Evaluating the Relationship between Neighborhood-Level Social Vulnerability and Patient Adherence to Ophthalmology Appointments.

PURPOSE: To examine the association between neighborhood-level social vulnerability and adherence to scheduled ophthalmology appointments. METHODS: In this retrospective cohort study, records of all patients ≥18 years scheduled for an ophthalmology appointment between September 12, 2020, and February 8, 2021, were reviewed. Primary exposure is neighborhood-level Social Vulnerability Index (SVI) based on the patient's residential location. SVI is a rank score of 15 social factors into four themes (socioeconomic status, household composition/disability, minority status/language, and housing type/transportation), ranging from 0 to 1.0, with higher ranks indicating greater social vulnerability. The overall SVI score and each theme were analyzed separately as the primary exposure of interest in multivariable logistic regression models that controlled for age, sex, appointment status (new or established), race, and distance from clinic. The primary outcome, non-adherence, was defined as missing more than 25% of scheduled appointments. RESULTS: Of 8,322 patients (41% non-Hispanic Black, 24% Hispanic, 22% non-Hispanic White) with scheduled appointments, 28% were non-adherent. Non-adherence was associated with greater social vulnerability (adjusted odds ratio [aOR] per 0.01 increase in overall SVI = 2.46 [95% confidence interval, 1.99, 3.06]) and each SVI theme (socioeconomic status: aOR = 2.38 [1.94, 2.91]; household composition/disability: aOR = = 1.51 [1.26, 1.81]; minority status/language: aOR = 2.03 [1.55, 2.68]; housing type/transportation: aOR = 1.41 [1.16, 1.73]). CONCLUSION: Neighborhood-level social vulnerability is associated with greater risk of non-adherence to scheduled ophthalmology appointments, controlling for individual characteristics. Multi-level intervention strategies that incorporate neighborhood-level vulnerabilities are needed to reduce disparities in access to ophthalmology care.

An Association Between Large Optic Cupping and Total and Regional Brain Volume: The Women's Health Initiative.

PURPOSE: To investigate the relationships between optic nerve cupping and total and regional brain volumes. DESIGN: Secondary analysis of randomized clinical trial data. METHODS: Women 65 to 79 years of age without glaucoma with cup-to-disc ratio (CDR) measurements from the Women's Health Initiative (WHI) Sight Examination study and magnetic resonance imaging (MRI)-based total and regional brain volumes from the WHI Memory Study MRI-1 were included. Large CDR was defined as 0.6 or greater in either eye. Generalized estimating equation models were used to account for intra-brain correlations between the right and left sides. The final analysis was adjusted for demographic and clinical characteristics and for total brain volume (for regional analyses). RESULTS: Final analyses included 471 women, with the mean age ± SD was 69.2 ± 3.6 years; 92.8% of the subjects were white. Of 471 women, 34 (7.2%) had large CDR. Controlling for total brain volume and for demographic and clinical characteristics, lateral ventricle volume was 3.01 cc larger for subjects with large CDR compared to those without large CDR (95% CI = 0.02 to 5.99; P = .048). Furthermore, frontal lobe volume was 4.78 cc lower for subjects with large CDR compared to those without (95% CI = -8.71, -0.84; P = 0.02), and occipital lobe volume was 1.86 cc lower for those with large CDR compared to those without (95% CI = -3.39, -0.3; P =.02). CONCLUSIONS: Our analysis suggests that in women aged 65 years or more, large CDR is associated with lower relative total brain volume and absolute regional volume in the frontal and occipital lobes. Enlarged CDR in individuals without glaucoma may represent a sign of optic nerve and brain aging, although more longitudinal data are needed.

Association between myopia progression and quantity of laser treatment for retinopathy of prematurity.

BACKGROUND: Previous studies found that infants with retinopathy of prematurity (ROP) who were treated for more posterior disease with a greater number of laser spots developed higher myopia. These studies included multiple physicians with variations in laser density. In treatments by a single physician, laser spot count is a better surrogate for area of avascular retina and anterior-posterior location of disease, so that the relationship with myopia can be better assessed. METHODS: Our retrospective study included infants treated with laser for ROP by a single surgeon at a single center. Exclusion criteria were irregularities during laser and additional treatment for ROP. We assessed correlation between laser spot count and change in refractive error over time using a linear mixed effects model. RESULTS: We studied 153 eyes from 78 subjects treated with laser for ROP. The average gestational age at birth was 25.3±1.8 weeks, birth weight 737±248 grams, laser spot count 1793±728, and post-treatment follow up 37±29 months. Between corrected ages 0-1 years, the mean spherical equivalent was +0.4±2.3 diopters; between ages 1-2, it was -1.3±3.2D; and ages 2-3 was -0.8±3.1D. Eyes that received more laser spots had significantly greater change in refractive error over time (0.30D more myopia per year per 1000 spots). None of the eyes with hyperopia before 18 months developed myopia during the follow-up period. CONCLUSIONS: Greater myopia developed over time in infants with ROP treated by laser to a larger area of avascular retina.

Association between cognitive function and large optic nerve cupping, accounting for cup-disc-ratio genetic risk score.

PURPOSE: To investigate if accounting for a cup-to-disc ratio (CDR) genetic risk score (GRS) modified the association between large CDR and cognitive function among women. DESIGN: This was a retrospective study using data from the Women's Health Initiative. METHODS: Patients with glaucoma or ocular hypertension were excluded. Large CDR was defined as ≥ 0.6 in either eye. Cognitive function was measured by the Modified Mini-Mental State Examination (3MSE). We used the combined effects from 13 single nucleotide polymorphisms (SNPs) to formulate the GRS for CDR. We used logistic regression to investigate associations between weighted GRS and large CDR, then a linear regression to assess the association between weighted GRS and 3MSE scores, and between weighted GRS, CDR, and 3MSE scores, adjusted for demographic and clinical characteristics. RESULTS: Final analyses included 1,196 White women with mean age of 69.60 ± 3.62 years and 7.27% with large CDR. Mean GRS in women with and without large CDR was 1.51 ± 0.31 vs. 1.41 ± 0.36, respectively (p = 0.004). The odds of large CDR for a one unit increase in GRS was 2.30 (95% CI: (1.22, 4.36), p = 0.011). Adding the CDR GRS in the model with CDR and 3MSE, women with large CDR still had statistically significantly lower 3MSE scores than those without large CDR, yielding a predicted mean difference in 3MSE scores of 0.84 (p = 0.007). CONCLUSIONS: Independent of the CDR GRS, women with large CDR had a lower cognitive function.

Optometrist's perspectives of Artificial Intelligence in eye care.

PURPOSE: The application of artificial intelligence (AI) in diagnosing and managing ocular disease has gained popularity as research highlights the utilization of AI to improve personalized medicine and healthcare outcomes. The objective of this study is to describe current optometric perspectives of AI in eye care. METHODS: Members of the American Academy of Optometry were sent an electronic invitation to complete a 17-item survey. Survey items assessed perceived advantages and concerns regarding AI using a 5-point Likert scale ranging from "strongly agree" to "strongly disagree." RESULTS: A total of 400 optometrists completed the survey. The mean number of years since optometry school completion was 25 ± 15.1. Most respondents reported familiarity with AI (66.8%). Though half of optometrists had concerns about the diagnostic accuracy of AI (53.0%), most believed it would improve the practice of optometry (72.0%). Optometrists reported their willingness to incorporate AI into practice increased from 53.3% before the COVID-19 pandemic to 65.5% after onset of the pandemic (p<0.001). CONCLUSION: In this study, optometrists are optimistic about the use of AI in eye care, and willingness to incorporate AI in clinical practice also increased after the onset of the COVID-19 pandemic.

External Validation of a Retinopathy of Prematurity Screening Model Using Artificial Intelligence in 3 Low- and Middle-Income Populations.

Importance: Retinopathy of prematurity (ROP) is a leading cause of preventable blindness that disproportionately affects children born in low- and middle-income countries (LMICs). In-person and telemedical screening examinations can reduce this risk but are challenging to implement in LMICs owing to the multitude of at-risk infants and lack of trained ophthalmologists. Objective: To implement an ROP risk model using retinal images from a single baseline examination to identify infants who will develop treatment-requiring (TR)-ROP in LMIC telemedicine programs. Design, Setting, and Participants: In this diagnostic study conducted from February 1, 2019, to June 30, 2021, retinal fundus images were collected from infants as part of an Indian ROP telemedicine screening program. An artificial intelligence (AI)-derived vascular severity score (VSS) was obtained from images from the first examination after 30 weeks' postmenstrual age. Using 5-fold cross-validation, logistic regression models were trained on 2 variables (gestational age and VSS) for prediction of TR-ROP. The model was externally validated on test data sets from India, Nepal, and Mongolia. Data were analyzed from October 20, 2021, to April 20, 2022. Main Outcomes and Measures: Primary outcome measures included sensitivity, specificity, positive predictive value, and negative predictive value for predictions of future occurrences of TR-ROP; the number of weeks before clinical diagnosis when a prediction was made; and the potential reduction in number of examinations required. Results: A total of 3760 infants (median [IQR] postmenstrual age, 37 [5] weeks; 1950 male infants [51.9%]) were included in the study. The diagnostic model had a sensitivity and specificity, respectively, for each of the data sets as follows: India, 100.0% (95% CI, 87.2%-100.0%) and 63.3% (95% CI, 59.7%-66.8%); Nepal, 100.0% (95% CI, 54.1%-100.0%) and 77.8% (95% CI, 72.9%-82.2%); and Mongolia, 100.0% (95% CI, 93.3%-100.0%) and 45.8% (95% CI, 39.7%-52.1%). With the AI model, infants with TR-ROP were identified a median (IQR) of 2.0 (0-11) weeks before TR-ROP diagnosis in India, 0.5 (0-2.0) weeks before TR-ROP diagnosis in Nepal, and 0 (0-5.0) weeks before TR-ROP diagnosis in Mongolia. If low-risk infants were never screened again, the population could be effectively screened with 45.0% (India, 664/1476), 38.4% (Nepal, 151/393), and 51.3% (Mongolia, 266/519) fewer examinations required. Conclusions and Relevance: Results of this diagnostic study suggest that there were 2 advantages to implementation of this risk model: (1) the number of examinations for low-risk infants could be reduced without missing cases of TR-ROP, and (2) high-risk infants could be identified and closely monitored before development of TR-ROP.

Assessment of a novel ophthalmology tele-triage system during the COVID-19 pandemic.

BACKGROUND: In response to the COVID-19 pandemic, a web-based tele-triage system was created to prioritize in-person clinic visits and ensure safety at the University of Illinois at Chicago Department of Ophthalmology and Visual Sciences during a statewide shelter-in-place order. The aim of this study is to evaluate the impact of the tele-triage system on urgent visit volume and explore the characteristics of acute visit requests at a tertiary referral eye center. METHODS: This retrospective study analyzed acute visit requests between April 6, 2020 and June 6, 2020. Descriptive statistics, chi-square tests, ANOVA, and bivariate logistic regression were used to compare variables with a p-value of 0.05. RESULTS: Three hundred fifty-eight surveys were completed. Mean age was 49.7 ± 18.8 years (range 2-91). The majority of requests were determined as urgent (63.0%) or emergent (0.8%). Forty-nine patients had recent eye trauma (13.7%), and the most common reported symptoms were new onset eye pain (25.7%) and photophobia (22.9%). Most patients were self-referred (63.7%), though provider referral was more common in patients with symptoms of new onset lid swelling (p < 0.01), diplopia (p < 0.01), flashing lights (p = 0.02), or droopy eyelid (p < 0.01). Patients presenting with symptom onset within 48 h tended to be younger (45.8 years) versus those with symptom duration of 48 h to 1 week (49.6 years), or more than 1 week (52.6 years; p < 0.01). CONCLUSION: This novel tele-triage system screened out one-third of acute visit requests as non-urgent, which limited in-person visits during the initial shelter-in-place period of the pandemic. Tele-triage systems should be implemented in eye care practices for future emergency preparedness.

Imaging and artificial intelligence for progression of age-related macular degeneration.

Age-related macular degeneration (AMD) is a leading cause of severe vision loss. With our aging population, it may affect 288 million people globally by the year 2040. AMD progresses from an early and intermediate dry form to an advanced one, which manifests as choroidal neovascularization and geographic atrophy. Conversion to AMD-related exudation is known as progression to neovascular AMD, and presence of geographic atrophy is known as progression to advanced dry AMD. AMD progression predictions could enable timely monitoring, earlier detection and treatment, improving vision outcomes. Machine learning approaches, a subset of artificial intelligence applications, applied on imaging data are showing promising results in predicting progression. Extracted biomarkers, specifically from optical coherence tomography scans, are informative in predicting progression events. The purpose of this mini review is to provide an overview about current machine learning applications in artificial intelligence for predicting AMD progression, and describe the various methods, data-input types, and imaging modalities used to identify high-risk patients. With advances in computational capabilities, artificial intelligence applications are likely to transform patient care and management in AMD. External validation studies that improve generalizability to populations and devices, as well as evaluating systems in real-world clinical settings are needed to improve the clinical translations of artificial intelligence AMD applications.

Probabilistic Forecasting of Anti-VEGF Treatment Frequency in Neovascular Age-Related Macular Degeneration.

Purpose: To probabilistically forecast needed anti-vascular endothelial growth factor (anti-VEGF) treatment frequency using volumetric spectral domain-optical coherence tomography (SD-OCT) biomarkers in neovascular age-related macular degeneration from real-world settings. Methods: SD-OCT volume scans were segmented with a custom deep-learning-based analysis pipeline. Retinal thickness and reflectivity values were extracted for the central and the four inner Early Treatment Diabetic Retinopathy Study (ETDRS) subfields for six retinal layers (inner retina, outer nuclear layer, inner segments [IS], outer segments [OS], retinal pigment epithelium-drusen complex [RPEDC] and the choroid). Machine-learning models were probed to predict the anti-VEGF treatment frequency within the next 12 months. Probabilistic forecasting was performed using natural gradient boosting (NGBoost), which outputs a full probability distribution. The mean absolute error (MAE) between the predicted versus actual anti-VEGF treatment frequency was the primary outcome measure. Results: In a total of 138 visits of 99 eyes with neovascular AMD (96 patients) from two clinical centers, the prediction of future anti-VEGF treatment frequency was observed with an accuracy (MAE [95% confidence interval]) of 2.60 injections/year [2.25-2.96] (R2 = 0.390) using random forest regression and 2.66 injections/year [2.31-3.01] (R2 = 0.094) using NGBoost, respectively. Prediction intervals were well calibrated and reflected the true uncertainty of NGBoost-based predictions. Standard deviation of RPEDC-thickness in the central ETDRS-subfield constituted an important predictor across models. Conclusions: The proposed, fully automated pipeline enables probabilistic forecasting of future anti-VEGF treatment frequency in real-world settings. Translational Relevance: Prediction of a probability distribution allows the physician to inspect the underlying uncertainty. Predictive uncertainty estimates are essential to highlight cases where human-inspection and/or reversion to a fallback alternative is warranted.

Evaluation of pediatric ophthalmologists' perspectives of artificial intelligence in ophthalmology.

PURPOSE: To survey pediatric ophthalmologists on their perspectives of artificial intelligence (AI) in ophthalmology. METHODS: This is a subgroup analysis of a study previously reported. In March 2019, members of the American Association for Pediatric Ophthalmology and Strabismus (AAPOS) were recruited via the online AAPOS discussion board to voluntarily complete a Web-based survey consisting of 15 items. Survey items assessed the extent participants "agreed" or "disagreed" with statements on the perceived benefits and concerns of AI in ophthalmology. Responses were analyzed using descriptive statistics. RESULTS: A total of 80 pediatric ophthalmologists who are members of AAPOS completed the survey. The mean number of years since graduating residency was 21 years (range, 0-46). Overall, 91% (73/80) reported understanding the concept of AI, 70% (56/80) believed AI will improve the practice of ophthalmology, 68% (54/80) reported willingness to incorporate AI into their clinical practice, 65% (52/80) did not believe AI will replace physicians, and 71% (57/80) believed AI should be incorporated into medical school and residency curricula. However, 15% (12/80) were concerned that AI will replace physicians, 26% (21/80) believed AI will harm the patient-physician relationship, and 46% (37/80) reported concern over the diagnostic accuracy of AI. CONCLUSIONS: Most pediatric ophthalmologists in this survey viewed the role of AI in ophthalmology positively.

Prediction of age-related macular degeneration disease using a sequential deep learning approach on longitudinal SD-OCT imaging biomarkers.

We propose a hybrid sequential prediction model called "Deep Sequence", integrating radiomics-engineered imaging features, demographic, and visual factors, with a recursive neural network (RNN) model in the same platform to predict the risk of exudation within a future time-frame in non-exudative AMD eyes. The proposed model provides scores associated with risk of exudation in the short term (within 3 months) and long term (within 21 months), handling challenges related to variability of OCT scan characteristics and the size of the training cohort. We used a retrospective clinical trial dataset that includes 671 AMD fellow eyes with 13,954 observations before any signs of exudation for training and validation in a tenfold cross validation setting. Deep Sequence achieved high performance for the prediction of exudation within 3 months (0.96 ± 0.02 AUCROC) and within 21 months (0.97 ± 0.02 AUCROC) on cross-validation. Training the proposed model on this clinical trial dataset and testing it on an external real-world clinical dataset showed high performance for the prediction within 3-months (0.82 AUCROC) but a clear decrease in performance for the prediction within 21-months (0.68 AUCROC). While performance differences at longer time intervals may be derived from dataset differences, we believe that the high performance and generalizability achieved in short-term predictions may have a high clinical impact allowing for optimal patient follow-up, adding the possibility of more frequent, detailed screening and tailored treatments for those patients with imminent risk of exudation.

Progression of Photoreceptor Degeneration in Geographic Atrophy Secondary to Age-related Macular Degeneration.

Importance: Sensitive outcome measures for disease progression are needed for treatment trials in geographic atrophy (GA) secondary to age-related macular degeneration (AMD). Objective: To quantify photoreceptor degeneration outside regions of GA in eyes with nonexudative AMD, to evaluate its association with future GA progression, and to characterize its spatio-temporal progression. Design, Setting, and Participants: Monocenter cohort study (Directional Spread in Geographic Atrophy [NCT02051998]) and analysis of data from a normative data study at a tertiary referral center. One hundred fifty-eight eyes of 89 patients with a mean (SD) age of 77.7 (7.1) years, median area of GA of 8.87 mm2 (IQR, 4.09-15.60), and median follow-up of 1.1 years (IQR, 0.52-1.7 years), as well as 93 normal eyes from 93 participants. Exposures: Longitudinal spectral-domain optical coherence tomography (SD-OCT) volume scans (121 B-scans across 30° × 25°) were segmented with a deep-learning pipeline and standardized in a pointwise manner with age-adjusted normal data (z scores). Outer nuclear layer (ONL), photoreceptor inner segment (IS), and outer segment (OS) thickness were quantified along evenly spaced contour lines surrounding GA lesions. Linear mixed models were applied to assess the association between photoreceptor-related imaging features and GA progression rates and characterize the pattern of photoreceptor degeneration over time. Main Outcomes and Measures: Association of ONL thinning with follow-up time (after adjusting for age, retinal topography [z score], and distance to the GA boundary). Results: The study included 158 eyes of 89 patients (51 women and 38 men) with a mean (SD) age of 77.7 (7.1) years. The fully automated B-scan segmentation was accurate (dice coefficient, 0.82; 95% CI, 0.80-0.85; compared with manual markings) and revealed a marked interpatient variability in photoreceptor degeneration. The ellipsoid zone (EZ) loss-to-GA boundary distance and OS thickness were prognostic for future progression rates. Outer nuclear layer and IS thinning over time was significant even when adjusting for age and proximity to the GA boundary (estimates of -0.16 µm/y; 95% CI, -0.30 to -0.02; and -0.17 µm/y; 95% CI, -0.26 to -0.09). Conclusions and Relevance: Distinct and progressive alterations of photoreceptor laminae (exceeding GA spatially) were detectable and quantifiable. The degree of photoreceptor degeneration outside of regions of retinal pigment epithelium atrophy varied markedly between eyes and was associated with future GA progression. Macula-wide photoreceptor laminae thinning represents a potential candidate end point to monitor treatment effects beyond mere GA lesion size progression.

Artificial intelligence in ophthalmology during COVID-19 and in the post COVID-19 era.

PURPOSE OF REVIEW: To highlight artificial intelligence applications in ophthalmology during the COVID-19 pandemic that can be used to: describe ocular findings and changes correlated with COVID-19; extract information from scholarly articles on SARS-CoV-2 and COVID-19 specific to ophthalmology; and implement efficient patient triage and telemedicine care. RECENT FINDINGS: Ophthalmology has been leading in artificial intelligence and technology applications. With medical imaging analysis, pixel-annotated distinguishable features on COVID-19 patients may help with noninvasive diagnosis and severity outcome predictions. Using natural language processing (NLP) and data integration methods, topic modeling on more than 200 ophthalmology-related articles on COVID-19 can summarize ocular manifestations, viral transmission, treatment strategies, and patient care and practice management. Artificial intelligence for telemedicine applications can address the high demand, prioritize and triage patients, as well as improve at home-monitoring devices and secure data transfers. SUMMARY: COVID-19 is significantly impacting the way we are delivering healthcare. Given the already successful implementation of artificial intelligence applications and telemedicine in ophthalmology, we expect that these systems will be embraced more as tools for research, education, and patient care.

Quantification of Angiogenesis and Lymphangiogenesis in the Dual ex vivo Aortic and Thoracic Duct Assay.

BACKGROUND: Lymphatic vessel formation (lymphangiogenesis) plays important roles in cancer metastasis, organ rejection, and lymphedema, but the underlying molecular events remain unclear. Furthermore, despite significant overlap in the molecular families involved in angiogenesis and lymphangiogenesis, little is known about the crosstalk between these processes. The ex vivo aortic ring assay and lymphatic ring assay have enabled detailed studies of vessel sprouting, but harvesting and imaging clear thoracic duct samples remain challenging. Here we present a modified ex vivo dual aortic ring and thoracic duct assay using tissues from dual fluorescence reporter Prox1- GFP/Flt1-DsRed (PGFD) mice, which permit simultaneous visualization of blood and lymphatic endothelial cells. OBJECTIVE: To characterize the concurrent sprouting of intrinsically fluorescent blood and lymphatic vessels from harvested aorta and thoracic duct samples. METHODS: Dual aorta and thoracic duct specimens were harvested from PGFD mice, grown in six types of endothelial cell growth media (one control, five that each lack a specific growth factor), and visualized by confocal fluorescence microscopy. Linear mixed models were used to compare the extent of vessel growth and sprouting over a 28-day period. RESULTS: Angiogenesis occurred prior to lymphangiogenesis in our assay. The control medium generally induced superior growth of both vessel types compared with the different modified media formulations. The greatest decrease in lymphangiogenesis was observed in vascular endothelial growth factor-C (VEGF-C)-devoid medium, suggesting the importance of VEGF-C in lymphangiogenesis. CONCLUSION: The modified ex vivo dual aortic ring and thoracic duct assay represents a powerful tool for studying angiogenesis and lymphangiogenesis in concert.

Application of corneal injury models in dual fluorescent reporter transgenic mice to understand the roles of the cornea and limbus in angiogenic and lymphangiogenic privilege.

The role of the corneal epithelium and limbus in corneal avascularity and pathological neovascularization (NV) is not well understood. To investigate the contributions of the corneal and limbal epithelia in angiogenic and lymphangiogenic privilege, we designed five injury models involving debridement of different portions of the cornea and limbus and applied them to the dual-fluorescence reporter Prox1-GFP/Flt1-DsRed mouse, which permits in vivo imaging of blood and lymphatic vessels via fluorescence microscopy. Debridement of the whole cornea resulted in significant hemangiogenesis (HA) and lymphangiogenesis (LA), while that of the whole limbus yielded minimal corneal HA or LA. Following hemilimbal plus whole corneal debridement, corneal NV occurred only through the non-injured aspect of the limbus. Overall, these results suggest that the integrity of the corneal epithelium is important for (lymph)angiogenic privilege, whereas the limbus does not act as a physical or physiologic barrier to invading vessels. In CDh5-CreERT2VEGFR2lox/PGFD mice, conditional deletion of vascular endothelial growth factor receptor 2 in vascular endothelial cells abolished injury-induced HA and LA, demonstrating the utility of this transgenic mouse line for identifying important factors in the process of neovascularization.

Zone of retinal vascularization and refractive error in premature eyes with and without spontaneously regressed retinopathy of prematurity.

PURPOSE: To evaluate the relationship between zone of retinal vascularization and refractive error in premature infants without retinopathy of prematurity (ROP) or with spontaneously regressed ROP. METHODS: The medical records of neonates screened for ROP between 2009 and 2015 at a tertiary academic center were reviewed retrospectively. Cases included untreated eyes with spontaneously regressed ROP; premature eyes without a diagnosis of ROP were control subjects. Primary outcomes were zone of retinal vascularization and refractive error, determined by cycloplegic retinoscopy (CR). RESULTS: Of 378 eyes evaluated, 184 had ROP, 24 of which underwent treatment and were excluded. Mean corrected age at first CR was 7.5 months. Seventeen eyes without ROP were myopic at first CR (8.8%), compared to 35 eyes with regressed ROP (21.9%). No untreated eyes had halted vasculature in zone I; notably, 44% of spontaneously regressed zone II eyes were myopic. Irrespective of ROP status, CR significantly differed by zone of vascularization (P < 0.001), with more myopia occurring with posterior halting of vascularization. For all eyes, CR significantly differed between complete vascularization versus zone II (P < 0.0001) and zone III versus zone II (P = 0.001); zone III versus complete vascularization did not statistically differ (P = 0.15). This relationship held true for untreated, spontaneously regressed ROP eyes (P < 0.01, P = 0.01, P = 0.8343). CONCLUSIONS: More myopic refraction occurred in neonates screened for ROP with posterior halting of vascularization. Patients with halted vascular growth in zone II should be closely monitored for myopia and refractive amblyopia.