Immune Checkpoint Inhibitor-induced Polymyalgia Rheumatica.

Polymyalgia rheumatica (PMR) immune-related adverse events (ICI-PMRs) represent a novel, distinct entity, despite many clinical, laboratory, and imaging similarities to classical PMR. Important questions remain in differentiating ICI-PMR from classical PMR, as well as other immune-related adverse events and PMR mimics. Despite this, ICI-PMR currently takes treatment cues from classical PMR, albeit with considerations relevant to cancer immunotherapy. Comparisons between ICI-PMR and classical PMR may provide further bidirectional insights, especially given that important questions remain unanswered about both diseases. The cause of classical PMR remains poorly understood, and ICI-PMR may represent a model of induced PMR, with important therapeutic implications.

The Need for Classification Criteria of Immune Checkpoint Inhibitor-induced inflammatory Arthritis: A Scoping Review.

Immune checkpoint inhibitor-induced inflammatory arthritis (ICI-IA) is an immune-related adverse event that can occur as a result of receiving ICIs for cancer treatment. Thus far, ICI-IA has been described variably in the literature, in part due to varying presentations that evolve over time, as well as a lack of standardized definitions and classification. This scoping review aggregates various descriptions of ICI-IA, highlighting the most prominent attributes of ICI-IA from categories such as symptoms, signs, imaging, and laboratory findings as well as discussing potential mimic conditions.

Characterising polymyalgia rheumatica on whole-body 18F-FDG PET/CT: an atlas.

The impact of modern imaging in uncovering the underlying pathology of PMR cannot be understated. Long dismissed as an inflammatory syndrome with links to the large vessel vasculitis giant cell arteritis (GCA), a pathognomonic pattern of musculotendinous inflammation is now attributed to PMR and may be used to confirm its diagnosis. Among the available modalities, 18F-fluorodeoxyglucose (18F-FDG) PET/CT is increasingly recognized for its high sensitivity and specificity, as well as added ability to detect concomitant large vessel GCA and exclude other relevant differentials like infection and malignancy. This atlas provides a contemporary depiction of PMR's pathology and outlines how this knowledge translates into a pattern of findings on whole body 18F-FDG PET/CT that can reliably confirm its diagnosis.

Managing Cardiovascular and Cancer Risk Associated with JAK Inhibitors.

Janus kinase inhibitors (JAKi) have enormous appeal as immune-modulating therapies across many chronic inflammatory diseases, but recently this promise has been overshadowed by questions regarding associated cardiovascular and cancer risk emerging from the ORAL Surveillance phase 3b/4 post-marketing requirement randomized controlled trial. In that study of patients with rheumatoid arthritis with existing cardiovascular risk, tofacitinib, the first JAKi registered for chronic inflammatory disease, failed to meet non-inferiority thresholds when compared with tumor necrosis factor inhibitors for both incident major adverse cardiovascular events and incident cancer. While this result was unexpected by many, subsequently published observational data have also supported this finding. Notably, however, such a risk has largely not yet been demonstrated in patients outside the specific clinical situation examined in the trial, even in the face of many studies examining this. Nevertheless, this signal has practically re-aligned approaches to both tofacitinib and other JAKi to varying extents, in other patient populations and contexts: within rheumatoid arthritis, but also in psoriatic arthritis, axial spondyloarthritis, inflammatory bowel disease, atopic dermatitis, and beyond. Application to individual patients can be more challenging but remains important to harness the substantive potential of JAKi to the maximum extent safely possible. This review not only explores the evolution of the regulatory response to the signal, its informing data, biological plausibility, and its impact on guidelines, but also the many factors that clinicians must consider in navigating cardiovascular and cancer risk for their patients considering JAKi as immune-modulating therapy.

Imaging of giant cell arteritis - recent advances.

Imaging is increasingly being used to guide clinical decision-making in patients with giant cell arteritis (GCA). While ultrasound has been rapidly adopted in fast-track clinics worldwide as an alternative to temporal artery biopsy for the diagnosis of cranial disease, whole-body PET/CT is emerging as a potential gold standard test for establishing large vessel involvement. However, many unanswered questions remain about the optimal approach to imaging in GCA. For example, it is uncertain how best to monitor disease activity, given there is frequent discordance between imaging findings and conventional disease activity measures, and imaging changes typically fail to resolve completely with treatment. This chapter addresses the current body of evidence for the use of imaging modalities in GCA across the spectrum of diagnosis, monitoring disease activity, and long-term surveillance for structural changes of aortic dilatation and aneurysm formation and provides suggestions for future research directions.

Diagnosis of septic arthritis in the acute care setting: the value of routine intra-operative sample culture.

Objective: Diagnosing septic arthritis can be challenging and frequently involves clinical assessment, laboratory investigations and synovial fluid analysis. We sought to determine the utility of synovial aspiration and intra-operative synovial fluid and tissue culture for the accurate diagnosis of septic arthritis. Methods: We carried out a retrospective review of the records of patients referred to a tertiary orthopaedic unit with possible septic arthritis between 2015 and 2019 inclusive, including clinical and laboratory data for this cohort study. Performance characteristics were determined for synovial aspiration, intra-operative synovial fluid and tissue culture in diagnosing expert review-determined true septic arthritis. Concordance between discharge diagnosis, antibiotic prescribing and true septic arthritis was determined. Results: Of 268 patients identified with suspected septic arthritis, 143 underwent both synovial fluid aspiration and intra-operative synovial fluid and tissue biopsy culture. True septic arthritis was not differentiated significantly by laboratory parameters including serum white cell count (WCC), CRP or synovial WCC. Considering only patients with negative pre-operative synovial aspirate cultures, intra-operative samples led to diagnosis of true septic arthritis in 6 of 63 patients [number needed to treat (NNT) 10.5]. For all patients sampled in theatre, positive synovial tissue biopsy was the only evidence of true septic arthritis in six (NNT 23.9). Despite insufficient microbiological evidence, 27 of the 59 patients who did not have septic arthritis received a discharge diagnosis of septic arthritis, 26 of whom were discharged with antibiotics. Conclusion: Intra-operative sample collection, particularly tissue biopsy, increases the likelihood of a true septic arthritis diagnosis. Such measures might help to reduce diagnostic ambiguity in clinical practice and might therefore reduce overtreatment.

Systematic review of colchicine neuromyopathy: Risk factors, duration and resolution.

OBJECTIVE: To identify reports of colchicine-induced neuropathy and myopathy and ascertain risk factors associated with this toxicity at commonly used doses. METHODS: A systematic review of case reports was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA methodology). PubMed and EMBASE were searched through October 2021 for case reports of neuropathy and/or myopathy associated with the use of colchicine at therapeutic doses. RESULTS: A total of 143 cases of neuromyopathy from 99 articles were identified as having a "definite" or "probable" association with colchicine usage, as assessed by the Naranjo algorithm. Most of these cases presented with features of both neuropathy and myopathy (n=72, 51%) but symptoms of myopathy were predominant. The mean total daily dose was 1.25±0.60 mg and 48% had been taking colchicine for more than 12 months before presenting with neuromyopathy. A total of 117 (82%) of all reports had either a significant co-morbidity or possible colchicine drug-drug interaction, while 57 (40%) had both risk factors. A total of 26 cases (18%) had no significant risk factor but only 15 of these reports contained complete descriptions of the patient's co-morbidities and co-medications. Cessation of colchicine generally led to complete resolution of symptoms in 70% of cases within a median of 21 days. There were 3 deaths reported which were due to multi-organ failure despite cessation of colchicine and medical management. Colchicine was restarted at reduced doses in 15 cases and 73% had no symptom recurrence. CONCLUSION: Neuromyopathy is an uncommon but reported adverse effect of colchicine. Cases generally present with proximal myopathy symptoms. Cases of colchicine neuromyopathy are largely reported in patients on commonly used doses. Renal and hepatic dysfunction and medications that inhibit cytochrome P450 3A4 isozyme (CYP3A4) and P-glycoprotein (P-gp) appear to be the most significant risk factors. Fortunately, cessation of colchicine generally leads to complete resolution of symptoms. Recommencement of colchicine at reduced doses appeared to be usually safe.

Developing a deep learning natural language processing algorithm for automated reporting of adverse drug reactions.

The detection of adverse drug reactions (ADRs) is critical to our understanding of the safety and risk-benefit profile of medications. With an incidence that has not changed over the last 30 years, ADRs are a significant source of patient morbidity, responsible for 5%-10% of acute care hospital admissions worldwide. Spontaneous reporting of ADRs has long been the standard method of reporting, however this approach is known to have high rates of under-reporting, a problem that limits pharmacovigilance efforts. Automated ADR reporting presents an alternative pathway to increase reporting rates, although this may be limited by over-reporting of other drug-related adverse events. We developed a deep learning natural language processing algorithm to identify ADRs in discharge summaries at a single academic hospital centre. Our model was developed in two stages: first, a pre-trained model (DeBERTa) was further pre-trained on 1.1 million unlabelled clinical documents; secondly, this model was fine-tuned to detect ADR mentions in a corpus of 861 annotated discharge summaries. This model was compared to a version without the pre-training step, and a previously published RoBERTa model pretrained on MIMIC III, which has demonstrated strong performance on other pharmacovigilance tasks. To ensure that our algorithm could differentiate ADRs from other drug-related adverse events, the annotated corpus was enriched for both validated ADR reports and confounding drug-related adverse events using. The final model demonstrated good performance with a ROC-AUC of 0.955 (95% CI 0.933 - 0.978) for the task of identifying discharge summaries containing ADR mentions, significantly outperforming the two comparator models.

COVID-19 vaccine hesitancy in inflammatory arthritis patients: serial surveys from a large longitudinal national Australian cohort.

OBJECTIVES: To determine COVID-19 vaccine hesitancy rates in inflammatory arthritis patients and identify factors associated with changing vaccine hesitancy over time. METHODS: This investigation was a prospective cohort study of inflammatory arthritis patients from community and public hospital outpatient rheumatology clinics enrolled in the Australian Rheumatology Association Database (ARAD). Two surveys were conducted, one immediately prior to (pre-pandemic) and another approximately 1 year after the start of the pandemic (follow-up). Coronavirus disease 2019 (COVID-19) vaccine hesitancy was measured at follow-up, and general vaccine hesitancy was inferred pre-pandemic; these were used to identify factors associated with fixed and changing vaccine beliefs, including sources of information and broader beliefs about medication. RESULTS: Of the 594 participants who completed both surveys, 74 (12%) were COVID-19 vaccine hesitant. This was associated with pre-pandemic beliefs about medications being harmful (P < 0.001) and overused (P = 0.002), with stronger beliefs resulting in vaccine hesitancy persistent over two time points (P = 0.008, P = 0.005). For those not vaccine hesitant pre-pandemic, the development of COVID-19 vaccine hesitancy was associated with a lower likelihood of seeking out vaccine information from health-care professionals (P < 0.001). COVID-19 vaccine hesitancy was not associated with new influenza vaccine hesitancy (P = 0.138). CONCLUSION: In this study of vaccine beliefs before and during the COVID-19 pandemic, factors associated with COVID-19 vaccine hesitancy in inflammatory arthritis patients varied, depending on vaccine attitudes immediately prior to the start of the pandemic. Fixed beliefs reflected broader views about medications, while fluid beliefs were highly influenced by whether they sought out information from health-care professionals, including rheumatologists.

Examining the Characteristics of Colchicine-Induced Myelosuppression in Clinical Cases: A Systematic Review.

OBJECTIVE: The use of colchicine has been associated with varying degrees of myelosuppression. Despite expanded use in cardiovascular and inflammatory conditions, there remains clinician concern because of potential myelosuppressive side effects. A systematic review was conducted to explore the reported myelosuppressive events of colchicine. METHODS: A systematic review was conducted using the MeSH terms ("colchicine") AND ("myelosuppression," "bone*," "marrow," "suppression," "aplasia," "leukopenia/leucopenia," "lymphopenia," "neutropenia") on September 1, 2020, and was updated on November 30, 2021. The search was conducted in PubMed, ScienceDirect, Scopus, Embase, and Cochrane Library. The search included references published from 1978 to 2020 and was limited to English-language observational studies (ie, case reports, case series, case control studies, and cohort studies) or trial data. RESULTS: In total, 3233 articles were screened, with 30 studies of 47 patients with myelosuppression from colchicine identified. Most patients with myelosuppression had comorbidities, including renal impairment (21/47, 44.7%). Out of 47 patients, 15 (31.9%) and 13 (27.7%) were reported to be concurrently taking cytochrome P450 3A4 (CYP3A4) inhibitors and P-glycoprotein (P-gp) efflux transporter inhibitors, respectively. Patients with renal impairment accounted for the majority of overall patients taking these CYP3A4 and P-gp inhibitors (8/15, 53.3%, and 8/13, 61.5%, respectively). Out of 21 patients with renal impairment, 13 had worsening cytopenia during colchicine use. The presentations ranged from moderate anemia (grade 2) to severe thrombocytopenia, neutropenia, and leukopenia (grade 4). CONCLUSION: Colchicine has few reports of myelosuppression. The majority of patients with myelosuppression had preexisting renal impairment or concomitant CYP3A4 or P-gp inhibitor use. Caution should be taken in this subset of patients with increased monitoring.

Attitudes and practices in the laboratory monitoring of conventional synthetic disease modifying anti-rheumatic drugs by rheumatologists and rheumatology trainees.

OBJECTIVES: There is scant research about laboratory monitoring in people taking conventional synthetic disease-modifying anti-rheumatic drugs (csDMARDs) for rheumatic disease. Our objective was to conduct a scoping study to assess the range of current attitudes and the variation in practice of laboratory monitoring of csDMARDs by rheumatologists and trainees. METHODS: Australian and overseas rheumatologists or trainees were invited through newsletter, Twitter and personal e-mail, to complete an anonymous online survey between 1 February and 22 March 2021. Questions focused on laboratory tests requested by csDMARD prescribed, frequency/pattern of monitoring, influence of additional factors and combination therapy, actions in response to abnormal tests, and attitudes to monitoring frequencies. Results were presented descriptively and analysed using linear and logistic regression. RESULTS: There were 221 valid responses. Most respondents were from Australia (n = 53, 35%) followed by the US (n = 39, 26%), with a slight preponderance of women (n = 84, 56%), ≥ 11 years in rheumatology practice (n = 83, 56%) and in mostly public practice (n = 79, 53%). Respondents had a wide variation in the frequency and scheduling of tests. In general, respondents reported increasing monitoring frequency if patients had numerous comorbidities or if both methotrexate and leflunomide were being taken concurrently. There was a wide variety of responses to abnormal monitoring results and 27 (40%) considered that in general, monitoring tests are performed too frequently. CONCLUSIONS: The results demonstrated a wide variation in the frequency of testing, factors that should influence this, and what responses to abnormal test results are appropriate, indicates a likely lack of evidence and the need to define the risks, benefits and costs of different csDMARD monitoring regimens.

Artificial Intelligence and Deep Learning for Rheumatologists.

Deep learning has emerged as the leading method in machine learning, spawning a rapidly growing field of academic research and commercial applications across medicine. Deep learning could have particular relevance to rheumatology if correctly utilized. The greatest benefits of deep learning methods are seen with unstructured data frequently found in rheumatology, such as images and text, where traditional machine learning methods have struggled to unlock the trove of information held within these data formats. The basis for this success comes from the ability of deep learning to learn the structure of the underlying data. It is no surprise that the first areas of medicine that have started to experience impact from deep learning heavily rely on interpreting visual data, such as triaging radiology workflows and computer-assisted colonoscopy. Applications in rheumatology are beginning to emerge, with recent successes in areas as diverse as detecting joint erosions on plain radiography, predicting future rheumatoid arthritis disease activity, and identifying halo sign on temporal artery ultrasound. Given the important role deep learning methods are likely to play in the future of rheumatology, it is imperative that rheumatologists understand the methods and assumptions that underlie the deep learning algorithms in widespread use today, their limitations and the landscape of deep learning research that will inform algorithm development, and clinical decision support tools of the future. The best applications of deep learning in rheumatology must be informed by the clinical experience of rheumatologists, so that algorithms can be developed to tackle the most relevant clinical problems.