Ophthalmology science最新文献

{"title":"Association of Tirzepatide versus Glucagon-Like Peptide-1 Receptor Agonists with Incident Glaucoma in Patients with Type 2 Diabetes: A Retrospective Cohort Study","authors":"Ssu-Yu Pan MD,&nbsp;Chien-Hsiang Weng MD, MPH,&nbsp;Yi-Jing Sheen MD, PhD,&nbsp;Jun-Peng Chen MS,&nbsp;Hui-Ju Lin MD, PhD,&nbsp;I-Jong Wang MD, PhD,&nbsp;Chien-Chih Chou MD, PhD","doi":"10.1016/j.xops.2026.101245","DOIUrl":"10.1016/j.xops.2026.101245","url":null,"abstract":"<div><h3>Objective</h3><div>To assess the comparative risk of glaucoma among patients with type 2 diabetes mellitus (T2DM) treated with tirzepatide versus glucagon-like peptide-1 receptor agonists (GLP-1RAs).</div></div><div><h3>Design</h3><div>A retrospective cohort study using data from a large, multinational, deidentified database.</div></div><div><h3>Participants</h3><div>Adults aged ≥60 years with T2DM who were newly prescribed tirzepatide or GLP-1RAs during 2022–2024 across 21 countries were included. Individuals with a prior diagnosis of glaucoma, previous use of glaucoma medications, or cross-use of tirzepatide and GLP-1RAs during follow-up were excluded.</div></div><div><h3>Methods</h3><div>Based on medication exposure, participants were divided into the tirzepatide and GLP-1RA groups. The 2 groups were balanced using 1:1 propensity score matching based on age, sex, race, smoking history, glycated hemoglobin, body mass index, prior use of glucose-lowering medications, corticosteroids, and comorbidities.</div></div><div><h3>Main Outcome Measures</h3><div>Incident primary open-angle glaucoma (POAG) across a 2-year follow-up period.</div></div><div><h3>Results</h3><div>A total of 51 540 participants were included in the final analysis. Tirzepatide, compared to GLP-1RAs, was associated with a lower risk of POAG after 2 years (hazard ratio, 0.65; 95% confidence interval, 0.44–0.96) of follow-up. This association remained consistent across multiple sensitivity analyses, including the exclusion of individuals with early POAG events after the index date, restricting the cohort to patients with ≥1 subsequent prescription during follow-up, an intention-to-treat design, and limiting the analysis exclusively to individuals with an ophthalmology visit.</div></div><div><h3>Conclusions</h3><div>Tirzepatide was associated with a lower risk of POAG compared to GLP-1RAs; however, further studies are needed to confirm its clinical significance given the limitations of the current study.</div></div><div><h3>Financial Disclosures</h3><div>The author has no/the authors have no proprietary or commercial interest in any materials discussed in this article.</div></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"6 7","pages":"Article 101245"},"PeriodicalIF":5.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13314882/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148363899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning–Based Prediction of Long-Term Intraocular Pressure Fluctuations in Open-Angle Glaucoma","authors":"Colya N. Englisch MD,&nbsp;André M. Trouvain MD,&nbsp;Philip Wakili MD,&nbsp;Moritz J. Wolf MSc,&nbsp;H. Burkhard Dick MD,&nbsp;Kaweh Mansouri MD,&nbsp;Esther M. Hoffmann MD,&nbsp;Marc J. Mackert MD,&nbsp;Achim Langenbucher PhD,&nbsp;Jan Alexandersson PhD,&nbsp;Karl T. Boden MD,&nbsp;Peter Szurman MD","doi":"10.1016/j.xops.2026.101240","DOIUrl":"10.1016/j.xops.2026.101240","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Objective&lt;/h3&gt;&lt;div&gt;To investigate the predictability of long-term intraocular pressure (IOP) fluctuations in open-angle glaucoma eyes implanted with a telemetric IOP sensor.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Design&lt;/h3&gt;&lt;div&gt;A prospective, open-label, single-arm, multicenter study.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Subjects&lt;/h3&gt;&lt;div&gt;Twenty-four patients were enrolled, including 20 with primary open-angle glaucoma, 2 with pseudoexfoliative, 1 with pigmentary, and 1 with uveitic glaucoma. Mean age was 65.2 ± 10.2 years.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;Telemetric IOP measurements were aggregated into nyctohemeral means. The first 90 postoperative days were excluded. A rolling reference framework was applied, in which temporally paired observations were generated by comparing each eligible day with a future time point at fixed prediction horizons. The relationship between short-term (7, 14, or 28 days) and long-term (273 or 364 days) fluctuations was assessed using Pearson correlation. Multivariate regression was applied to predict long-term fluctuations based on short-term data. In addition, supervised machine learning with a Random Forest Classifier was used to predict long-term fluctuations from clinical, demographic, and IOP-derived features. For each horizon (273 or 364 days) and threshold (+2.0, +3.0, and +4.0 mmHg), changes in mean nyctohemeral IOP were calculated. Outcomes were labeled as “1” if the increase met or exceeded the threshold and “0” otherwise.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Main Outcome Measures&lt;/h3&gt;&lt;div&gt;Predictability of long-term IOP fluctuations at 273 and 364 days.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;Short-term fluctuations correlated only weakly with long-term variability (Pearson &lt;em&gt;r&lt;/em&gt; ≤ 0.33) and explained at most 15.2% in regression analysis. Across 1224 Random Forest Classifier models, 47 met inclusion criteria of area under the receiver operating characteristic curve (AUROC) &gt;0.8 and sensitivity and specificity &gt;0.7 (27 for 364 days, 20 for 273 days). On average, 5563 ± 116 valid pairings from 9.2 ± 0.8 patients were used per configuration. Five final configurations were selected for each threshold–horizon combination based on the highest F1 values. Performance metrics included AUROC 0.81 to 0.86, cross-validated AUROC 0.78 to 0.83, accuracy 0.72 to 0.81, sensitivity 0.72 to 0.78, specificity 0.70 to 0.82, precision 0.32 to 0.44, and F1 value 0.44 to 0.56. All models included 7, 14, and 28-day fluctuations, mean nyctohemeral IOP, ocular pulse amplitude, age, body mass index, and central corneal thickness as predictors, with mean nyctohemeral IOP contributing most (38%–55%).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;div&gt;Long-term IOP fluctuations can be predicted from baseline clinical and demographic data combined with IOP-related features. Telemetric devices and remote IOP monitoring, combined with predictive modeling, could reduce the burden of time-intensive procedures and health care costs while supporting individualized care in the f","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"6 7","pages":"Article 101240"},"PeriodicalIF":5.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13284456/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148310647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sleep Pattern and Structural Damage in Older Adults with Glaucoma: A United States and China Study","authors":"Zhonghua Luo MD,&nbsp;Zihao Lin MD, PhD,&nbsp;Yuefeng Yu Mmed,&nbsp;Yangyang Du Mmed,&nbsp;Yue Xu MD, PhD,&nbsp;Yu Yang MMed,&nbsp;Shengsong Huang MD,&nbsp;Ying Han MD, PhD,&nbsp;Jingjing Huang MD, PhD","doi":"10.1016/j.xops.2026.101237","DOIUrl":"10.1016/j.xops.2026.101237","url":null,"abstract":"<div><h3>Purpose</h3><div>To investigate sleep patterns in older adults with glaucoma and evaluate their associations with glaucomatous damage and emotional disturbances.</div></div><div><h3>Design</h3><div>A cross-sectional observational study.</div></div><div><h3>Participants</h3><div>Adults aged ≥60 years from the National Health and Nutrition Examination Survey (NHANES) 2005-2008 in the United States (n = 3317) and the Elderly Glaucoma and Sleep Study (EGSS) in China (n = 200).</div></div><div><h3>Methods</h3><div>Participants in NHANES and EGSS completed standardized sleep questionnaires and underwent ophthalmic examinations, while wearable derived-sleep measures and urinary melatonin metabolite assays were additionally performed in the EGSS cohort. In EGSS, multivariable linear regression models were used to examine associations of the Pittsburgh Sleep Quality Index (PSQI) global score with demographic, clinical, and ocular factors.</div></div><div><h3>Main Outcome Measures</h3><div>Overall sleep disorder in NHANES and PSQI global score in EGSS.</div></div><div><h3>Results</h3><div>In NHANES, older adults with glaucoma reported more frequent sleep disturbances than controls, showing higher odds of sleep disorders and difficulty initiating sleep, as well as longer sleep latency (all <em>P</em> &lt; 0.05). In EGSS, patients with primary open-angle glaucoma (POAG) showed poorer sleep quality, higher device-estimated light sleep time, and a lower 6-sulfatoxymelatonin/creatinine ratio than controls (all <em>P</em> &lt; 0.05), while no significant differences were found for primary angle-closure glaucoma. In multivariable analyses, higher 9-item Patient Health Questionnaire scores and thinner peripapillary retinal nerve fiber layer thickness were independently associated with higher PSQI scores in POAG (all <em>P</em> &lt; 0.05).</div></div><div><h3>Conclusions</h3><div>Older adults with glaucoma had poorer sleep quality. Participants with POAG showed poorer subjective sleep quality, a longer wearable-derived light sleep duration, and lower urinary melatonin metabolite levels. In POAG, poorer sleep quality was significantly associated with more severe glaucomatous structural damage and a greater depressive symptom burden.</div></div><div><h3>Financial Disclosure(s)</h3><div>The authors have no proprietary or commercial interest in any materials discussed in this article.</div></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"6 7","pages":"Article 101237"},"PeriodicalIF":5.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13284440/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148310677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pathways to Patchy Atrophy in High Myopia: Precursor Patterns, Structural Characteristics, and Long-Term Outcomes","authors":"Sebastiano Del Fabbro MD,&nbsp;Jost B. Jonas MD,&nbsp;Songhomitra Panda-Jonas MD,&nbsp;Karolina Motloch MD,&nbsp;Maria Vittoria Cicinelli MD,&nbsp;Maurizio Battaglia Parodi MD,&nbsp;Elisabetta Miserocchi MD,&nbsp;Aude Couturier MD, PhD,&nbsp;Elise Philippakis MD","doi":"10.1016/j.xops.2026.101244","DOIUrl":"10.1016/j.xops.2026.101244","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Objective&lt;/h3&gt;&lt;div&gt;To characterize distinct precursor patterns associated with the development of patchy atrophy (PA) in highly myopic eyes and to describe their structural evolution and clinical outcomes over time.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Design&lt;/h3&gt;&lt;div&gt;A retrospective multicenter cohort study.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Participants&lt;/h3&gt;&lt;div&gt;Highly myopic patients with documented onset of PAs and at least 3 months of multimodal imaging (MMI) prior to the PA development.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;Clinical, demographic, and MMI findings were retrospectively collected from 3 tertiary referral centers. Based on MMI findings preceding PA onset, eyes were categorized into 3 patterns: (1) inflammatory PAs in the context of punctate inner choroidopathy; (2) neovascular PAs, associated with extrafoveal macular neovascularization (MNV); and (3) idiopathic PAs, in the absence of identifiable inflammatory or neovascular precursor lesions.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Main Outcome Measures&lt;/h3&gt;&lt;div&gt;Precursor patterns, clinical and MMI characteristics, clinical complication profile, and atrophy growth rate (AGR).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;The study included 103 eyes (84 patients; 28 [27%] men) with a mean (standard deviation) age of 62 (13.4) years and mean refractive error of –13.2 (5.8) diopters at PA onset. Median observation periods prior to PA onset and post-PA follow-up were 2.1 years (interquartile range 5.20) and 3.44 years (interquartile range 7.23), respectively. Inflammatory PAs were detected in 33 (32%) eyes, neovascular PAs in 9 (9%) eyes, and 61 eyes (59%) had idiopathic PAs. Number of pre-existing PAs at baseline was the highest in inflammatory PAs (6.1 ± 6.4), followed by neovascular PAs (1.6 ± 1.1) and idiopathic PAs (1.5 ± 1.1; &lt;em&gt;P&lt;/em&gt; &lt; 0.001). Late-stage complications, including MNV (&lt;em&gt;P&lt;/em&gt; &lt; 0.001), subretinal fibrosis (&lt;em&gt;P&lt;/em&gt; &lt; 0.001), and macular atrophy (&lt;em&gt;P&lt;/em&gt; = 0.01), occurred more frequently in inflammatory and neovascular PA than in idiopathic PA. Exploratory tree-based analysis identified baseline lesion number as a key feature distinguishing inflammatory PAs from idiopathic PAs. Mean AGR in the overall cohort was 0.73 ± 1.06 mm&lt;sup&gt;2&lt;/sup&gt;/year (95% confidence interval, 0.52–0.94), with progression fastest in inflammatory PAs (1.3 ± 1.4 mm&lt;sup&gt;2&lt;/sup&gt;/year), followed by neovascular PAs (0.7 ± 0.7 mm&lt;sup&gt;2&lt;/sup&gt;/year) and slowest in idiopathic PAs (0.4 ± 0.7 mm&lt;sup&gt;2&lt;/sup&gt;/year; &lt;em&gt;P&lt;/em&gt; &lt; 0.001). Higher baseline lesion number was associated with faster AGR (&lt;em&gt;P&lt;/em&gt; = 0.005).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;div&gt;Patchy atrophies in highly myopic eyes develop with 3 precursor patterns (inflammatory, neovascular, and idiopathic) and differ in structural characteristics, atrophy progression, and complication profile. Recognition of PA patterns may improve clinical assessment of highly myopic eyes.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Financial Disclosure(s)&lt;/h3&gt;&lt;div&gt;The authors have no proprietary or c","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"6 7","pages":"Article 101244"},"PeriodicalIF":5.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13284479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148310628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multisource Machine Learning Model for Detecting Referral-Warranted Retinopathy of Prematurity","authors":"Xinwei Luo PhD,&nbsp;Yong Chen PhD,&nbsp;Bowen Ying MS,&nbsp;Graham E. Quinn MD, MSCE,&nbsp;Gil Binenbaum MD, MSCE,&nbsp;Gui-Shuang Ying PhD,&nbsp;Lifang He PhD","doi":"10.1016/j.xops.2026.101242","DOIUrl":"10.1016/j.xops.2026.101242","url":null,"abstract":"<div><h3>Purpose</h3><div>To develop a multisource machine learning model for detecting referral-warranted retinopathy of prematurity (RW-ROP) using retinal images and demographics.</div></div><div><h3>Design</h3><div>Secondary analysis of data from the Telemedicine Approaches to Evaluating Acute-Phase Retinopathy of Prematurity Study.</div></div><div><h3>Subjects</h3><div>One thousand two hundred fifty-seven premature infants (mean birth weight 864 g; mean gestational age 27 weeks; 19.4% with RW-ROP) enrolled from 12 clinical centers in North America.</div></div><div><h3>Methods</h3><div>A multisource ROPNet (MS-ROPNet) model that combines a VGG-Swin Transformer model to extract features from retinal images and a random forest model that captures patterns of demographics was developed using central-view retinal images from 7741 eye visits with concurrent clinical eye examinations and demographic characteristics (birth weight, gestational age, sex, ethnicity, and age at retinal image). The MS-ROPnet model was compared to several existing machine learning models for detecting RW-ROP.</div></div><div><h3>Main Outcome Measures</h3><div>Model performance metrics including the area under the receiver operating characteristic curve (AUROC), area under the precision–recall curve (AUPRC), sensitivity, specificity, and accuracy from fivefold cross-validation, with RW-ROP diagnosed by certified ophthalmologists as the reference standard.</div></div><div><h3>Results</h3><div>The MS-ROPNet achieved an AUROC of 95.0 ± 0.7% (mean ± standard deviation), AUPRC of 78.0 ± 2.8%, sensitivity of 81.5 ± 4.0%, specificity of 94.3 ± 1.4%, and accuracy of 93.0 ± 0.9% under the default cutoff of 0.50 in predicted probability. After adjusting the cutoff to achieve higher sensitivity, MS-ROPNet had 90% sensitivity and specificity of 84.8 ± 2.5% (using cutoff 0.3504), and 95% sensitivity with specificity of 72.8 ± 6.9% (using cutoff 0.2074), which outperformed both existing multisource models and the best single-source model by ≥3.7% in specificity at the same sensitivity.</div></div><div><h3>Conclusions</h3><div>The MS-ROPNet achieved high performance in RW-ROP classification by effectively integrating retinal images with demographics, demonstrating its potential for accurate risk stratification of RW-ROP.</div></div><div><h3>Financial Disclosures</h3><div>The authors have no proprietary or commercial interest in any materials discussed in this article.</div></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"6 7","pages":"Article 101242"},"PeriodicalIF":5.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13314750/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative Analysis of Choroidal Thickness and Blood Flow in Thyroid-Associated Ophthalmopathy Using Ultra-Widefield Swept-Source OCT Angiography","authors":"Guodong Liu MD,&nbsp;Kunhua Jiang MD,&nbsp;Mingyang Wang MD,&nbsp;Tianyu Zheng MD, PhD","doi":"10.1016/j.xops.2026.101236","DOIUrl":"10.1016/j.xops.2026.101236","url":null,"abstract":"<div><h3>Objective</h3><div>To quantitatively analyze choroidal thickness and blood flow in thyroid-associated ophthalmopathy (TAO) using ultra-widefield swept-source OCT angiography (UWF SS-OCTA), and to characterize differences among dysthyroid optic neuropathy (DON), non-DON, and normal eyes.</div></div><div><h3>Design</h3><div>Retrospective cross-sectional study.</div></div><div><h3>Subjects</h3><div>The study included 65 eyes from 35 TAO patients (15 males, 20 females; mean age 51.8 ± 12.6 years), comprising 5 mild, 40 moderate-to-severe, and 20 DON eyes. A control group of 70 eyes from 38 age- and sex-matched healthy subjects (15 males, 23 females; mean age 52.8 ± 14.9 years) was also included.</div></div><div><h3>Methods</h3><div>Ultra-widefield SS-OCTA imaging was performed to capture data sets divided into 9 subfields. Automated segmentation was used to measure structural thickness (whole choroid, large-vessel choroidal layer) and vascular density (choriocapillaris, large-vessel choroidal layer). Parameters were compared between TAO and control eyes, and subgroup analyses were conducted between DON and non-DON eyes.</div></div><div><h3>Main Outcome Measures</h3><div>Choroidal thickness and vascular density in the whole choroid, choriocapillaris, and large-vessel choroidal layer across 9 subfields.</div></div><div><h3>Results</h3><div>Thyroid-associated ophthalmopathy eyes showed significantly greater whole choroidal thickness (189.9 ± 2.6 μm vs. 182.4 ± 2.3 μm, <em>P</em> &lt; 0.001) and large-vessel choroidal layer thickness (161.4 ± 2.7 μm vs. 149.9 ± 2.3 μm, <em>P</em> &lt; 0.0001) compared with controls, particularly in central and nasal regions. Vascular density was reduced in TAO eyes for the large-vessel choroidal layer (39.86 ± 0.25% vs. 40.54 ± 0.15%, <em>P</em> = 0.014) and showed a nonsignificant decrease in the choriocapillaris (47.57 ± 3.24% vs. 47.89 ± 0.12%, <em>P</em> = 0.067). Subgroup analysis revealed that these differences were primarily driven by DON eyes, whereas non-DON TAO eyes showed no significant changes compared with controls.</div></div><div><h3>Conclusions</h3><div>Ultra-widefield SS-OCTA effectively identifies choroidal changes in TAO, revealing increased thickness accompanied by reduced vascular density. These findings suggest that choroidal thickening is primarily due to stromal edema rather than vascular engorgement, particularly in DON eyes, highlighting the role of choroidal remodeling in TAO progression.</div></div><div><h3>Financial Disclosure(s)</h3><div>Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</div></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"6 7","pages":"Article 101236"},"PeriodicalIF":5.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13314756/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Polypoidal Choroidal Vasculopathy-Related OCT Features in the TENAYA and LUCERNE Trials","authors":"Kelvin Yi Chong Teo PhD, FRCOphth,&nbsp;Philippe Margaron PhD,&nbsp;Timothy Y.Y. Lai MD, FRCOphth,&nbsp;Won Ki Lee MD, PhD,&nbsp;Srinivas R. Sadda MD, FARVO,&nbsp;Giovanni Staurenghi MD, PhD,&nbsp;Shriji Patel MD, MBA,&nbsp;Chui Ming Gemmy Cheung FRCOphth","doi":"10.1016/j.xops.2026.101217","DOIUrl":"10.1016/j.xops.2026.101217","url":null,"abstract":"<div><h3>Objective</h3><div>To describe OCT features associated with polypoidal choroidal vasculopathy (PCV) in the TENAYA and LUCERNE clinical trial cohorts.</div></div><div><h3>Design</h3><div>Imaging analysis within 2 multinational, double-masked, randomized controlled phase III trials.</div></div><div><h3>Subjects</h3><div>Participants with treatment-naïve neovascular age-related macular degeneration enrolled in TENAYA and LUCERNE with baseline OCT and in a subset, baseline indocyanine green angiography (ICGA).</div></div><div><h3>Methods</h3><div>Previously described OCT features associated with PCV were evaluated among 1329 participants. Two major OCT features were evaluated (sharp-peaked pigment epithelial detachment [SPED] and sub-retinal pigment epithelium ring-like lesion) to determine the occurrence of OCT-defined PCV. Subretinal fluid, intraretinal fluid (IRF), and hyperreflective material (HRM), were compared between OCT-defined PCV and non-PCV eyes. A sensitivity analysis was performed in the subset of eyes with available ICGA to assess concordance between OCT and ICGA-confirmed PCV.</div></div><div><h3>Main Outcome Measures</h3><div>Occurrence of OCT-defined PCV and distribution of OCT-based disease activity biomarkers by PCV status.</div></div><div><h3>Results</h3><div>Using the 2 major OCT criteria, 88 (6.7%) of eyes were classified as OCT-defined PCV. Fewer OCT-defined PCV eyes had IRF and HRM compared with non-PCV eyes (27.3% vs. 45.5%, <em>P</em> = 0.002, 19.3% vs. 47.0%, <em>P</em> &lt; 0.001). In the ICGA subset (n = 274), the combination of SPED and ring demonstrated good sensitivity (85.2%) and high specificity (98.8%) relative to ICGA.</div></div><div><h3>Conclusions</h3><div>Using 2 Asia-Pacific Ocular Imaging Society major OCT features, OCT-based diagnostic strategy may help triage or screen for PCV when ICGA is unavailable.</div></div><div><h3>Financial Disclosure(s)</h3><div>Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</div></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"6 7","pages":"Article 101217"},"PeriodicalIF":5.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13316682/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148363877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cover","authors":"","doi":"10.1016/j.xops.2026.101256","DOIUrl":"10.1016/j.xops.2026.101256","url":null,"abstract":"","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"6 6","pages":"Article 101256"},"PeriodicalIF":5.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cover","authors":"","doi":"10.1016/j.xops.2026.101209","DOIUrl":"10.1016/j.xops.2026.101209","url":null,"abstract":"","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"6 5","pages":"Article 101209"},"PeriodicalIF":4.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147952219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cover","authors":"","doi":"10.1016/j.xops.2026.101175","DOIUrl":"10.1016/j.xops.2026.101175","url":null,"abstract":"","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"6 4","pages":"Article 101175"},"PeriodicalIF":4.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147702780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}