PurposeTo detect visual field (VF) progression by analyzing spatial pattern changes.MethodsWe selected 12,217 eyes from 7360 patients with at least five reliable 24-2 VFs and 5 years of follow-up with an interval of at least 6 months. VFs were decomposed into 16 archetype patterns previously derived by artificial intelligence techniques. Linear regressions were applied to the 16 archetype weights of VF series over time. We defined progression as the decrease rate of the normal archetype or any increase rate of the 15 VF defect archetypes to be outside normal limits. The archetype method was compared with mean deviation (MD) slope, Advanced Glaucoma Intervention Study (AGIS) scoring, Collaborative Initial Glaucoma Treatment Study (CIGTS) scoring, and the permutation of pointwise linear regression (PoPLR), and was validated by a subset of VFs assessed by three glaucoma specialists.ResultsIn the method development cohort of 11,817 eyes, the archetype method agreed more with MD slope (kappa: 0.37) and PoPLR (0.33) than AGIS (0.12) and CIGTS (0.22). The most frequently progressed patterns included decreased normal pattern (63.7%), and increased nasal steps (16.4%), altitudinal loss (15.9%), superior-peripheral defect (12.1%), paracentral/central defects (10.5%), and near total loss (10.4%). In the clinical validation cohort of 397 eyes with 27.5% of confirmed progression, the agreement (kappa) and accuracy (mean of hit rate and correct rejection rate) of the archetype method (0.51 and 0.77) significantly (P < 0.001 for all) outperformed AGIS (0.06 and 0.52), CIGTS (0.24 and 0.59), MD slope (0.21 and 0.59), and PoPLR (0.26 and 0.60).ConclusionsThe archetype method can inform clinicians of VF progression patterns.

Précis: Characteristics of the most mentioned glaucoma articles on the internet were analyzed, allowing a better understanding of the dissemination of glaucoma research to the general public. Purpose:The aim was to determine the 100 most mentioned articles on the internet in the field of glaucoma and analyze their characteristics. Materials and Methods:We identified the top 100 glaucoma articles with the highest Altmetric Attention Score (AAS), an automatically calculated metric for monitoring social media. Each article was evaluated for several characteristics including year of publication, title, journal name, journal impact factor (IF), article topic, article type, affiliation, and online mentions (news, blog, policy, Twitter, Facebook, etc.). Correlation analysis was conducted for AAS with these characteristics. Results:The selected 100 articles came from 44 journals with more than half (56%) published in ophthalmology-specific journals. There was no significant correlation between IF and number of articles in a specific journal or AAS (P > 0.1), but the number of articles in the top 100 was higher for ophthalmology journals with a higher IF (P < 0.05). Original study was the most common study type (87%), of which clinical observation study was the most common subgroup (40%). Epidemiology/risk factor and basic science were the most common article topics (each 24%), followed by medical treatment (13%). Article topics regarding medical treatment had a significantly greater AAS than other topics (P < 0.05). Of the top 5 articles, more than half (60%) were related to "Lifestyle choice" topics.Conclusions: There was no association between journal IF and AAS, consistent with previous studies. 90% of journals that had articles in the top 100 had a Twitter page. "Lifestyle choice" activities and other modifiable risk factors attracted significant online attention regarding glaucoma studies, with two of the top three most mentioned articles related to dietary intake. The present study thus provides a better understanding of online engagement with glaucoma research and the dissemination of this research to the general public.

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