Predictive value of positive temporal artery biopsies in patients with clinically suspected giant cell arteritis considering temporal artery ultrasound findings

Intraoperative Frozen Section Diagnosis of Giant Cell Arteritis

Giant cell arteritis (GCA) is a systemic vasculitis of medium and large vessels that is diagnosed using temporal artery biopsy (TAB). In this case report, we explored the benefits of frozen section analysis as a rapid intraoperative diagnostic technique for GCA. We present the cases of two patients who underwent TAB with frozen section analysis, to demonstrate the value of this technique in initiating immediate treatment and potentially avoiding unnecessary contralateral biopsies when the frozen section of the first biopsy confirms GCA. We recommend further investigation into the use of frozen section analysis for patients suspected of having GCA, who might otherwise undergo bilateral TAB.

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.

Role of the halo sign in the assessment of giant cell arteritis: a systematic review and meta-analysis

OBJECTIVES

This systematic review and meta-analysis aimed to evaluate the diagnostic value of the halo sign in the assessment of GCA.

METHODS

A systematic literature review was performed using MEDLINE, EMBASE and Cochrane central register databases up to August 2020. Studies informing on the sensitivity and specificity of the US halo sign for GCA (index test) were selected. Studies with a minimum of five participants were included. Study articles using clinical criteria, imaging such as PET-CT and/or temporal artery biopsy (TAB) as the reference standards were selected. Meta-analysis was conducted with a bivariate model.

RESULTS

The initial search yielded 4023 studies. Twenty-three studies (patients n = 2711) met the inclusion criteria. Prospective (11 studies) and retrospective (12 studies) studies in academic and non-academic centres were included. Using clinical diagnosis as the standard (18 studies) yielded a pooled sensitivity of 67% (95% CI: 51, 80) and a specificity of 95% (95% CI: 89, 98%). This gave a positive and negative likelihood ratio for the diagnosis of GCA of 14.2 (95% CI: 5.7, 35.5) and 0.375 (95% CI: 0.22, 0.54), respectively. Using TAB as the standard (15 studies) yielded a pooled sensitivity of 63% (95% CI: 50, 75) and a specificity of 90% (95% CI: 81, 95).

CONCLUSION

The US halo sign is a sensitive and specific approach for GCA assessment and plays a pivotal role in diagnosis of GCA in routine clinical practice.

REGISTRATION

PROSPERO 2020 CRD42020202179.

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.