Faculty Bibliography

PRCIS/CONCLUSIONS:Residence in a middle-class neighborhood correlated with lower follow-up compared to residence in more affluent neighborhoods. The most common explanations for not following up were the process of making an appointment and lack of symptoms. PURPOSE/OBJECTIVE:To explore which individual and neighborhood-level factors influence follow-up as recommended after positive ophthalmic and primary care screening in a vulnerable population using novel methodologies. PARTICIPANTS/METHODS:and Methods: From 2017 to 2018, 957 participants were screened for ophthalmic disease and cardiovascular risk factors as part of the Real-Time Mobile Teleophthalmology study. Individuals who screened positive for either ophthalmic or cardiovascular risk factors were contacted to determine whether or not they followed up with a healthcare provider. Data from the Social Vulnerability Index, a novel virtual auditing system, and personal demographics were collected for each participant. A multivariate logistic regression was performed to determine which factors significantly differed between participants who followed up and those who did not. RESULTS:As a whole, the study population was more socioeconomically vulnerable than the national average (mean summary Social Vulnerability Index score=0.81). Participants whose neighborhoods fell in the middle of the national per capita income distribution had lower likelihood of follow-up compared to those who resided in the most affluent neighborhoods (relative risk ratio=0.21, P-value<0.01). Participants cited the complicated process of making an eye care appointment and lack of symptoms as the most common reasons for not following up as instructed within four months. CONCLUSIONS:Residence in a middle-class neighborhood, difficulty accessing eye care appointments, and low health literacy may influence follow up among vulnerable populations.

BACKGROUND:Glaucoma is one of the leading causes of global blindness and is expected to co-occur more frequently with vascular morbidities in the upcoming years, as both are aging-related diseases. Yet, the pathogenesis of glaucoma is not entirely elucidated and the interplay between intraocular pressure, arterial blood pressure and ocular perfusion pressure is poorly understood. OBJECTIVE:This systematic review aims to provide clinicians with the latest literature regarding the management of arterial blood pressure in glaucoma patients. METHODS:A systematic search was performed in Medline, Embase, Web of Science and Cochrane Library. Articles written in English assessing the influence of arterial blood pressure and systemic antihypertensive treatment of glaucoma and its management were eligible for inclusion. Additional studies were identified by revising references included in selected articles. RESULTS:80 articles were included in this systemic review. A bimodal relation between blood pressure and glaucoma progression was found. Both high and low blood pressure increase the risk of glaucoma. Glaucoma progression was, possibly via ocular perfusion pressure variation, strongly associated with nocturnal dipping and high variability in the blood pressure over 24-hours. CONCLUSIONS:We concluded that systemic blood pressure level associates with glaucomatous damage and provided recommendations for the management and study of arterial blood pressure in glaucoma. Prospective clinical trials are needed to further support these recommendations.

PURPOSE:Cost-effectiveness analyses (CEAs) quantify and compare both costs and measures of efficacy for different interventions. As the costs of glaucoma management to patients, payers, and physicians are increasing, we seek to investigate the role of CEAs in the field of glaucoma and how such studies impact clinical management. METHODS:We adhered to the "Preferred Reporting Items for Systematic Reviews and Meta-analyses" guidelines for our systematic review structure. Eligible studies included any full-text articles that investigated cost-effectiveness or cost-utility as it relates to the field of open angle glaucoma management in the United States. Risk of bias assessment was conducted using the validated Joanna Briggs Institute Critical Appraisal Checklist for Economic Evaluations. RESULTS:Eighteen studies were included in the review. Dates of publication ranged from 1983 to 2021. Most of the studies were published in the 2000s and performed CEAs in the domains of treatment/therapy, screening, and adherence for patients with primary angle open glaucoma. Of the 18 articles included, 14 focused on treatment, 2 on screening, and 2 on adherence. Most of these studies focused on the cost-effectiveness of different topical medical therapies, whereas only a few studies explored laser procedures, surgical interventions, or minimally invasive procedures. Economic models using decision analysis incorporating state-transition Markov cycles or Montecarlo simulations were widely used, however, the methodology among studies was variable, with a wide spectrum of inputs, measures of outcomes, and time horizons used. CONCLUSION:Overall, we found that cost-effectiveness research in glaucoma in the United States remains relatively unstructured, resulting in unclear and conflicting implications for clinical management.

BACKGROUND:Environmental factors have been implicated in various eye pathologies. The purpose of this review is to synthesise the published research on environmental effects on eye disease. METHODS:Four databases were searched for terms relating to environmental exposures and ophthalmic disease. Titles and abstracts were screened followed by full-text review. Data was extracted from 118 included studies. Quality assessment was conducted for each study. RESULTS:Air pollutants, including nitrogen dioxide, nitrites, sulphur dioxide, particulate matter, carbon monoxide, ozone and hydrocarbons are associated with ocular conditions ranging from corneal damage to various retinopathies, including central retinal artery occlusion. Certain chemicals and metals, such as cadmium, are associated with increased risk of age-related macular degeneration. Climate factors, such as sun exposure, have been associated with the development of cataracts. Living in rural areas was associated with various age-related eye diseases whereas people living in urban settings had higher risk for dry eye disease and uveitis. CONCLUSION/CONCLUSIONS:Environmental exposures in every domain are associated with various ophthalmic conditions. These findings underscore the importance of continued research on the interplay between the environment and eye health.

Artificial intelligence and machine learning applications are becoming increasingly popular in health care and medical devices. The development of accurate machine learning algorithms requires large quantities of good and diverse data. This poses a challenge in health care because of the sensitive nature of sharing patient data. Decentralized algorithms through federated learning avoid data aggregation. In this paper we give an overview of federated learning, current examples in healthcare and ophthalmology, challenges, and next steps.

BACKGROUND:The development of accurate machine learning algorithms requires sufficient quantities of diverse data. This poses a challenge in health care because of the sensitive and siloed nature of biomedical information. Decentralized algorithms through federated learning (FL) avoid data aggregation by instead distributing algorithms to the data before centrally updating one global model. OBJECTIVE:To establish a multicenter collaboration and assess the feasibility of using FL to train machine learning models for intracranial hemorrhage (ICH) detection without sharing data between sites. METHODS:Five neurosurgery departments across the United States collaborated to establish a federated network and train a convolutional neural network to detect ICH on computed tomography scans. The global FL model was benchmarked against a standard, centrally trained model using a held-out data set and was compared against locally trained models using site data. RESULTS:A federated network of practicing neurosurgeon scientists was successfully initiated to train a model for predicting ICH. The FL model achieved an area under the ROC curve of 0.9487 (95% CI 0.9471-0.9503) when predicting all subtypes of ICH compared with a benchmark (non-FL) area under the ROC curve of 0.9753 (95% CI 0.9742-0.9764), although performance varied by subtype. The FL model consistently achieved top three performance when validated on any site's data, suggesting improved generalizability. A qualitative survey described the experience of participants in the federated network. CONCLUSION/CONCLUSIONS:This study demonstrates the feasibility of implementing a federated network for multi-institutional collaboration among clinicians and using FL to conduct machine learning research, thereby opening a new paradigm for neurosurgical collaboration.

Objective: Although artificial intelligence (AI) models may offer innovative and powerful ways to use the wealth of data generated by diagnostic tools, there are important challenges related to their development and validation. Most notable is the lack of a perfect reference standard for glaucomatous optic neuropathy (GON). Because AI models are trained to predict presence of glaucoma or its progression, they generally rely on a reference standard that is used to train the model and assess its validity. If an improper reference standard is used, the model may be trained to detect or predict something that has little or no clinical value. This article summarizes the issues and discussions related to the definition of GON in AI applications as presented by the Glaucoma Workgroup from the Collaborative Community for Ophthalmic Imaging (CCOI) US Food and Drug Administration Virtual Workshop, on September 3 and 4, 2020, and on January 28, 2022. Design: Review and conference proceedings. Subjects: No human or animal subjects or data therefrom were used in the production of this article. Methods: A summary of the Workshop was produced with input and approval from all participants. Main Outcome Measures: Consensus position of the CCOI Workgroup on the challenges in defining GON and possible solutions. Results: The Workshop reviewed existing challenges that arise from the use of subjective definitions of GON and highlighted the need for a more objective approach to characterize GON that could facilitate replication and comparability of AI studies and allow for better clinical validation of proposed AI tools. Different tests and combination of parameters for defining a reference standard for GON have been proposed. Different reference standards may need to be considered depending on the scenario in which the AI models are going to be applied, such as community-based or opportunistic screening versus detection or monitoring of glaucoma in tertiary care. Conclusions: The development and validation of new AI-based diagnostic tests should be based on rigorous methodology with clear determination of how the reference standards for glaucomatous damage are constructed and the settings where the tests are going to be applied.

Diagnosis and detection of progression of glaucoma remains challenging. Artificial intelligence-based tools have the potential to improve and standardize the assessment of glaucoma but development of these algorithms is difficult given the multimodal and variable nature of the diagnosis. Currently, most algorithms are focused on a single imaging modality, specifically screening and diagnosis based on fundus photos or optical coherence tomography images. Use of anterior segment optical coherence tomography and goniophotographs is limited. The majority of algorithms designed for disease progression prediction are based on visual fields. No studies in our literature search assessed the use of artificial intelligence for treatment response prediction and no studies conducted prospective testing of their algorithms. Additional challenges to the development of artificial intelligence-based tools include scarcity of data and a lack of consensus in diagnostic criteria. Although research in the use of artificial intelligence for glaucoma is promising, additional work is needed to develop clinically usable tools.

OBJECTIVE:Our aim was to assess the acceptability and feasibility of iCare HOME tonometer (HT) and Virtual Field (VF) devices in the home monitoring of glaucoma. DESIGN/METHODS:Prospective feasibility and acceptability study. SUBJECTS/METHODS:Twenty patients (39 eyes) with primary open-angle glaucoma, open-angle glaucoma, ocular hypertension, or suspected glaucoma. METHODS:Patients were trained and instructed to bring 2 devices home for 1 week and use the HT 4 times/day for 4 days and the VF 3 times total. MAIN OUTCOME MEASURES/METHODS:For acceptability, we conducted satisfaction surveys and semistructured, qualitative interviews with a thematic analysis. Feasibility was assessed by device usage and quality of tests. RESULTS:Most patients (73.7%) felt that the HT was easy to use, and 100% of them found the HT useful. All patients (100%) felt that VF was easy to use, and 94.4% of them found the VF useful. All patients (100%) obtained acceptable intraocular pressure and completed a VF test at home. We identified 4 key themes, with 33 subthemes. The key themes include the following: (1) advantages of home monitoring; (2) difficulties with home monitoring; (3) future considerations in home monitoring; and (4) the experience of patients with glaucoma. CONCLUSIONS:The HT and VF were acceptable and feasible in a small cohort of motivated subjects. Patients were able to perform these tests proficiently at home, and they were generally enthused to obtain more data about their intraocular health, as it allowed them a heightened sense of security and insight about their chronic disease, as well as a reduction in foreseeable barriers to care. Home monitoring may also improve upon glaucoma care by enhancing patient empowerment and fostering community bonds. The VF should be further evaluated to ensure validity.