Faculty Bibliography

PRECISE/UNASSIGNED:In this article, we describe the development of validated software that automates cost-effectiveness analyses of minimally invasive glaucoma surgeries based on modifications to robust mathematical models of glaucoma progression and management discussed in previous literature. PURPOSE/OBJECTIVE:To develop a validated application to streamline use of cost-effectiveness (CE) in clinical management and investigations of minimally invasive glaucoma surgeries (MIGS) in the United States. DESIGN/METHODS:Automated cost-utility analysis adapted from Markov models described in prior literature. PARTICIPANTS/METHODS:Patients of ages 65 years and greater with mild to moderate primary open-angle glaucoma irrespective of concurrent visually significant cataract. METHODS:Markov models simulating glaucoma progression through 37 states and death were constructed based on previous CE models of minimally invasive trabecular meshwork stents. These states represent combinations of various glaucoma severity (mild, moderate, advanced, and severe/blind) with differences in clinical management, including use of up to 4 medications, selective laser trabeculoplasty, or incisional surgery. These are not mutually exclusive and are based on decisions related to the rate of thinning of the retinal nerve fiber layer. Rather than using fixed sets of transition probabilities for specific surgical interventions, new transition probabilities are dynamically derived based on the expected reduction in intraocular pressure related to visual field mean deviation decline. In addition to the generic MIGS arm, two comparison arms (cataract-surgery or medications-only) are included. Medication reduction, whole costs, and utilities are modifiable inputs in the model. Optimal and worst-case results are determined by uncomplicated or complicated (secondary surgical intervention required/medication nonadherence) intervention outcomes. The model was entirely re-implemented in R and validated by comparing results to TreeAge data. MAIN OUTCOME MEASURES/METHODS:Total costs, quality adjusted life years (QALY), and incremental cost-effectiveness ratio (ICER). RESULTS:An optimal-case 35-year CE-analysis of the implantation of Hydrus and iStent inject devices provided values of costs and QALY that were similar to prior data (R vs. TreeAge): Hydrus (Cost: $50446.53 vs. $48026.13; QALY: 12.18 vs. 12.26), iStent inject (Cost: $52323.43 vs. $49599.86; QALY: 12.13 vs. 12.21), cataract (Cost: $54150.56 vs. $54409.25; QALY: 12.03 vs. 12.04). Trends of ICER over time were also very similar. CONCLUSIONS:Novel software is available to aid in CE analyses of MIGS with modifiable inputs and outcomes of interest. Such a tool makes CE more accessible for use in clinical management decisions and may guide future investigation.

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.

TOPIC/UNASSIGNED:This scoping review summarizes artificial intelligence (AI) reporting in ophthalmology literature in respect to model development and validation. We characterize the state of transparency in reporting of studies prospectively validating models for disease classification. CLINICAL RELEVANCE/UNASSIGNED:Understanding what elements authors currently describe regarding their AI models may aid in the future standardization of reporting. This review highlights the need for transparency to facilitate the critical appraisal of models prior to clinical implementation, to minimize bias and inappropriate use. Transparent reporting can improve effective and equitable use in clinical settings. METHODS/UNASSIGNED:Eligible articles (as of January 2022) from PubMed, Embase, Web of Science, and CINAHL were independently screened by 2 reviewers. All observational and clinical trial studies evaluating the performance of an AI model for disease classification of ophthalmic conditions were included. Studies were evaluated for reporting of parameters derived from reporting guidelines (CONSORT-AI, MI-CLAIM) and our previously published editorial on model cards. The reporting of these factors, which included basic model and dataset details (source, demographics), and prospective validation outcomes, were summarized. RESULTS/UNASSIGNED:Thirty-seven prospective validation studies were included in the scoping review. Eleven additional associated training and/or retrospective validation studies were included if this information could not be determined from the primary articles. These 37 studies validated 27 unique AI models; multiple studies evaluated the same algorithms (EyeArt, IDx-DR, and Medios AI). Details of model development were variably reported; 18 of 27 models described training dataset annotation and 10 of 27 studies reported training data distribution. Demographic information of training data was rarely reported; 7 of the 27 unique models reported age and gender and only 2 reported race and/or ethnicity. At the level of prospective clinical validation, age and gender of populations was more consistently reported (29 and 28 of 37 studies, respectively), but only 9 studies reported race and/or ethnicity data. Scope of use was difficult to discern for the majority of models. Fifteen studies did not state or imply primary users. CONCLUSION/UNASSIGNED:Our scoping review demonstrates variable reporting of information related to both model development and validation. The intention of our study was not to assess the quality of the factors we examined, but to characterize what information is, and is not, regularly reported. Our results suggest the need for greater transparency in the reporting of information necessary to determine the appropriateness and fairness of these tools prior to clinical use. FINANCIAL DISCLOSURES/UNASSIGNED:Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

[This corrects the article DOI: 10.3389/fcvm.2022.1024044.].

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.