Association of Atopic Dermatitis and Risk of Glaucoma Surgery: A Global Population-Based Study.

PRCIS: Severe atopic dermatitis (AD) in patients with glaucoma heightens the risk of requiring surgical intervention, necessitating prompt specialist care and strict surveillance. OBJECTIVE: The impact of AD on the prognosis of patients with glaucoma is rarely studied. This study aims to assess the risk of requiring glaucoma surgery among patients with glaucoma with and without AD. MATERIALS AND METHODS: In this retrospective cohort analysis, we assessed patients with glaucoma initially diagnosed from December 5, 2003 to December 3, 2018 using the TriNetX database, dividing them into AD and non-AD cohorts. 1:1 propensity-score matching created balanced groups for baseline traits and comorbidities. We compared the cohorts' risk and cumulative incidence of needing glaucoma surgery (minimally invasive glaucoma surgery, trabeculectomy, aqueous shunt, or transscleral cyclophotocoagulation). A subgroup analysis was also conducted for patients with severe AD. RESULTS: Out of 528,469 patients with glaucoma, 2624 were in the AD group. Among the AD group, 584 had severe AD. The AD group showed a comparable risk of requiring surgery to the non-AD group (hazard ratio: 1.03; 95% CI: 0.72, 1.47). In contrast, the severe AD group demonstrated a significantly greater risk and cumulative incidence of surgery (hazard ratio: 2.80; 95% CI: 1.37, 5.73; log-rank P = 0.003) compared with the non-AD group. CONCLUSION: Patients with glaucoma with severe AD are significantly more likely to need surgical intervention, with AD severity being a correlating factor for increased risk.

Comment on the article: The state of social science research on COVID­19.

A well-written and interesting article was published on November 21, 2021. Future relevant studies, however, may be improved by implementing (1) a framework that outlines the overall research; (2) an author-weighted scheme (AWS) that accurately quantifies the contributions of entities to articles; and (3) a more appropriate size for the nodes representing the proportional counts for each entity in social network analysis (SNA). VOSviewer was used to construct and visualize the scientometric networks and the relation-based analyses included three categories: (1) citation relations, (2) word cooccurrences, and (3) coauthorship relations. Nevertheless, the counts for each topical entity have not been consistently integrated. As a result, the nodes of the keyword co-occurrence network are large when compared to the number of connections between the entities or terms (i.e., the total number of relationships between co-occurring terms or entities). Additionally, all weighted counts in keywords (or the total link strength of a country/region) should equal the total number of documents (e.g., n = 9954 in that article). This would lead to biases in the calculation of publications (or citations) for entities, as is common in traditional SNA. This node illustrates a study framework and a couple of AWSs (i.e., equal and nonequal AWSs) to improve the article, and discusses the need to understand the requirement that the total centrality degree in SNA equals the total number of documents (or citations).

Real-World Efficacy and Safety of Tafluprost in Primary Open-Angle Glaucoma Patients with Corneal Disorders: A Taiwan Experience.

Purpose: To investigate the efficacy and safety of switching to 0.0015% tafluprost ophthalmic solution with reduced benzalkonium chloride (BAK) on primary open-angle glaucoma (POAG) patients with corneal disorders under 0.005% latanoprost treatment. Material and Methods. This was a single-arm, open-label, switching study on adult POAG patients treated with latanoprost 0.005% for more than 3 months, with corneal disorders but no dry eye therapy. All patients were switched to tafluprost 0.0015% and followed up for 3 months. The primary outcome was the change in fluorescein staining score (National Eye Institute/Industry [NEI] score) at the end of the study. Secondary outcomes included changes in intraocular pressure (IOP), tear break-up time (TBUT), hyperemia score, and other ocular and nonocular adverse events. Results: Of the 20 patients initially enrolled, 17 patients, all with POAG, completed the study. At the end of the study, the mean NEI score significantly decreased by 1.8 ± 2.2 (p < 0.01). No significant changes in IOP were observed (12.8 ± 4.6 mmHg at baseline vs. 12.3 ± 4.0 mmHg on visit 2; p=0.470). TBUT increased by 1.2 ± 1.7 seconds (p < 0.05). The proportions of patients with no sign of hyperemia on the bulbar and palpebral conjunctiva increased from 58.5% to 64.7% at baseline (before switching to tafluprost treatment) to 94.1% and 94.1%, respectively, after switching to tafluprost treatment. Dry eye sensation scores were significantly reduced (p < 0.05), while other ocular symptom scores did not change significantly. Conclusion: Switching to tafluprost 0.0015% significantly improved fluorescein staining score, TBUT, and conjunctival hyperemia while maintaining IOP control among POAG patients with corneal disorders.

Comparison of prediction accuracies between mathematical models to make projections of confirmed cases during the COVID-19 pandamic by country/region.

BACKGROUND: The COVID-19 pandemic caused >0.228 billion infected cases as of September 18, 2021, implying an exponential growth for infection worldwide. Many mathematical models have been proposed to predict the future cumulative number of infected cases (CNICs). Nevertheless, none compared their prediction accuracies in models. In this work, we compared mathematical models recently published in scholarly journals and designed online dashboards that present actual information about COVID-19. METHODS: All CNICs were downloaded from GitHub. Comparison of model R2 was made in 3 models based on quadratic equation (QE), modified QE (OE-m), and item response theory (IRT) using paired-t test and analysis of variance (ANOVA). The Kano diagram was applied to display the association and the difference in model R2 on a dashboard. RESULTS: We observed that the correlation coefficient was 0.48 (t = 9.87, n = 265) between QE and IRT models based on R2 when modeling CNICs in a short run (dated from January 1 to February 16, 2021). A significant difference in R2 was found (P < .001, F = 53.32) in mean R2 of 0.98, 0.92, and 0.84 for IRT, OE-mm, and QE, respectively. The IRT-based COVID-19 model is superior to the counterparts of QE-m and QE in model R2 particularly in a longer period of infected days (i.e., in the entire year in 2020). CONCLUSION: An online dashboard was demonstrated to display the association and difference in prediction accuracy among predictive models. The IRT mathematical model was recommended to make projections about the evolution of CNICs for each county/region in future applications, not just limited to the COVID-19 epidemic.

Using the absolute advantage coefficient (AAC) to measure the strength of damage hit by COVID-19 in India on a growth-share matrix.

BACKGROUND: The COVID-19 pandemic occurred and rapidly spread around the world. Some online dashboards have included essential features on a world map. However, only transforming data into visualizations for countries/regions is insufficient for the public need. This study aims to (1) develop an algorithm for classifying countries/regions into four quadrants inn GSM and (2) design an app for a better understanding of the COVID-19 situation. METHODS: We downloaded COVID-19 outbreak numbers daily from the Github website, including 189 countries/regions. A four-quadrant diagram was applied to present the classification of each country/region using Google Maps run on dashboards. A novel presentation scheme was used to identify the most struck entities by observing (1) the multiply infection rate (MIR) and (2) the growth trend in the recent 7 days. Four clusters of the COVID-19 outbreak were dynamically classified. An app based on a dashboard aimed at public understanding of the outbreak types and visualizing of the COVID-19 pandemic with Google Maps run on dashboards. The absolute advantage coefficient (AAC) was used to measure the damage hit by COVID-19 referred to the next two countries severely hit by COVID-19. RESULTS: We found that the two hypotheses were supported: India (i) is in the increasing status as of April 28, 2021; (ii) has a substantially higher ACC(= 0.81 > 0.70), and (iii) has a substantially higher ACC(= 0.66 < 0.70) as of May 17, 2021. CONCLUSION: Four clusters of the COVID-19 outbreak were dynamically classified online on an app making the public understand the outbreak types of COVID-19 pandemic shown on dashboards. The app with GSM and AAC is recommended for researchers in other disease outbreaks, not just limited to COVID-19.