Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation

Utility of online GCA risk models in predicting the result of temporal artery biopsy within a clinical setting: study of diagnostic and screening tests

BACKGROUND

Temporal artery biopsy (TAB) is the gold standard for the diagnosis of giant cell arteritis (GCA) but has many limitations. The Ing model, González-López model, and Weis model are tools to estimate a patient's likelihood of GCA. This study investigates the utility of these prediction models in triaging patients referred for TAB.

METHODS

This study is a retrospective examination of patients who underwent TAB by a neuro-ophthalmologist over a 5-year period. Data collected through chart review were inputted into prediction models to evaluate GCA risk and compared with TAB results and clinical diagnosis. Cut-off values for 100% sensitivity and specificity for TAB result were used to determine whether TAB could be avoided where there was preoperative certainty of the result.

RESULTS

Among 155 eligible patients, mean age was 73 years, and 78.1% were female. TAB was negative in 103 patients (66.5%) and positive in 42 patients (27.1%). Twenty-three patients (22.3%) were diagnosed clinically and treated for biopsy-negative GCA. The Ing model had no positive biopsies below 10.59% and no negative biopsies above 68.44%. The González-López model had no positive biopsies below 0.27% and no negative biopsies above 98.08%. The Weis model had no positive biopsies with a score less than zero.

CONCLUSION

Forty-one biopsies (28.9%) could have been avoided using the Ing model, 9 (6.34%) using the González-López model, and 28 (19.7%) using the Weis model. The findings suggest that the Ing and Weis models are useful screening tools for GCA with the potential to improve the effective use of health care resources.

Imaging Findings in Giant Cell Arteritis: Don't Turn a Blind Eye to the Obvious!

Giant cell arteritis (GCA) is the most common primary large vessel systemic vasculitis in the Western World. Even though the involvement of scalp and intracranial vessels has received much attention in the neuroradiology literature, GCA, being a systemic vasculitis, can involve multiple other larger vessels including the aorta and its major head and neck branches. Herein, the authors present a pictorial review of the various cranial, extracranial, and orbital manifestations of GCA. An increased awareness of this entity may help with timely and accurate diagnosis, helping expedite therapy and preventing serious complications.

Applications of artificial intelligence and bioinformatics methodologies in the analysis of ocular biofluid markers: a scoping review

PURPOSE

This scoping review summarizes the applications of artificial intelligence (AI) and bioinformatics methodologies in analysis of ocular biofluid markers. The secondary objective was to explore supervised and unsupervised AI techniques and their predictive accuracies. We also evaluate the integration of bioinformatics with AI tools.

METHODS

This scoping review was conducted across five electronic databases including EMBASE, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics were included.

RESULTS

A total of 10,262 articles were retrieved from all databases and 177 studies met the inclusion criteria. The most commonly studied ocular diseases were diabetic eye diseases, with 50 papers (28%), while glaucoma was explored in 25 studies (14%), age-related macular degeneration in 20 (11%), dry eye disease in 10 (6%), and uveitis in 9 (5%). Supervised learning was used in 91 papers (51%), unsupervised AI in 83 (46%), and bioinformatics in 85 (48%). Ninety-eight papers (55%) used more than one class of AI (e.g. > 1 of supervised, unsupervised, bioinformatics, or statistical techniques), while 79 (45%) used only one. Supervised learning techniques were often used to predict disease status or prognosis, and demonstrated strong accuracy. Unsupervised AI algorithms were used to bolster the accuracy of other algorithms, identify molecularly distinct subgroups, or cluster cases into distinct subgroups that are useful for prediction of the disease course. Finally, bioinformatic tools were used to translate complex biomarker profiles or findings into interpretable data.

CONCLUSION

AI analysis of biofluid markers displayed diagnostic accuracy, provided insight into mechanisms of molecular etiologies, and had the ability to provide individualized targeted therapeutic treatment for patients. Given the progression of AI towards use in both research and the clinic, ophthalmologists should be broadly aware of the commonly used algorithms and their applications. Future research may be aimed at validating algorithms and integrating them in clinical practice.

Giant cell arteritis: reviewing the advancing diagnostics and management

Giant Cell Arteritis (GCA) is well known to be a critical ischaemic disease that requires immediate medical recognition to initiate treatment and where one in five people still suffer visual loss. The immunopathophysiology has continued to be characterised, and the influencing of ageing in the development of GCA is beginning to be understood. Recent national and international guidelines have supported the directed use of cranial ultrasound to reduce diagnostic delay and improve clinical outcomes. Immediate high dose glucocorticoids remain the standard emergency treatment for GCA, with a number of targeted agents that have been shown in clinical trials to have superior clinical efficacy and steroid sparing effects. The aim of this review was to present the latest advances in GCA that have the potential to influence routine clinical practice.

Temporal artery biopsy

Giant cell arteritis is a common vasculitis in patients over the age of 50 years old. If not promptly recognized and aggressively treated with high-dose glucocorticoids, ischemia resulting in permanent vision loss or stroke can occur. Yet, the treatment with high-dose glucocorticoids over a long period of time can be problematic in this particular patient population given their age and associated comorbidities. Temporal artery biopsies (TAB) are an important diagnostic tool to evaluate patients with suspected giant cell arteritis. Herein, we explore indications for TAB and practical points in obtaining a TAB based on available evidence. We review the surgical procedure itself and associated complications. Lastly, we examine common pathological findings and considerations of alternative diagnoses.

Temporal artery biopsy: A technical guide and review of its importance and indications

Temporal artery biopsy (TAB) is a surgical procedure that enables the histological diagnosis of giant cell arteritis (GCA). Performing a TAB requires expertise and a precise approach. Nevertheless, available data supports the value of tissue diagnosis in managing GCA. The current therapeutic recommendation for GCA is long-term glucocorticoid therapy, with an increasing emphasis on the addition of immunosuppressants/biotherapies. Though effective, immunosuppressants and other such biotherapies may put the patient at more risk. Optimizing the diagnosis through tissue evaluation is therefore important in weighing the risks and benefits of initiating therapeutic intervention. We evaluate the evidence supporting the importance of TAB and its indications. We also describe what technical approaches should be used to maximize sensitivity and to avoid possible complications during the procedure.

Classification Performance of Neural Networks Versus Logistic Regression Models: Evidence From Healthcare Practice

Machine learning encompasses statistical approaches such as logistic regression (LR) through to more computationally complex models such as neural networks (NN). The aim of this study is to review current published evidence for performance from studies directly comparing logistic regression, and neural network classification approaches in medicine.

A literature review was carried out to identify primary research studies which provided information regarding comparative area under the curve (AUC) values for the overall performance of both LR and NN for a defined clinical healthcare-related problem. Following an initial search, articles were reviewed to remove those that did not meet the criteria and performance metrics were extracted from the included articles. Teh initial search revealed 114 articles; 21 studies were included in the study. In 13/21 (62%) of cases, NN had a greater AUC compared to LR, but in most the difference was small and unlikely to be of clinical significance; (unweighted mean difference in AUC 0.03 (95% CI 0-0.06) in favour of NN versus LR.

In the majority of cases examined across a range of clinical settings, LR models provide reasonable performance that is only marginally improved using more complex methods such as NN. In many circumstances, the use of a relatively simple LR model is likely to be adequate for real-world needs but in specific circumstances in which large amounts of data are available, and where even small increases in performance would provide significant management value, the application of advanced analytic tools such as NNs may be indicated.

Giant Cell Arteritis: Updates and Controversies

Abstract

Giant cell arteritis (GCA) is a systemic granulomatous vasculitis affecting the medium and large-size arteries, and may present with a range of ophthalmic findings. This review will cover GCA epidemiology, pathophysiology, clinical presentation, diagnostic workup, and treatment.

Epidemiology and Pathophysiology

GCA is commonly found in elderly patients and individuals of Scandinavian descent. Recent publications suggest it may be more common in African Americans and Hispanics than previously thought. It is very rare in Asian and Middle-Eastern populations, and there is little data regarding African populations. Genetic studies have identified increased risk associated with HLA-DRB1*04. Rather than a response to a specific antigen such as varicella zoster virus, current immunology research suggests that GCA results from changes associated with the aging immune system.

Clinical presentation to Ophthalmology

Arteritic anterior ischemic optic neuropathy is the most common ophthalmic manifestation of GCA, but central or branch retinal artery occlusion, ophthalmic artery occlusion, cranial neuropathies causing diplopia, and more rarely anterior segment ischemia and anisocoria may also occur. Clinical testing including visual field testing, OCT, OCT-A, ICG and fluorescein angiography can be helpful in suggesting a diagnosis in addition to the clinical exam.

Diagnostic Workup

GCA is ultimately a clinical diagnosis, but it is usually supported with lab results, pathology, and/or imaging. Temporal artery biopsy (TAB) remains the gold standard diagnostic test although its sensitivity is debated and practice patterns still vary with respect to sample length and whether unilateral or simultaneous bilateral biopsies are performed. Some studies have reported higher sensitivity of ultrasounds over TAB, with added benefits of time efficiency and cost effectiveness, promoting the diagnostic use of ultrasounds. MRI and even PET CT protocols offer additional options for less invasive diagnostic testing.

Treatment

Vision-threatening GCA is treated acutely with emergent admission for intravenous methylprednisolone, and long-term high dose oral corticosteroids remain the standard of care, despite common and sometimes serious side effects. The use of steroid-sparing alternatives such as tocilizumab is becoming more common and additional agents are being investigated.

Large-Vessel Vasculitis in Ophthalmology: Giant Cell Arteritis and Takayasu Arteritis

ABSTRACT

Giant cell arteritis and Takayasu arteritis are large-vessel vasculitides that share multiple common features but also have significant differences in epidemiology, demographics, clinical presentation, evaluation, and treatment. Giant cell arteritis is more common in elderly patients of Caucasian descent versus Takayasu arteritis, which is more prevalent in younger patients of Asian descent. Although traditionally age has been the main criterion for differentiating the 2 etiologies, modifications in the diagnostic criteria have recognized the overlap between the 2 conditions. In this monograph, we review the diagnostic criteria for both conditions and describe the epidemiology, pathogenesis, histology, evaluation, and management for large-vessel vasculitis in ophthalmology. Additionally, we describe ocular imaging techniques that may be utilized by ophthalmologists to identify manifestations of large-vessel vasculiti- des in patients. Lastly, we compare and contrast the key clinical, laboratory, and pathologic features that might help ophthalmologists to differentiate the 2 entities.

Contextualizing temporal arterial magnetic resonance angiography in the diagnosis of giant cell arteritis: a retrospective cohort study

OBJECTIVES

Imaging modalities have become common in evaluating patients for a possible diagnosis of GCA. This study seeks to contextualize how temporal arterial magnetic resonance angiography (TA-MRA) can be used in facilitating the diagnosis of GCA.

METHODS

A retrospective cohort study was performed on patients who had been previously referred to a rheumatologist for evaluation of possible GCA in Hamilton, Ontario, Canada. Data including clinical features, inflammatory markers, imaging, and biopsy results were extracted. Multivariable logistic regression model to predict the diagnosis of GCA. Using these models, the utility of TA-MRA in series with or in parallel to clinical evaluation was demonstrated across the cohort as well as in subgroups defined by biopsy and imaging status.

RESULTS

In total 268 patients had complete data. Those diagnosed with biopsy- and/or imaging-positive GCA were more likely to demonstrate classic features including jaw claudication and vision loss. Clinical multivariable modelling allowed for fair discriminability [receiver operating characteristic (ROC) 0.759, 95% CI: 0.703, 0.815] for diagnosing GCA; there was excellent discriminability in facilitating the diagnosis of biopsy-positive GCA (ROC 0.949, 0.898-1.000). When used in those with a pre-test probability of 50% or higher, TA-MRA had a positive predictive value of 93.0%; in those with a pre-test probability of 25% or less TA-MRA had a negative predictive value of 89.5%.

CONCLUSION

In those with high disease probability, TA-MRA can effectively rule in disease (and replace temporal artery biopsy). In those with low to medium probability, TA-MRA can help rule out the disease, but this continues to be a challenging diagnostic population.

Identification of diagnostic cytosine-phosphate-guanine biomarkers in patients with gestational diabetes mellitus via epigenome-wide association study and machine learning

OBJECTIVE

To explore gestational diabetes mellitus (GDM) diagnostic markers and establish the predictive model of GDM.

METHODS

We downloaded the DNA methylation data of GSE70453 and GSE102177 from the Gene Expression Omnibus database. Epigenome-wide association study (EWAS) was performed to analyze the relationship between cytosine-phosphate-guanine (CpG) methylation and GDM. And then the logistic regression models were constructed, with the β-values of CpG sites as predictor variable and the GDM occurrence as binary outcome variable. Data from GSE70453 served as training sets and data from GSE102177 served as verification sets.

RESULTS

The EWAS and overlap analysis identified nine-shared significant CpGs in the two DNA methylation data sets. Remarkably, these nine CpGs were differently methylated in GDM samples compared to their matched normal specimens, among which five fully methylated CpGs were finally selected. Importantly, we established a binary logistic regression model based on the above five CpGs, in which cg11169102, cg21179618 and cg21620107 were critical. Hence, we further built a logistic regression model by using the three CpGs and found that the area under the curve was 0.8209. The validation of the model by using the verification sets indicated the area under the curve was 0.8519.

CONCLUSIONS

We identified potential CpG biomarkers for the diagnosis of gestational diabetes mellitus patients through using EWAS and Logistic regression models in combination.

Current advances in giant cell arteritis

PURPOSE OF REVIEW

The aim of this study was to present the latest advances in giant cell arteritis (GCA) care, and recent national and international rheumatology societies guidance which influences clinical practice.

RECENT FINDINGS

Cranial ultrasound reduces diagnostic delay and improves clinical outcomes. Immediate high dose glucocorticoids remain the standard treatment for GCA. Controlled trial evidence using Tocilizumab, an interleukin-6 receptor antagonist, shows good clinical efficacy with steroid-sparing effects.

SUMMARY

Improved patient outcomes require formalizing pathways to diagnosis and closer liaison with rheumatology for long-term management with second-line therapies.

Diagnosing Giant Cell Arteritis: A Comprehensive Practical Guide for the Practicing Rheumatologist

Giant cell arteritis (GCA) is the most common large vessel vasculitis in the elderly population. In recent years, advanced imaging has changed the way GCA can be diagnosed in many locations. The GCA fast-track clinic (FTC) approach combined with ultrasound (US) examination allows prompt treatment and diagnosis with high certainty. FTCs have been shown to improve prognosis while being cost effective. However, all diagnostic modalities are highly operator dependent, and in many locations expertise in advanced imaging may not be available. In this paper, we review the current evidence on GCA diagnostics and propose a simple algorithm for diagnosing GCA for use by rheumatologists not working in specialist centres.

Giant Cell Arteritis and COVID-19: Similarities and Discriminators. A Systematic Literature Review

OBJECTIVE

To identify shared and distinct features of giant cell arteritis (GCA) and coronavirus disease 2019(COVID-19) to reduce diagnostic errors that could cause delays in correct treatment.

METHODS

Two systematic literature reviews determined the frequency of clinical features of GCA and COVID-19 in published reports. Frequencies in each disease were summarized using medians and ranges.

RESULTS

Headache was common in GCA but was also observed in COVID-19 (GCA 66%, COVID-19 10%). Jaw claudication or visual loss (43% and 26% in GCA, respectively) generally were not reported in COVID-19. Both diseases featured fatigue (GCA 38%, COVID-19 43%) and elevated inflammatory markers (C-reactive protein [CRP] elevated in 100% of GCA, 66% of COVID-19), but platelet count was elevated in 47% of GCA but only 4% of COVID-19 cases. Cough and fever were commonly reported in COVID-19 and less frequently in GCA (cough, 63% for COVID-19 vs 12% for GCA; fever, 83% for COVID-19 vs 27% for GCA). Gastrointestinal upset was occasionally reported in COVID-19 (8%), rarely in GCA (4%). Lymphopenia was more common in COVID-19 than GCA (53% in COVID-19, 2% in GCA). Alteration of smell and taste have been described in GCA but their frequency is unclear.

CONCLUSION

Overlapping features of GCA and COVID-19 include headache, fever, elevated CRP and cough. Jaw claudication, visual loss, platelet count and lymphocyte count may be more discriminatory. Physicians should be aware of the possibility of diagnostic confusion. We have designed a simple checklist to aid evidence-based evaluation of patients with suspected GCA.

A Diagnostic Algorithm Based on a Simple Clinical Prediction Rule for the Diagnosis of Cranial Giant Cell Arteritis

Background: Risk stratification based on pre-test probability may improve the diagnostic accuracy of temporal artery high-resolution compression sonography (hrTCS) in the diagnostic workup of cranial giant cell arteritis (cGCA). Methods: A logistic regression model with candidate items was derived from a cohort of patients with suspected cGCA (n = 87). The diagnostic accuracy of the model was tested in the derivation cohort and in an independent validation cohort (n = 114) by receiver operator characteristics (ROC) analysis. The clinical items were composed of a clinical prediction rule, integrated into a stepwise diagnostic algorithm together with C-reactive protein (CRP) values and hrTCS values. Results: The model consisted of four clinical variables (age > 70, headache, jaw claudication, and anterior ischemic optic neuropathy). The diagnostic accuracy of the model for discrimination of patients with and without a final clinical diagnosis of cGCA was excellent in both cohorts (area under the curve (AUC) 0.96 and AUC 0.92, respectively). The diagnostic algorithm improved the positive predictive value of hrCTS substantially. Within the algorithm, 32.8% of patients (derivation cohort) and 49.1% (validation cohort) would not have been tested by hrTCS. None of these patients had a final diagnosis of cGCA. Conclusion: A diagnostic algorithm based on a clinical prediction rule improves the diagnostic accuracy of hrTCS.

Artificial neural network and logistic regression modelling to characterize COVID-19 infected patients in local areas of Iran

Background

COVID-19 is an infectious disease that started spreading globally at the end of 2019. Due to differences in patient characteristics and symptoms in different regions, in this research, a comparative study was performed on COVID-19 patients in 6 provinces of Iran. Also, multilayer perceptron (MLP) neural network and Logistic Regression (LR) models were applied for the diagnosis of COVID-19.

Methods

A total of 1043 patients with suspected COVID-19 infection in Iran participated in this study. 29 characteristics, symptoms and underlying disease were obtained from hospitalized patients. Afterwards, we compared the obtained data between confirmed cases. Furthermore, the data was applied for building the ANN and LR models to diagnosis the infected patients by COVID-19.

Results

In 750 confirmed patients, Common symptoms were: fever (%) >37.5 °C, cough, shortness of breath, fatigue, chills and headache. The most common underlying diseases were: hypertension, diabetes, chronic obstructive pulmonary disease and coronary heart disease. Finally, the accuracy of the ANN model to the diagnosis of COVID-19 infection was higher than the LR model.

Conclusion

The prevalent symptoms and underlying diseases of COVID-19 patients were similar in different provinces, but the incidence of symptoms was significantly different from each other. Also, the study demonstrated that ANN and LR models have a high ability in the diagnosis of COVID-19 infection.

Diagnosis and management of giant cell arteritis: Major review

Giant cell arteritis is a medical emergency because of the high risk of irreversible blindness and cerebrovascular accidents. While elevated inflammatory markers, temporal artery biopsy and modern imaging modalities are useful diagnostic aids, thorough history taking and clinical acumen still remain key elements in establishing a timely diagnosis. Glucocorticoids are the cornerstone of treatment but are associated with high relapse rates and side effects. Targeted biologic agents may open up new treatment approaches in the future.

Diagnostic Accuracy of Symptoms, Physical Signs, and Laboratory Tests for Giant Cell Arteritis: A Systematic Review and Meta-analysis

Importance

Current clinical guidelines recommend selecting diagnostic tests for giant cell arteritis (GCA) based on pretest probability that the disease is present, but how pretest probability should be estimated remains unclear.

Objective

To evaluate the diagnostic accuracy of symptoms, physical signs, and laboratory tests for suspected GCA.

Data Sources

PubMed, EMBASE, and the Cochrane Database of Systematic Reviews were searched from November 1940 through April 5, 2020.

Study Selection

Trials and observational studies describing patients with suspected GCA, using an appropriate reference standard for GCA (temporal artery biopsy, imaging test, or clinical diagnosis), and with available data for at least 1 symptom, physical sign, or laboratory test.

Data Extraction and Synthesis

Screening, full text review, quality assessment, and data extraction by 2 investigators. Diagnostic test meta-analysis used a bivariate model.

Main Outcome(s) and Measures

Diagnostic accuracy parameters, including positive and negative likelihood ratios (LRs).

Results

In 68 unique studies (14 037 unique patients with suspected GCA; of 7798 patients with sex reported, 5193 were women [66.6%]), findings associated with a diagnosis of GCA included limb claudication (positive LR, 6.01; 95% CI, 1.38-26.16), jaw claudication (positive LR, 4.90; 95% CI, 3.74-6.41), temporal artery thickening (positive LR, 4.70; 95% CI, 2.65-8.33), temporal artery loss of pulse (positive LR, 3.25; 95% CI, 2.49-4.23), platelet count of greater than 400 × 103/μL (positive LR, 3.75; 95% CI, 2.12-6.64), temporal tenderness (positive LR, 3.14; 95% CI, 1.14-8.65), and erythrocyte sedimentation rate greater than 100 mm/h (positive LR, 3.11; 95% CI, 1.43-6.78). Findings that were associated with absence of GCA included the absence of erythrocyte sedimentation rate of greater than 40 mm/h (negative LR, 0.18; 95% CI, 0.08-0.44), absence of C-reactive protein level of 2.5 mg/dL or more (negative LR, 0.38; 95% CI, 0.25-0.59), and absence of age over 70 years (negative LR, 0.48; 95% CI, 0.27-0.86).

Conclusions and Relevance

This study identifies the clinical and laboratory features that are most informative for a diagnosis of GCA, although no single feature was strong enough to confirm or refute the diagnosis if taken alone. Combinations of these symptoms might help direct further investigation, such as vascular imaging, temporal artery biopsy, or seeking evaluation for alternative diagnoses.

Validation of the usefulness of artificial neural networks for risk prediction of adverse drug reactions used for individual patients in clinical practice

Artificial neural networks are the main tools for data mining and were inspired by the human brain and nervous system. Studies have demonstrated their usefulness in medicine. However, no studies have used artificial neural networks for the prediction of adverse drug reactions. We aimed to validate the usefulness of artificial neural networks for the prediction of adverse drug reactions and focused on vancomycin -induced nephrotoxicity. For constructing an artificial neural network, a multilayer perceptron algorithm was employed. A 10-fold cross validation method was adopted for evaluating the resultant artificial neural network. In total, 1141 patients who received vancomycin at Hokkaido University Hospital from November 2011 to February 2019 were enrolled. Among these patients, 179 (15.7%) developed vancomycin -induced nephrotoxicity. The top three risk factors of vancomycin -induced nephrotoxicity which are relatively important in the artificial neural networks were average vancomycin trough concentration ≥ 13.0 mg/L and concomitant use of piperacillin–tazobactam and vasopressor drugs. The predictive accuracy of the artificial neural network was 86.3% and that of the multiple logistic regression model (conventional statistical method) was 85.1%. Moreover, area under the receiver operating characteristic curve (AUROC) of the artificial neural network was 0.83. In the 10-fold cross-validation, the accuracy obtained was 86.0% and AUROC was 0.82. The artificial neural network model predicting the vancomycin -induced nephrotoxicity showed good predictive performance. This appears to be the first report of the usefulness of artificial neural networks for an adverse drug reactions risk prediction model.

Positive Predictive Value of the Giant Cell Arteritis Diagnosis in the Danish National Patient Registry: A Validation Study

Purpose

To investigate the positive predictive value (PPV) of the giant cell arteritis (GCA) diagnosis in the Danish National Patient Registry (DNPR).

Patients and Methods

A total of 293 patients aged ≥50 years with a first-time diagnosis of GCA in the DNPR between January 2012 and December 2017 were included. Patients were sampled from two secondary and one tertiary care hospitals in the Central Region Denmark. Two independent investigators (PH & PT) reviewed all medical files, including medical records, treatment, biochemistry, histopathology and imaging, and either confirmed or dismissed the diagnosis of GCA. In case of disagreement, a consensus agreement was reached. Sub-analyses including number of redeemed prescriptions performed temporal artery biopsies (TABs), and number of GCA-related hospital contacts were performed.

Results

We confirmed the diagnosis of GCA in 183/293 patients resulting in a PPV of 62% (95% CI: 57–68). In patients with ≥3 redeemed prescriptions of glucocorticoids (GCs), we confirmed the diagnosis in 166/214 resulting in a PPV of 78% (95% CI: 71–83). In patients with ≥3 redeemed prescriptions of GCs and ≥3 GCA-related hospital contacts, we confirmed the diagnosis in 88/95 resulting in a PPV of 93% (95% CI: 85–96); however, this only included 88/183 confirmed GCA patients.

Conclusion

This is the first study to validate the diagnostic code of GCA in the DNPR. The overall PPV of GCA in the DNPR was 62%. Requiring redeemed prescriptions of GCs and/or GCA-related hospital contacts increase the PPV, but also excludes a significant number of GCA patients.

Practice Preferences: Temporal Artery Biopsy versus Doppler Ultrasound in the Work-Up of Giant Cell Arteritis

To determine whether temporal artery biopsy (TABx) or Doppler ultrasound (US) of the temporal artery is the preferred confirmatory test for giant cell arteritis, an online survey of ophthalmologists and neurologists in North America, Europe and Israel was conducted in 2019; Canadian rheumatologists were also included. There were 406 survey participants with an estimated survey response rate of 18%. Ninety-four per cent of North American practitioners preferred TABx compared with 74% of their European counterparts. Two per cent of North American practitioners preferred Doppler US versus 24% of European physicians. Regional differences were statistically significant (p < .001).

Temporal artery biopsy versus imaging in patients with cranial giant cell arteritis

Temporal artery biopsy (TAB) is frequently still required in patients with suspected cranial giant cell arteritis (GCA). The literature on the performance characteristics of TAB versus imaging is conflicting, and several meta-analyses suggest that TAB is more sensitive than ultrasound. False-positive ultrasound results can occur. Also, diseases that mimic the symptoms, signs or imaging characteristics of GCA may only be revealed on pathology.

European Headache Federation recommendations for neurologists managing giant cell arteritis

BACKGROUND AND AIM

Giant cell arteritis (GCA) remains a medical emergency because of the risk of sudden irreversible sight loss and rarely stroke along with other complications. Because headache is one of the cardinal symptoms of cranial GCA, neurologists need to be up to date with the advances in investigation and management of this condition. The aim of this document by the European Headache Federation (EHF) is to provide an evidence-based and expert-based recommendations on GCA.

METHODS

The working group identified relevant questions, performed systematic literature review and assessed the quality of available evidence, and wrote recommendations. Where there was not a high level of evidence, the multidisciplinary (neurology, ophthalmology and rheumatology) group recommended best practice based on their clinical experience.

RESULTS

Across Europe, fast track pathways and the utility of advanced imaging techniques are helping to reduce diagnostic delay and uncertainty, with improved clinical outcomes for patients. GCA is treated with high dose glucocorticoids (GC) as a first line agent however long-term GC toxicity is one of the key concerns for clinicians and patients. The first phase 2 and phase 3 randomised controlled trials of Tocilizumab, an IL-6 receptor antagonist, have been published. It is now been approved as the first ever licensed drug to be used in GCA.

CONCLUSION

The present article will outline recent advances made in the diagnosis and management of GCA.

Ischemic Optic Neuropathy

PURPOSE OF REVIEW

Vision is often threatened or lost by acute ischemic damage to the optic nerves. Such pathology most often affects the anterior portion of the nerve and is visible on funduscopic examination. Ischemic optic neuropathy is associated with typical vascular risk factors and with one systemic disease in particular: giant cell arteritis (GCA). This article provides an overview of the three major classes of ischemic optic neuropathy, including information on risk factors, differential diagnosis, evaluation, and management.

RECENT FINDINGS

Optical coherence tomography provides precise anatomic imaging in ischemic optic neuropathy, showing neural loss weeks before it is visible on examination. Refinements of optical coherence tomography reveal optic nerve microvasculature and may assist in understanding pathogenesis and verifying diagnosis. New diagnostic algorithms and cranial vascular imaging techniques help define the likelihood of GCA in patients with ischemic optic neuropathy. Finally, intraocular drug and biological agent delivery holds promise for nonarteritic ischemic optic neuropathy, whereas newer immunologic agents may provide effective steroid-sparing treatment for GCA.

SUMMARY

It is essential to recognize ischemic optic neuropathy upon presentation, especially to determine the likelihood of GCA and the need for immediate steroid therapy. A broad differential diagnosis should be considered so as not to miss alternative treatable pathology, especially in cases with retrobulbar optic nerve involvement.