Aggressive Onset of a Progressive FEVR Phenotype in a Child With Novel Mutations in LRP5 and TSPAN12.

Purpose: To describe a patient with familial exudative vitreoretinopathy (FEVR) and the treatment course. Methods: A case was evaluated. Results: A 3-year-old boy presented with severe onset of FEVR, with a subhyaloid hemorrhage in 1 eye and tractional retinal detachment (TRD) in the fellow eye. Aggressive treatment with retinal photocoagulation and repeated injections of intravitreal bevacizumab resulted in stability of the retinal disease. Lens-sparing vitrectomy was performed for the TRD. The treatment effect was durable, and the patient retained useful vision in the better eye at 19 years of age. A subsequent genetic analysis showed 2 novel heterozygous missense mutations in LRP5 and TSPAN12. Conclusions: The presence of 2 novel mutations associated with severe FEVR identified in our patient is in agreement with in vitro studies showing that a more severe reduction in Norrin/ß-catenin signal activity occurs with the combination of 2 mutations.

Use of an Artificial Intelligence-Generated Vascular Severity Score Improved Plus Disease Diagnosis in Retinopathy of Prematurity.

PURPOSE: To evaluate whether providing clinicians with an artificial intelligence (AI)-based vascular severity score (VSS) improves consistency in the diagnosis of plus disease in retinopathy of prematurity (ROP). DESIGN: Multireader diagnostic accuracy imaging study. PARTICIPANTS: Eleven ROP experts, 9 of whom had been in practice for 10 years or more. METHODS: RetCam (Natus Medical Incorporated) fundus images were obtained from premature infants during routine ROP screening as part of the Imaging and Informatics in ROP study between January 2012 and July 2020. From all available examinations, a subset of 150 eye examinations from 110 infants were selected for grading. An AI-based VSS was assigned to each set of images using the i-ROP DL system (Siloam Vision). The clinicians were asked to diagnose plus disease for each examination and to assign an estimated VSS (range, 1-9) at baseline, and then again 1 month later with AI-based VSS assistance. A reference standard diagnosis (RSD) was assigned to each eye examination from the Imaging and Informatics in ROP study based on 3 masked expert labels and the ophthalmoscopic diagnosis. MAIN OUTCOME MEASURES: Mean linearly weighted κ value for plus disease diagnosis compared with RSD. Area under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AUPR) for labels 1 through 9 compared with RSD for plus disease. RESULTS: Expert agreement improved significantly, from substantial (κ value, 0.69 [0.59, 0.75]) to near perfect (κ value, 0.81 [0.71, 0.86]), when AI-based VSS was integrated. Additionally, a significant improvement in plus disease discrimination was achieved as measured by mean AUC (from 0.94 [95% confidence interval (CI), 0.92-0.96] to 0.98 [95% CI, 0.96-0.99]; difference, 0.04 [95% CI, 0.01-0.06]) and AUPR (from 0.86 [95% CI, 0.81-0.90] to 0.95 [95% CI, 0.91-0.97]; difference, 0.09 [95% CI, 0.03-0.14]). CONCLUSIONS: Providing ROP clinicians with an AI-based measurement of vascular severity in ROP was associated with both improved plus disease diagnosis and improved continuous severity labeling as compared with an RSD for plus disease. If implemented in practice, AI-based VSS could reduce interobserver variability and could standardize treatment for infants with ROP. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Eye Care in Federally Qualified Health Centers.

PURPOSE: To assess changes in vision care availability at Federally Qualified Health Centers (FQHCs) between 2017 and 2021 and whether neighborhood-level demographic social risk factors (SRFs) associated with eye care services provided by FQHCs. DESIGN: Secondary data analysis of the Health Resources and Services Administration (HRSA) data and 2017-2021 American Community Survey (ACS). PARTICIPANTS: Federally Qualified Health Centers. METHODS: Patient and neighborhood characteristics for SRFs were summarized. Differences in FQHCs providing and not providing vision care were compared via Wilcoxon-Mann-Whitney tests for continuous measures and chi-square tests for categorical measures. Logistic regression models were used to test the associations between neighborhood measures and FQHCs providing vision care, adjusted for patient characteristics. MAIN OUTCOME MEASURES: Odds ratios (ORs) with 95% confidence intervals (CIs) for neighborhood-level predictors of FQHCs providing vision care services. RESULTS: Overall, 28.5% of FQHCs (n = 375/1318) provided vision care in 2017 versus 32% (n = 435/1362) in 2021 with some increases and decreases in both the number of FQHCs and those with and without vision services. Only 2.6% of people who accessed FQHC services received eye care in 2021. Among the 435 FQHCs that provided vision care in 2021, 27.1% (n = 118) had added vision services between 2017 and 2021, 71.5% (n = 311) had been offering vision services since at least 2017, and 1.4% (n = 6) were newly established. FQHCs providing vision care in 2021 were more likely to be in neighborhoods with a higher percentage of Hispanic/Latino individuals (OR, 1.08, 95% CI, 1.02-1.14, P = 0.0094), Medicaid-insured individuals (OR, 1.08, 95% CI, 1.02-1.14, P = 0.0120), and no car households (OR, 1.07, 95% CI, 1.01-1.13, P = 0.0142). However, FQHCs with vision care, compared to FQHCs without vision care, served a lower percentage of Hispanic/Latino individuals (27.2% vs. 33.9%, P = 0.0007), Medicaid-insured patients (42.8% vs. 46.8%, P < 0.0001), and patients living at or below 100% of the federal poverty line (61.3% vs. 66.3%, P < 0.0001). CONCLUSIONS: Vision care services are available at a few FQHCs, localized to a few states. Expanding eye care access at FQHCs would meet patients where they seek care to mitigate vision loss to underserved communities. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

P Score: A Reference Image-Based Clinical Grading Scale for Vascular Change in Retinopathy of Prematurity.

PURPOSE: The International Classification of Retinopathy of Prematurity, Third Edition (ICROP3), acknowledged that plus-like retinopathy of prematurity (ROP) vascular changes occurs along a spectrum. Historically, clinician-experts demonstrate variable agreement for plus diagnosis. We developed a 9-photograph reference image set for grading plus-like changes and compared intergrader agreement of the set with standard grading with no plus, preplus, and plus disease. DESIGN: Retinal photographic grading and expert consensus opinion. PARTICIPANTS: The development set included 34 international ICROP3 committee members. The validation set included 30 ophthalmologists with ROP expertise (15 ICROP3 committee members and 15 non-ICROP3 members) METHODS: Nine ROP fundus images (P1 through P9) representing increasing degrees of zone I vascular tortuosity and dilation, based on the 34 ICROP3 committee members' gradings and consensus image reviews, were used to establish standard photographs for the plus (P) score. Study participants graded 150 fundus photographs 2 ways, separated by a 1-week washout period: (1) no plus, preplus, or plus disease and (2) choosing the closest P score image. MAIN OUTCOME MEASURES: Intergrader agreement measured by intraclass correlation coefficient. RESULTS: Intergrader agreement was higher using the P score (intraclass correlation coefficient, 0.75; 95% confidence interval, 0.71-0.79) than no plus, preplus, or plus disease (intraclass correlation coefficient, 0.67; 95% confidence interval, 0.62-0.72). Mean ± standard deviation P scores for images with mode gradings of no plus, preplus, and plus disease were 2.5 ± 0.7, 4.8 ± 0.8, and 7.4 ± 0.8, respectively. CONCLUSIONS: Intergrader agreement of plus-like vascular change in ROP using the P score is high. We now incorporate this 9-image reference set into ICROP3 for use in clinician daily practice alongside zone, stage, and plus assessment. P score is not yet meant to replace plus diagnosis for treatment decisions, but its use at our institutions has permitted better comparison between examinations for progression and regression, communication between examiners, and documentation of vascular change without fundus imaging. P score also could provide more detailed ROP classification for clinical trials, consistent with the spectrum of plus-like change that is now formally part of the International Classification of Retinopathy of Prematurity. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Childhood vision impairment and blindness in West Africa: public health measures and implications for systemic health.

Childhood blindness is an issue of global health impact, affecting approximately 2 million children worldwide. Vision 2020 and the United Nations Sustainable Development Goals previously identified childhood blindness as a key issue in the twentieth century, and while public health measures are underway, the precise etiologies and management require ongoing investigation and care, particularly within resource-limited settings such as sub-Saharan Africa. We systematically reviewed the literature on childhood blindness in West Africa to identify the anatomic classification and etiologies, particularly those causes of childhood blindness with systemic health implications. Treatable causes included cataract, refractive error, and corneal disease. Systemic etiologies identified included measles, rubella, vitamin A deficiency, and Ebola virus disease. While prior public health measures including vitamin A supplementation and vaccination programs have been deployed in most countries with reported data, multiple studies reported preventable or reversible etiologies of blindness and vision impairment. Ongoing research is necessary to standardize reporting for anatomies and/or etiologies of childhood blindness to determine the necessity of further development and implementation of public health measures that would ameliorate childhood blindness and vision impairment.

Multinational External Validation of Autonomous Retinopathy of Prematurity Screening.

Importance: Retinopathy of prematurity (ROP) is a leading cause of blindness in children, with significant disparities in outcomes between high-income and low-income countries, due in part to insufficient access to ROP screening. Objective: To evaluate how well autonomous artificial intelligence (AI)-based ROP screening can detect more-than-mild ROP (mtmROP) and type 1 ROP. Design, Setting, and Participants: This diagnostic study evaluated the performance of an AI algorithm, trained and calibrated using 2530 examinations from 843 infants in the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) study, on 2 external datasets (6245 examinations from 1545 infants in the Stanford University Network for Diagnosis of ROP [SUNDROP] and 5635 examinations from 2699 infants in the Aravind Eye Care Systems [AECS] telemedicine programs). Data were taken from 11 and 48 neonatal care units in the US and India, respectively. Data were collected from January 2012 to July 2021, and data were analyzed from July to December 2023. Exposures: An imaging processing pipeline was created using deep learning to autonomously identify mtmROP and type 1 ROP in eye examinations performed via telemedicine. Main Outcomes and Measures: The area under the receiver operating characteristics curve (AUROC) as well as sensitivity and specificity for detection of mtmROP and type 1 ROP at the eye examination and patient levels. Results: The prevalence of mtmROP and type 1 ROP were 5.9% (91 of 1545) and 1.2% (18 of 1545), respectively, in the SUNDROP dataset and 6.2% (168 of 2699) and 2.5% (68 of 2699) in the AECS dataset. Examination-level AUROCs for mtmROP and type 1 ROP were 0.896 and 0.985, respectively, in the SUNDROP dataset and 0.920 and 0.982 in the AECS dataset. At the cross-sectional examination level, mtmROP detection had high sensitivity (SUNDROP: mtmROP, 83.5%; 95% CI, 76.6-87.7; type 1 ROP, 82.2%; 95% CI, 81.2-83.1; AECS: mtmROP, 80.8%; 95% CI, 76.2-84.9; type 1 ROP, 87.8%; 95% CI, 86.8-88.7). At the patient level, all infants who developed type 1 ROP screened positive (SUNDROP: 100%; 95% CI, 81.4-100; AECS: 100%; 95% CI, 94.7-100) prior to diagnosis. Conclusions and Relevance: Where and when ROP telemedicine programs can be implemented, autonomous ROP screening may be an effective force multiplier for secondary prevention of ROP.

Genome-wide association identifies novel ROP risk loci in a multiethnic cohort.

We conducted a genome-wide association study (GWAS) in a multiethnic cohort of 920 at-risk infants for retinopathy of prematurity (ROP), a major cause of childhood blindness, identifying 1 locus at genome-wide significance level (p < 5×10-8) and 9 with significance of p < 5×10-6 for ROP ≥ stage 3. The most significant locus, rs2058019, reached genome-wide significance within the full multiethnic cohort (p = 4.96×10-9); Hispanic and European Ancestry infants driving the association. The lead single nucleotide polymorphism (SNP) falls in an intronic region within the Glioma-associated oncogene family zinc finger 3 (GLI3) gene. Relevance for GLI3 and other top-associated genes to human ocular disease was substantiated through in-silico extension analyses, genetic risk score analysis and expression profiling in human donor eye tissues. Thus, we identify a novel locus at GLI3 with relevance to retinal biology, supporting genetic susceptibilities for ROP risk with possible variability by race and ethnicity.

Oxygenation Fluctuations Associated with Severe Retinopathy of Prematurity: Insights from a Multimodal Deep Learning Approach.

Purpose: Retinopathy of prematurity (ROP) is one of the leading causes of blindness in children. Although the role of oxygen in the pathophysiology of ROP is well established, a precise understanding of the dynamic relationship between oxygen exposure ROP incidence and severity is lacking. The purpose of this study was to evaluate the correlation between time-dependent oxygen variables and the onset of ROP. Design: Retrospective cohort study. Participants: Two hundred thirty infants who were born at a single academic center and met the inclusion criteria were included. Infants are mainly born between January 2011 and October 2022. Methods: Patient data were extracted from electronic health records (EHRs), with sufficient time-dependent oxygen data. Clinical outcomes for ROP were recorded as none/mild or moderate/severe (defined as type II or worse). Mixed-effects linear models were used to compare the 2 groups in terms of dynamic oxygen variables, such as daily average and the coefficient of variation (COV) fraction of inspired oxygen (FiO2). Support vector machine (SVM) and long-short-term memory (LSTM)-based multimodal models were trained with fivefold cross-validation to predict which infants would develop moderate/severe ROP. Gestational age (GA), birth weight, and time-dependent oxygen variables were used to develop predictive models. Main Outcome Measures: Model cross-validation performance was evaluated by computing the mean area under the receiver operating characteristic (AUROC) curve, precision, recall, and F1 score. Results: We found that both daily average and COV of FiO2 were associated with more severe ROP (adjusted P < 0.001). With fivefold cross-validation, the multimodal LSTM models had higher performance than the best static models (SVM using GA and 3 average FiO2 features) and SVM models trained on GA alone (mean AUROC = 0.89 ± 0.04 vs. 0.86 ± 0.05 vs. 0.83 ± 0.04). Conclusions: The development of severe ROP might not only be influenced by oxygen exposure but also by its fluctuation, which provides direction for future study of pathophysiological factors associated with severe ROP development. Additionally, we demonstrated that multimodal neural networks can be a method to extract useful information from time-series data, which may be a valuable methodology for the investigation of other diseases using EHR data. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.