Ophthalmology science最新文献

{"title":"Barriers to Extracting and Harmonizing Glaucoma Testing Data: Gaps, Shortcomings, and the Pursuit of FAIRness","authors":"","doi":"10.1016/j.xops.2024.100621","DOIUrl":"10.1016/j.xops.2024.100621","url":null,"abstract":"","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142416753","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":"Severity Scale of Diabetic Macular Ischemia Based on the Distribution of Capillary Nonperfusion in OCT Angiography","authors":"","doi":"10.1016/j.xops.2024.100603","DOIUrl":"10.1016/j.xops.2024.100603","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate the severity scales of diabetic macular ischemia (DMI) by analyzing the quantity and distribution of capillary nonperfusion using OCT angiography (OCTA) images.</div></div><div><h3>Design</h3><div>A single-center, prospective case series.</div></div><div><h3>Participants</h3><div>Three hundred one eyes from 301 patients with diabetic retinopathy.</div></div><div><h3>Methods</h3><div>We acquired 3 × 3-mm swept-source OCTA images and created en face images within a central 2.5-mm circle. The circle was divided into 15 × 15-pixel squares and nonperfusion squares (NPSs) were defined as those without retinal vessels. Eyes with high-dimensional spatial data were arranged on a 2-dimensional space using the uniform manifold approximation and projection (UMAP) algorithm and classified by clustering into 5 groups: <em>Initial</em>, <em>Mild</em>, <em>Superficial</em>, <em>Moderate</em>, and <em>Severe</em>.</div></div><div><h3>Main Outcome Measures</h3><div>Development of a severity scale for DMI.</div></div><div><h3>Results</h3><div>Eyes arranged on a 2-dimensional UMAP space were divided into 5 clusters, based on the similarity of nonperfusion area distribution. Nonperfusion square counts in the deep layer increased in eyes of the <em>Initial</em>, <em>Mild</em>, <em>Moderate</em>, and <em>Severe</em> groups in a stepwise manner. In contrast, there were no significant changes in superficial NPS counts between eyes of the <em>Initial</em> and <em>Mild</em> groups. In the intermediate stage, eyes of the <em>Superficial</em> group exhibited higher NPS counts in the central sector of the superficial layer compared with those of the <em>Moderate</em> group. The foveal avascular zone extended into the temporal subfield of the deep layer in eyes of the <em>Moderate</em> group. Eyes of the <em>Severe</em> group had significantly poorer visual acuity that was more frequently accompanied with proliferative diabetic retinopathy.</div></div><div><h3>Conclusions</h3><div>The application of dimensionality reduction and clustering has facilitated the development of a novel severity scale for DMI based on the distribution of capillary nonperfusion in OCTA images.</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":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666914524001398/pdfft?md5=9fd86bd124fe988794885bc8b18f64b7&pid=1-s2.0-S2666914524001398-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314355","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/S2666-9145(24)00142-8","DOIUrl":"10.1016/S2666-9145(24)00142-8","url":null,"abstract":"","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666914524001428/pdfft?md5=95d97aa46c724c2dba85eaa459927652&pid=1-s2.0-S2666914524001428-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142240448","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":"Development and Validation of a Deep Learning Algorithm for Differentiation of Choroidal Nevi from Small Melanoma in Fundus Photographs","authors":"","doi":"10.1016/j.xops.2024.100613","DOIUrl":"10.1016/j.xops.2024.100613","url":null,"abstract":"<div><h3>Purpose</h3><div>To develop and validate a deep learning algorithm capable of differentiating small choroidal melanomas from nevi.</div></div><div><h3>Design</h3><div>Retrospective multicenter cohort study.</div></div><div><h3>Participants</h3><div>A total of 802 images from 688 patients diagnosed with choroidal nevi or melanoma.</div></div><div><h3>Methods</h3><div>Wide field and standard field fundus photographs were collected from patients diagnosed with choroidal nevi or melanoma by ocular oncologists during clinical examinations. A lesion was classified as a nevus if it was followed for at least 5 years without being rediagnosed as melanoma. A neural network optimized for image classification was trained and validated on cohorts of 495 and 168 images and subsequently tested on independent sets of 86 and 53 images.</div></div><div><h3>Main Outcome Measures</h3><div>Area under the curve (AUC) in receiver operating characteristic analysis for differentiating small choroidal melanomas from nevi.</div></div><div><h3>Results</h3><div>The algorithm achieved an AUC of 0.88 in both test cohorts, outperforming ophthalmologists using the Mushroom shape, Orange pigment, Large size, Enlargement, and Subretinal fluid (AUC 0.77) and To Find Small Ocular Melanoma Using Helpful Hints Daily (AUC 0.67) risk factors (DeLong’s test, <em>P</em> &lt; 0.001). The algorithm performed equally well for wide field and standard field photos (AUC 0.89 for both when analyzed separately). Using an optimal operating point of 0.63 (on a scale from 0.00 to 1.00) determined from the training and validation datasets, the algorithm achieved 100% sensitivity and 74% specificity in the first test cohort (F-score 0.72), and 80% sensitivity and 81% specificity in the second (F-score 0.71), which consisted of images from external clinics nationwide. It outperformed 12 ophthalmologists in sensitivity (Mann–Whitney <em>U</em>, <em>P</em> = 0.006) but not specificity (<em>P</em> = 0.54). The algorithm showed higher sensitivity than both resident and consultant ophthalmologists (Dunn's test, <em>P</em> = 0.04 and <em>P</em> = 0.006, respectively) but not ocular oncologists (<em>P</em> &gt; 0.99, all <em>P</em> values Bonferroni corrected).</div></div><div><h3>Conclusions</h3><div>This study develops and validates a deep learning algorithm for differentiating small choroidal melanomas from nevi, matching or surpassing the discriminatory performance of experienced human ophthalmologists. Further research will aim to validate its utility in clinical settings.</div></div><div><h3>Financial Disclosure(s)</h3><div>Financial DisclosuresProprietary 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":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142423211","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":"Extent of Complete Retinal Pigment Epithelial and Outer Retinal Atrophy with Foveal Center Involvement is Associated with Visual Acuity","authors":"","doi":"10.1016/j.xops.2024.100612","DOIUrl":"10.1016/j.xops.2024.100612","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate the OCT images of eyes with fovea-involved complete retinal pigment epithelial and outer retinal atrophy (cRORA) as well as best-corrected visual acuity (BCVA) to explore the pathogenesis of visual impairment and atrophy.</div></div><div><h3>Design</h3><div>Retrospective observational study.</div></div><div><h3>Subjects</h3><div>Data of eyes with cRORA associated with age-related macular degeneration with foveal center involvement were collected from 10 hospitals in Japan.</div></div><div><h3>Methods</h3><div>Ophthalmic examination data, BCVA, and extents of retinal pigment epithelial and outer retinal atrophy (RORA), represented by choroidal hyper-transmission, and outer plexiform layer (OPL) deterioration, central retinal thickness (CRT), and central choroidal thickness (CCT) measured using built-in software on the sectional OCT images were evaluated.</div></div><div><h3>Main Outcome Measures</h3><div>Relationship between BCVA and extents of RORA and OPL deterioration.</div></div><div><h3>Results</h3><div>Of the 64 eyes of 64 patients (mean age: 76.8 ± 9.5 years old), 38 eyes (59.4%) belonged to men. Mean BCVA was 0.602 ± 0.475 (median: 0.523; range, −0.079 to 1.523) in logarithm of the minimum angle of resolution (logMAR). Mean extent of RORA was 2921 ± 1291 (median: 3172; range: 479–5985) μm. BCVA in logMAR positively correlated with extents of RORA (<em>P</em> = 0.004) and OPL deterioration (<em>P</em> = 0.004) and negatively correlated with CRT (<em>P</em> = 0.022). Best-corrected visual acuity ≥0.5 was associated with extents of RORA ≥3000 μm (odds ratio [OR], 4.227; 95% confidence interval [CI], 1.440–12.408; <em>P</em> = 0.009) and OPL deterioration ≥1700 μm (OR, 2.984; 95% CI, 1.034–8.609; <em>P</em> = 0.043), and presence of complete central outer plexiform layer defect (cCOD) (OR, 12.700; 95% CI, 2.439–66.132; <em>P</em> = 0.003), after adjusting for age and sex. The extent of RORA ≥3000 μm was associated with BCVA ≥0.5 (OR, 4.213; 95% CI, 1.437–12.356; <em>P</em> = 0.009), extent of OPL deterioration ≥1700 μm (OR, 58.682; 95% CI, 6.865–501.592; <em>P</em> &lt; 0.001), and presence of cCOD (OR, 4.107; 95% CI, 1.339–12.604; <em>P</em> = 0.014), after adjusting for age and sex. The extent of RORA positively correlated with that of OPL deterioration (<em>P</em> &lt; 0.001), CRT (<em>P</em> = 0.001), and CCT (<em>P</em> = 0.041).</div></div><div><h3>Conclusions</h3><div>A longer extent of cRORA in the OCT images with foveal center involvement was associated with a longer extent of OPL deterioration and the presence of cCOD and worse BCVA. Further studies focusing on OPL changes are warranted for understanding the pathogenesis of RORA and vision loss.</div></div><div><h3>Financial Disclosures</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":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142423204","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":"Association of Lipopolysaccharide-Type Endotoxins with Retinal Neurodegeneration: The Alienor Study","authors":"","doi":"10.1016/j.xops.2024.100610","DOIUrl":"10.1016/j.xops.2024.100610","url":null,"abstract":"<div><h3>Purpose</h3><div>Lipopolysaccharide (LPS)-type endotoxins are naturally found in the gut microbiota and there is emerging evidence linking gut microbiota and neuroinflammation leading to retinal neurodegeneration. Thinning of the retinal nerve fiber layer (RNFL) is a biomarker of retinal neurodegeneration, and a hallmark of glaucoma, the second leading cause of blindness worldwide. We assessed the association of a blood biomarker of LPS with peripapillary RNFL thickness (RNFLT) and its longitudinal evolution up to 11 years.</div></div><div><h3>Design</h3><div>The Alienor study is a single center prospective population-based cohort study.</div></div><div><h3>Subjects</h3><div>The studied sample of this study includes 1062 eyes of 548 participants receiving ≥1 gradable RNFL measurement.</div></div><div><h3>Methods</h3><div>Plasma esterified 3-hydroxy fatty acids (3-OH FAs) were measured as a proxy of LPS burden. Retinal nerve fiber layer thickness was acquired using spectral-domain OCT imaging every 2 years from 2009 to 2020 (up to 5 visits).</div></div><div><h3>Main Outcome Measures</h3><div>Associations of plasma esterified 3-OH FAs with RNFLT were assessed using linear mixed models.</div></div><div><h3>Results</h3><div>Mean age of the included 548 participants was 82.4 ± 4.3 years and 62.6% were women. Higher plasma esterified 3-OH FAs was significantly associated with thinner RNFLT at baseline (coefficient beta = −1.42 microns for 1 standard deviation-increase in 3-OH FAs, 95% confidence interval [−2.56; −0.28], <em>P</em> = 0.02). This association remained stable after multivariate adjustment for potential confounders. No statistically significant association was found between 3-OH FAs and longitudinal RNFLT change.</div></div><div><h3>Conclusions</h3><div>Higher plasma esterified 3-OH FAs were associated with thinner RNFLT at baseline, indicating an involvement of LPS in the early processes of optic nerve neurodegeneration and highlighting the potential importance of the human microbiota in preserving retinal health.</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":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666914524001465/pdfft?md5=d87b134146a791f80cb8b2ca7c30456e&pid=1-s2.0-S2666914524001465-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314354","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":"Analysis of ChatGPT Responses to Ophthalmic Cases: Can ChatGPT Think like an Ophthalmologist?","authors":"","doi":"10.1016/j.xops.2024.100600","DOIUrl":"10.1016/j.xops.2024.100600","url":null,"abstract":"<div><h3>Objective</h3><p>Large language models such as ChatGPT have demonstrated significant potential in question-answering within ophthalmology, but there is a paucity of literature evaluating its ability to generate clinical assessments and discussions. The objectives of this study were to (1) assess the accuracy of assessment and plans generated by ChatGPT and (2) evaluate ophthalmologists’ abilities to distinguish between responses generated by clinicians versus ChatGPT.</p></div><div><h3>Design</h3><p>Cross-sectional mixed-methods study.</p></div><div><h3>Subjects</h3><p>Sixteen ophthalmologists from a single academic center, of which 10 were board-eligible and 6 were board-certified, were recruited to participate in this study.</p></div><div><h3>Methods</h3><p>Prompt engineering was used to ensure ChatGPT output discussions in the style of the ophthalmologist author of the Medical College of Wisconsin Ophthalmic Case Studies. Cases where ChatGPT accurately identified the primary diagnoses were included and then paired. Masked human-generated and ChatGPT-generated discussions were sent to participating ophthalmologists to identify the author of the discussions. Response confidence was assessed using a 5-point Likert scale score, and subjective feedback was manually reviewed.</p></div><div><h3>Main Outcome Measures</h3><p>Accuracy of ophthalmologist identification of discussion author, as well as subjective perceptions of human-generated versus ChatGPT-generated discussions.</p></div><div><h3>Results</h3><p>Overall, ChatGPT correctly identified the primary diagnosis in 15 of 17 (88.2%) cases. Two cases were excluded from the paired comparison due to hallucinations or fabrications of nonuser-provided data. Ophthalmologists correctly identified the author in 77.9% ± 26.6% of the 13 included cases, with a mean Likert scale confidence rating of 3.6 ± 1.0. No significant differences in performance or confidence were found between board-certified and board-eligible ophthalmologists. Subjectively, ophthalmologists found that discussions written by ChatGPT tended to have more generic responses, irrelevant information, hallucinated more frequently, and had distinct syntactic patterns (all <em>P</em> &lt; 0.01).</p></div><div><h3>Conclusions</h3><p>Large language models have the potential to synthesize clinical data and generate ophthalmic discussions. While these findings have exciting implications for artificial intelligence-assisted health care delivery, more rigorous real-world evaluation of these models is necessary before clinical deployment.</p></div><div><h3>Financial Disclosures</h3><p>The author(s) have no proprietary or commercial interest in any materials discussed in this article.</p></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666914524001362/pdfft?md5=1fc56cec0e121016c01c38686515b525&pid=1-s2.0-S2666914524001362-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142270456","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":"ChatGPT-Assisted Classification of Postoperative Bleeding Following Microinvasive Glaucoma Surgery Using Electronic Health Record Data","authors":"","doi":"10.1016/j.xops.2024.100602","DOIUrl":"10.1016/j.xops.2024.100602","url":null,"abstract":"<div><h3>Purpose</h3><p>To evaluate the performance of a large language model (LLM) in classifying electronic health record (EHR) text, and to use this classification to evaluate the type and resolution of hemorrhagic events (HEs) after microinvasive glaucoma surgery (MIGS).</p></div><div><h3>Design</h3><p>Retrospective cohort study.</p></div><div><h3>Participants</h3><p>Eyes from the Bascom Palmer Glaucoma Repository.</p></div><div><h3>Methods</h3><p>Eyes that underwent MIGS between July 1, 2014 and February 1, 2022 were analyzed. Chat Generative Pre-trained Transformer (ChatGPT) was used to classify deidentified EHR anterior chamber examination text into HE categories (no hyphema, microhyphema, clot, and hyphema). Agreement between classifications by ChatGPT and a glaucoma specialist was evaluated using Cohen’s Kappa and precision-recall (PR) curve. Time to resolution of HEs was assessed using Cox proportional-hazards models. Goniotomy HE resolution was evaluated by degree of angle treatment (90°–179°, 180°–269°, 270°–360°). Logistic regression was used to identify HE risk factors.</p></div><div><h3>Main Outcome Measures</h3><p>Accuracy of ChatGPT HE classification and incidence and resolution of HEs.</p></div><div><h3>Results</h3><p>The study included 434 goniotomy eyes (368 patients) and 528 Schlemm’s canal stent (SCS) eyes (390 patients). Chat Generative Pre-trained Transformer facilitated excellent HE classification (Cohen’s kappa 0.93, area under PR curve 0.968). Using ChatGPT classifications, at postoperative day 1, HEs occurred in 67.8% of goniotomy and 25.2% of SCS eyes (<em>P</em> &lt; 0.001). The 270° to 360° goniotomy group had the highest HE rate (84.0%, <em>P</em> &lt; 0.001). At postoperative week 1, HEs were observed in 43.4% and 11.3% of goniotomy and SCS eyes, respectively (<em>P</em> &lt; 0.001). By postoperative month 1, HE rates were 13.3% and 1.3% among goniotomy and SCS eyes, respectively (<em>P</em> &lt; 0.001). Time to HE resolution differed between the goniotomy angle groups (log-rank <em>P</em> = 0.034); median time to resolution was 10, 10, and 15 days for the 90° to 179°, 180° to 269°, and 270° to 360° groups, respectively. Risk factor analysis demonstrated greater goniotomy angle was the only significant predictor of HEs (odds ratio for 270°–360°: 4.08, <em>P</em> &lt; 0.001).</p></div><div><h3>Conclusions</h3><p>Large language models can be effectively used to classify longitudinal EHR free-text examination data with high accuracy, highlighting a promising direction for future LLM-assisted research and clinical decision support. Hemorrhagic events are relatively common self-resolving complications that occur more often in goniotomy cases and with larger goniotomy treatments. Time to HE resolution differs significantly between goniotomy groups.</p></div><div><h3>Financial Disclosure(s)</h3><p>Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</p></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666914524001386/pdfft?md5=6a7c056392e56a9af8bd6168c9dd77cb&pid=1-s2.0-S2666914524001386-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142270369","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":"Visualization of Scleral Flap Patency in Glaucoma Filtering Blebs Using OCT","authors":"","doi":"10.1016/j.xops.2024.100604","DOIUrl":"10.1016/j.xops.2024.100604","url":null,"abstract":"<div><h3>Purpose</h3><div>To investigate the use of anterior-segment OCT (AS-OCT) to visualize the aqueous outflow pathway and patency of the scleral flap in glaucoma filtration surgery blebs.</div></div><div><h3>Design</h3><div>Cross-sectional study.</div></div><div><h3>Subjects</h3><div>Two hundred five filtering blebs of 112 patients with glaucoma who had undergone trabeculectomy (Trab, n = 97) or deep sclerectomy (DS, n = 108) surgery with/without mitomycin-C (MMC).</div></div><div><h3>Methods</h3><div>Swept-source AS-OCT raster slices were used to image the Trab and DS blebs in sagittal and coronal planes using a standardized protocol. Bleb appearances were classified into 4 categories based on the scleral flap and sclerostomy/trabeculo-descemet window (TDW) appearance: A—sclerostomy/TDW not visible; B—sclerostomy/TDW visible but scleral flap indiscriminate from sclera; C—scleral flap distinct but edges adherent to surrounding sclera; D—scleral flap edges non adherent to surrounding sclera.</div></div><div><h3>Main Outcome Measures</h3><div>Surgical outcomes were classified into complete success (CS) (intraocular pressure [IOP] ≤18 mmHg with no medications), qualified success (QS) (IOP ≤18 with medications), and failure (F) (IOP &gt;18 mmHg).</div></div><div><h3>Results</h3><div>The proportions of CS, QS, and F in the Trab and DS cohorts were 45.0% and 29.6%, 33.0% and 31.5%, 22.0% and 38.9% respectively, with a median postoperative follow-up of 8.4 years (standard deviation 7.9; interquartile range 3.2–9.0). In QS and failed blebs, category C (Trab, 53.7%; DS, 52.5%) accounted for the majority of scleral flap appearances, followed by categories A and B. Category D (86.0%; 71.9%) accounted for the majority of appearances in Trab and DS blebs with CS. There was a significantly greater proportion of MMC use in categories C and D compared with categories A and B in both Trab (<em>P</em> &lt; 0.0001) and DS (<em>P</em> = 0.02) cohorts, demonstrating the association of intraoperative MMC use with increased patency of the scleral flap.</div></div><div><h3>Conclusions</h3><div>Swept-source AS-OCT may be used to visualize the position and patency of the sclerostomy/TDW and scleral flap in relation to surrounding structures in both sagittal and coronal planes. Although free scleral flap edges are primarily correlated with MMC use, it may also correlate with surgical success. Anterior-segment OCT may be used to complement subjective bleb grading at the slit lamp in the assessment of filtering blebs.</div></div><div><h3>Financial Disclosure(s)</h3><div>The author(s) have no proprietary or commercial interest in any materials discussed in this article.</div></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142423214","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":"Sources of Discrepancy between Retinal Nerve Fiber Layer and Bruch’s Membrane Opening-Minimum Rim Width Thickness in Eyes with Glaucoma","authors":"","doi":"10.1016/j.xops.2024.100601","DOIUrl":"10.1016/j.xops.2024.100601","url":null,"abstract":"<div><h3>Purpose</h3><div>To compare the discrepancies between circumpapillary retinal nerve fiber layer (RNFL) and Bruch’s membrane opening-minimum rim width (BMO-MRW) thickness in glaucoma eyes.</div></div><div><h3>Design</h3><div>A cross-sectional observational study.</div></div><div><h3>Subjects</h3><div>One hundred eighty-six eyes (118 patients) with glaucoma.</div></div><div><h3>Methods</h3><div>OCT optic nerve head volume scans of patients enrolled in the Advanced Glaucoma Progression Study at the final available visit were exported. The RNFL and BMO-MRW measurements were averaged into corresponding 7.5° sectors, and the nasal sector data were excluded from analyses. A 2-stage screening process was used to identify true mismatches between the RNFL and BMO-MRW measurements, in which either the RNFL or BMO-MRW value was in the less than first percentile range while its counterpart was in the greater than first percentile range on the temporal-superior-nasal-inferior-temporal curve. The prevalence of these mismatches was mapped, and corresponding images were reviewed to determine the underlying cause of these discrepancies.</div></div><div><h3>Main Outcome Measures</h3><div>Proportion of mismatches between RNFL and BMO-MRW, location of mismatches between RNFL and BMO-MRW, anatomical causes of mismatches between RNFL and BMO-MRW.</div></div><div><h3>Results</h3><div>Mismatch analysis revealed true mismatches between RNFL and BMO-MRW in 7.7% of sectors. High BMO-MRW with low corresponding RNFL mismatches were most frequently located at the 45° and 322.5° sectors, whereas high RNFL with corresponding low BMO-MRW mismatches peaked at the 75° sector. Large blood vessels accounted for 90.9% of high RNFL with low BMO-MRW mismatches. Small to large blood vessels accounted for 62.9% of high BMO-MRW with low RNFL mismatches; the remaining mismatches could be attributed to retinoschisis or inclusion of outer retinal layers in BMO-MRW measurements.</div></div><div><h3>Conclusions</h3><div>Although overall agreement between RNFL and BMO-MRW measurements is good in areas with advanced damage, blood vessels and other anatomical factors can cause discrepancies between the 2 types of structural measurements and need to be considered when evaluating the utility of such measurements for detection of change.</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":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142423212","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}