Temporal Artery Vascular Diseases

In the presence of temporal arteritis, clinicians often refer to the diagnosis of giant cell arteritis (GCA). However, differential diagnoses should also be evoked because other types of vascular diseases, vasculitis or not, may affect the temporal artery. Among vasculitis, Anti-neutrophil cytoplasmic antibodies (ANCA)-associated vasculitis is probably the most common, and typically affects the peri-adventitial small vessel of the temporal artery and sometimes mimics giant cell arteritis, however, other symptoms are frequently associated and more specific of ANCA-associated vasculitis prompt a search for ANCA. The Immunoglobulin G4-related disease (IgG4-RD) can cause temporal arteritis as well. Some infections can also affect the temporal artery, primarily an infection caused by the varicella-zoster virus (VZV), which has an arterial tropism that may play a role in triggering giant cell arteritis. Drugs, mainly checkpoint inhibitors that are used to treat cancer, can also trigger giant cell arteritis. Furthermore, the temporal artery can be affected by diseases other than vasculitis such as atherosclerosis, calcyphilaxis, aneurysm, or arteriovenous fistula. In this review, these different diseases affecting the temporal artery are described.

Approach to Common Visual Complaints Presenting to the Outpatient Neurologist

Visual complaints are commonly encountered by the practicing neurologist. We review assessment of vision loss, diplopia, and positive visual phenomena, all of which require a thoughtful evaluation to localize disease and refine management. While many causative entities are unlikely to cause poor visual outcomes, including dry eyes, migraine, and congenital strabismus, others may threaten vision, life, or both, such as posterior communicating artery aneurysms, pituitary apoplexy, or temporal arteritis. A systematic approach to vision loss and diplopia is reviewed along with focused differential diagnoses.

The Impact of Explanations on Layperson Trust in Artificial Intelligence–Driven Symptom Checker Apps: Experimental Study

Background Artificial intelligence (AI)–driven symptom checkers are available to millions of users globally and are advocated as a tool to deliver health care more efficiently. To achieve the promoted benefits of a symptom checker, laypeople must trust and subsequently follow its instructions. In AI, explanations are seen as a tool to communicate the rationale behind black-box decisions to encourage trust and adoption. However, the effectiveness of the types of explanations used in AI-driven symptom checkers has not yet been studied. Explanations can follow many forms, including why-explanations and how-explanations. Social theories suggest that why-explanations are better at communicating knowledge and cultivating trust among laypeople. Objective The aim of this study is to ascertain whether explanations provided by a symptom checker affect explanatory trust among laypeople and whether this trust is impacted by their existing knowledge of disease. Methods A cross-sectional survey of 750 healthy participants was conducted. The participants were shown a video of a chatbot simulation that resulted in the diagnosis of either a migraine or temporal arteritis, chosen for their differing levels of epidemiological prevalence. These diagnoses were accompanied by one of four types of explanations. Each explanation type was selected either because of its current use in symptom checkers or because it was informed by theories of contrastive explanation. Exploratory factor analysis of participants’ responses followed by comparison-of-means tests were used to evaluate group differences in trust. Results Depending on the treatment group, two or three variables were generated, reflecting the prior knowledge and subsequent mental model that the participants held. When varying explanation type by disease, migraine was found to be nonsignificant (P=.65) and temporal arteritis, marginally significant (P=.09). Varying disease by explanation type resulted in statistical significance for input influence (P=.001), social proof (P=.049), and no explanation (P=.006), with counterfactual explanation (P=.053). The results suggest that trust in explanations is significantly affected by the disease being explained. When laypeople have existing knowledge of a disease, explanations have little impact on trust. Where the need for information is greater, different explanation types engender significantly different levels of trust. These results indicate that to be successful, symptom checkers need to tailor explanations to each user’s specific question and discount the diseases that they may also be aware of. Conclusions System builders developing explanations for symptom-checking apps should consider the recipient’s knowledge of a disease and tailor explanations to each user’s specific need. Effort should be placed on generating explanations that are personalized to each user of a symptom checker to fully discount the diseases that they may be aware of and to close their information gap.

Prevalence of large vessel vasculitis in ANCA-associated vasculitis: a retrospective cohort study

ANCA-associated vasculitis (AAV) in general involves small blood vessels and includes granulomatosis with polyangiitis (GPA), eosinophilic granulomatosis with polyangiitis (EGPA), and microscopic polyangiitis (MPA). Although reported in a few studies, the prevalence of large vessel vasculitis (LVV) in patients with AAV remains to be further explored. The goal of the present study was to assess the prevalence of LVV in a cohort of patients with AAV and to characterize this population. We conducted a ten-year retrospective study of a single-center cohort of AAV, including 101 patients with GPA (n = 58), EGPA (n = 28), MPA (n = 15), and compared the groups with or without associated LVV. LVV was diagnosed in five patients, two with aortitis and three with temporal arteritis, corresponding to a total prevalence of 5.0% [95% CI 1.6–11.2%]. This value was significantly higher than the estimated prevalence of LVV in the normal Swiss population (OR 234.9 95% CI 91.18–605.2, p < 0.001). All five patients had GPA, whereas no cases with EGPA or MPA were identified. Anti-PR3 antibodies were detected in four out of five patients, anti-MPO in one patient. Since LVV can occur in a significant proportion of patients with GPA, evaluation for LVV may be considered systematically in the diagnostic workup of AAV.

Calciphylaxis of the temporal artery simulating temporal arteritis

Calciphylaxis is characterized by intense deposition of calcium in small blood vessels, skin, and other organs, described mainly in patients with chronic renal insufficiency, renal transplant of parathyroid dysfunction. To date, there are only seven cases described in literature of calciphylaxis mimicking giant cell arteritis (GCA). In this review, we present the eighth case pathologically documented.

Transthyretin amyloidosis and herpes zoster infection: a mimic of temporal arteritis

We describe the case of a patient who presented with symptoms of persistent headaches, left-sided facial pain and blurry vision of the left eye. The patient had recovered from a herpes zoster infection of the V1 division of the trigeminal nerve 1 month prior. Serum inflammatory markers were elevated, raising concern for temporal arteritis. Empiric high-dose prednisone was initiated. Bilateral temporal artery biopsies were performed but did not show evidence of vasculitis or multinucleated giant cells. Instead, extracellular material deposits were present within the vessel walls. Congo red staining was diagnostic for amyloidosis. Liquid chromatography and mass spectrometry identified the amyloid fibrils to be transthyretin-type (ATTR) consistent with age-related amyloidosis. Temporal artery involvement of amyloidosis is rare but when present is most often due to light chain amyloidosis. Based on our review of the literature, only a few cases of temporal artery ATTR amyloidosis have been reported.

OP0281 EXCESS GIANT CELL ARTERITIS CASES ARE ASSOCIATED WITH PEAKS IN COVID-19 PREVALENCE

Background:Immediately following the first wave of the COVID-19 pandemic, the number of giant cell arteritis (GCA) diagnoses noticeably increased at the Royal National Hospital for Rheumatic Diseases in Bath, UK. Furthermore, there was an increase in the proportion of patients with visual complications [1]. The finding supports the viral hypothesis of GCA aetiopathogenesis as previously described [2]. This not only has ramifications for understanding the underlying disease mechanisms in GCA but also has implications for the provision of local GCA services which may have already be affected by the pandemic.Objectives:The objective of the study was to estimate the incidence of giant cell arteritis during the COVID-19 pandemic years of 2020 – 2021 and compare it to 2019 data. Given that there have now been two distinct peaks of COVID-19 as reflected by hospital admissions of COVID-19-positive patients this has allowed us to investigate if there is a temporal relationship between the prevalence of COVID-19 and the incidence of GCA.Methods:The incidence of GCA was calculated by assessing emailed referrals to the GCA service and the hospital electronic medical records to identity positive cases from 2019 to the current date. Local COVID-19 prevalence was estimated by measuring the number of hospital beds taken up by COVID-19 positive patients, available publicly in a UK Government COVID-19 dataset [3].Results:There were 61 (95% Poisson distribution confidence interval [CI] 47 - 78) probable or definite GCA diagnoses made in 2020 compared to 28 (CI 19 – 40) in 2019 (Figure 1). This is an excess of 33 cases in 2020, or an increase in 118%. Given that 41% of the hospital’s catchment population is over 50, this equates to an annual incidence rate of 13.7 per 100,000 in 2019 and 29.8 per 100,000 in 2020. This compares to a previously estimated regional incidence rate of 21.6 per 100,000 for the South West of the UK [4].Figure 1. Prevalence of hospital COVID-19 and incidence of GCA (2019 – 2021). Graph showing the number of hospital beds occupied by COVID-19-positive patients in 2020 – 2021 (blue circles), number of daily GCA diagnoses in 2020 – 2021 (red circles), and previous GCA diagnoses in 2019 (green circles). The broken lines represent moving averages with a period of 7 days for COVID-19 cases and 28 days for GCA diagnoses.A peak in COVID-19-positive inpatients was seen on 10th April 2020 with a corresponding peak of GCA diagnoses on 29th May 2020, giving a lag period of approximately 6 weeks between these peaks (Figure 1).Conclusion:The incidence of GCA in Bath was significantly increased in 2020 compared to 2019. This may be the result of the widespread infection of the local population with the COVID-19 virus as a precipitating factor. Possible mechanisms include, but are not limited to, endothelial disruption by the virus, immune system priming towards T helper cell type 1 (Th1) cellular immunity and/or activation of the monocyte-macrophage system. More work is currently underway to assess the causal relationship between the two diseases.There was a lag period of 6 weeks between the peak during the first wave of the pandemic and the rise in GCA cases. We shall be closely monitoring the number of referrals that follow the current wave of the pandemic.References:[1]Luther R, Skeoch S, Pauling JD, et al. Increased number of cases of giant cell arteritis and higher rates of ophthalmic involvement during the era of COVID-19. Rheumatol Adv Pract 2020;4:1–4. doi:10.1093/rap/rkaa067[2]Russo MG, Waxman J, Abdoh AA, et al. Correlation between infection and the onset of the giant cell (temporal) arteritis syndrome. Arthritis Rheum 1995;38:374–80. doi:10.1002/art.1780380312[3]England PH. GOV.UK Coronavirus (COVID-19) in the UK. 2021.https://coronavirus.data.gov.uk/details/download (accessed 25 Jan 2021).[4]Smeeth L, Cook C, Hall AJ. Incidence of diagnosed polymyalgia rheumatica and temporal arteritis in the United Kingdom, 1990-2001. Ann Rheum Dis 2006;65:1093–8. doi:10.1136/ard.2005.046912Disclosure of Interests:Ben Mulhearn Speakers bureau: Novartis UK, 2019, Grant/research support from: Chugai, 2019, Jessica Ellis: None declared, Sarah Skeoch: None declared, John Pauling: None declared, Sarah Tansley: None declared

Introducing an ANP-led temporal artery biopsy service for patients with suspected giant cell arteritis

Giant cell arteritis (GCA) is an uncommon autoimmune inflammatory vasculopathy that can lead to the destruction and occlusion of various arteries that consequently can cause serious complications such as stroke or sight loss. It is seen as a medical emergency. The most commonly affected vessel in GCA is the temporal artery in the side of the head, hence the condition is sometimes also referred to as ‘temporal arteritis’. This article discusses the introduction of an advanced nurse practitioner-led temporal artery biopsy service.

The Impact of Explanations on Layperson Trust in Artificial Intelligence–Driven Symptom Checker Apps: Experimental Study (Preprint)

BACKGROUND Artificial intelligence (AI)–driven symptom checkers are available to millions of users globally and are advocated as a tool to deliver health care more efficiently. To achieve the promoted benefits of a symptom checker, laypeople must trust and subsequently follow its instructions. In AI, explanations are seen as a tool to communicate the rationale behind black-box decisions to encourage trust and adoption. However, the effectiveness of the types of explanations used in AI-driven symptom checkers has not yet been studied. Explanations can follow many forms, including <i>why</i>-explanations and <i>how</i>-explanations. Social theories suggest that <i>why</i>-explanations are better at communicating knowledge and cultivating trust among laypeople. OBJECTIVE The aim of this study is to ascertain whether explanations provided by a symptom checker affect explanatory trust among laypeople and whether this trust is impacted by their existing knowledge of disease. METHODS A cross-sectional survey of 750 healthy participants was conducted. The participants were shown a video of a chatbot simulation that resulted in the diagnosis of either a migraine or temporal arteritis, chosen for their differing levels of epidemiological prevalence. These diagnoses were accompanied by one of four types of explanations. Each explanation type was selected either because of its current use in symptom checkers or because it was informed by theories of contrastive explanation. Exploratory factor analysis of participants’ responses followed by comparison-of-means tests were used to evaluate group differences in trust. RESULTS Depending on the treatment group, two or three variables were generated, reflecting the prior knowledge and subsequent mental model that the participants held. When varying explanation type by disease, migraine was found to be nonsignificant (<i>P</i>=.65) and temporal arteritis, marginally significant (<i>P</i>=.09). Varying disease by explanation type resulted in statistical significance for input influence (<i>P</i>=.001), social proof (<i>P</i>=.049), and no explanation (<i>P</i>=.006), with counterfactual explanation (<i>P</i>=.053). The results suggest that trust in explanations is significantly affected by the disease being explained. When laypeople have existing knowledge of a disease, explanations have little impact on trust. Where the need for information is greater, different explanation types engender significantly different levels of trust. These results indicate that to be successful, symptom checkers need to tailor explanations to each user’s specific question and discount the diseases that they may also be aware of. CONCLUSIONS System builders developing explanations for symptom-checking apps should consider the recipient’s knowledge of a disease and tailor explanations to each user’s specific need. Effort should be placed on generating explanations that are personalized to each user of a symptom checker to fully discount the diseases that they may be aware of and to close their information gap.