IIB disease. Therefore, the correct pre-treatment staging is essential to the proper management of this disease. Pelvic magnetic resonance imaging (MRI) is the gold standard examination but studies about MRI accuracy in the detection of lymph node metastasis (LNM) in LACC patients show conflicting data. Machine learning (ML) is emerging as a promising tool for unraveling complex non-linear relationships between patient attributes that cannot be solved by traditional statistical methods. Here we investigated whether ML might improve the accuracy of MRI in the detection of LNM in LACC patients. METHODS: We analyzed retrospectively LACC patients who underwent NACT and radical hysterectomy from 2015 to 2020. Demographic, clinical and MRI characteristics before and after NACT were collected, as well as information about post-surgery histopathology. Random features elimination wrapper was used to determine an attribute core set. A ML algorithm, namely Extreme Gradient Boosting (XGBoost) was trained and validated with tenfold cross-validation. The performances of the algorithm were assessed. RESULTS: Our analysis included n.92 patients. FIGO stage was IB2 in n.4/92 (4.3%), IB3 in n.42/92 (45%), IIA1 in n.1/92 (1.1%), IIA2 in n.16/92 (17.4%) and IIB in n.29/92 (31.5%). Despite detected neither at pre-treatment and post-treatment MRI in any patients, LNM occurred in n.16/92 (17%) patients. The attribute core set used to train ML algorithms included grading, histotypes, age, parity, largest diameter of lesion at either pre- and post-treatment MRI, presence/absence of fornix infiltration at pre-treatment MRI and FIGO stage. XGBoost showed a good performance (accuracy 89%, precision 83%, recall 78%, AUROC 0.79). CONCLUSIONS: We developed an accurate model to predict LNM in LACC patients in NACT, based on a ML algorithm requiring few easy-to-collect attributes.

Anti-SARS-CoV-2 antibody decay after vaccination and immunogenicity of the booster dose of the BNT162b2 mRNA vaccine in patients with psoriatic arthritis on TNF inhibitors.

OBJECTIVES: Scanty data on the anti- SARS-CoV-2 IgG level decay after two-dose BNT162b2 vaccination have been published in patients with psoriatic arthritis (PsA) on TNF inhibitors (TNFi). Similarly, no reports on the immunogenicity of a booster dose in such patients have been provided yet.We aimed to investigate the IgG level decay after two-dose BNT162b2 vaccination and the immunogenicity and safety of the booster dose in PsA patients on TNFi. METHODS: Forty patients with classified PsA on TNFi undergoing booster dose with the BNT162b2 mRNA SARS- CoV-2 vaccine (BioNTech/Pfizer) were enrolled. Fifteen days after the third shot, serum IgG levels against SARS-CoV-2 (Abbott®ARCHITECT i2000SR, positivity cut-off 50 AU/mL) were assayed in all patients. Clinimetrics and treatment data were gathered. TNFi treatment was not discontinued. Sera from healthcare professionals were considered as healthy controls for 1:1 propensity score-matching. Student's t-test and logistic regression were used for investigating differences in immunogenicity between groups and predictors of antibody response. RESULTS: Even though the decay of IgG levels showed similar magnitude between groups, PsA patients had a lower IgG level than matched controls at 4 months after two-dose vaccination (2009.22±4050.22 vs. 6206.59±4968.33 AU/mL, respectively p=0.0006). Booster dose restored IgG levels to a similar extent in both groups (15846.47±12876.48 vs. 20374.46±12797.08 AU/ml p=0.20, respectively). Clinical Disease Activity Index (CDAI) did not change before and after vaccination (6.68±4.38 vs. 4.95±4.20, p=0.19). CONCLUSIONS: A BNT162b2 booster dose should be recommended in PsA patients on TNFi as its administration restores anti-SARS-CoV-2 IgG levels similar to healthy individuals.

Benralizumab for eosinophilic granulomatosis with polyangiitis: a retrospective, multicentre, cohort study.

BACKGROUND: Interleukin-5 (IL-5) inhibitors represent novel therapies for eosinophilic granulomatosis with polyangiitis (EGPA). This study assessed the effectiveness and safety of the IL-5 receptor inhibitor benralizumab in a European cohort of patients with EGPA. METHODS: This retrospective cohort study included patients with EGPA from 28 European referral centres of the European EGPA Study Group across six countries (Italy, France, UK, Russia, Spain, and Switzerland) who received benralizumab as any line of treatment between Jan 1, 2019, and Sep 30, 2022. We assessed the rates of complete response, defined as no disease activity (Birmingham Vasculitis Activity Score [BVAS] of 0) and a prednisone dose of up to 4 mg/day, in contrast to partial response, defined as a BVAS of 0 and a prednisone dose greater than 4 mg/day. Active disease manifestations, pulmonary function, variation in glucocorticoid dose, and safety outcomes were also assessed over a 12-month follow-up. FINDINGS: 121 patients with relapsing-refractory EGPA treated with benralizumab at the dose approved for eosinophilic asthma were included (64 [53%] women and 57 [47%] men; median age at the time of beginning benralizumab treatment 54·1 years [IQR 44·2-62·2]). Complete response was reported in 15 (12·4%, 95% CI 7·1-19·6) of 121 patients at month 3, 25 (28·7%, 19·5-39·4) of 87 patients at month 6, and 32 (46·4%, 34·3-58·8) of 69 patients at month 12; partial response was observed in an additional 43 (35·5%, 27·0-44·8) patients at month 3, 23 (26·4%, 17·6-37·0) at month 6, and 13 (18·8%, 10·4-30·1) at month 12. BVAS dropped from 3·0 (IQR 2·0-8·0) at baseline to 0·0 (0·0-2·0) at months 3 and 6, and to 0·0 (0·0-1·0) at month 12. The proportion of patients with systemic manifestations, active peripheral neurological disease, ear, nose, and throat involvement, and pulmonary involvement decreased, with an improvement in lung function tests. Six patients relapsed after having a complete response. The oral prednisone (or equivalent) dose decreased from 10·0 mg/day (5·0-12·5) at baseline to 5·0 mg/day (3·6-8·5) at month 3 (p<0·01), to 5·0 mg/day (2·5-6·3) at month 6, and to 2·5 mg/day (0·0-5·0) at month 12 (p<0·0001). 19 (16%) of 121 patients had adverse events and 16 (13%) discontinued benralizumab. INTERPRETATION: These data suggest that benralizumab could be an effective treatment for EGPA in real-life clinical practice. Further clinical trials are required to confirm the efficacy of benralizumab in patients with a higher baseline disease activity. FUNDING: None.