Ensemble learning to enhance accurate identification of patients with glaucoma using electronic health records.

Objectives: Existing ophthalmology studies for clinical phenotypes identification in real-world datasets (RWD) rely exclusively on structured data elements (SDE). We evaluated the performance, generalizability, and fairness of multimodal ensemble models that integrate real-world SDE and free-text data compared to SDE-only models to identify patients with glaucoma.

Materials And Methods: This is a retrospective cross-sectional study involving 2 health systems- University of Michigan (UoM) and Stanford University (SU).

Enhanced Phenotype Identification of Common Ocular Diseases in Real-World Datasets.

Objective: For studies using real-world data, accurately identifying patients with phenotypes of interest is challenging. To identify cohorts of interest, most studies exclusively use the International Classification of Diseases (ICD) billing codes, which can be limiting. We developed a method to accurately identify the presence or absence of 3 common ocular diseases (diabetic retinopathy [DR], age-related macular degeneration [AMD], and glaucoma) using electronic health record (EHR) data.

Association of Eye Drop-Treated Diseases and Conditions That Can Impair Eye Drop Self-Administration.

Objective: Eye drops are often first-line treatment for glaucoma and dry eye disease (DED). Unfortunately, proper eye drop self-administration is difficult, and this is likely magnified in persons with comorbid rheumatological, neurological, or cognitive disorders. This study investigates the association between ocular conditions often treated with eye drops (glaucoma and DED) and medical conditions that may impair proper eye drop self-administration.

The Association of Social Determinants of Health on Monitoring for Disease Progression Among Patients With Primary Open-Angle Glaucoma.

Purpose: To examine the association of race, ethnicity, and other social determinants of health (SDH) on receipt of optic nerve (ON) evaluation in accordance with National Quality Forum (NQF) and the American Academy of Ophthalmology (AAO) guideline-based metrics.

Methods: This was a retrospective cohort study consisting of 13,582 patients with POAG receiving care across 12 tertiary care health. The odds of receiving ≥1 ON evaluations to monitor for glaucoma progression over 45 months of follow-up was evaluated.

Validation of a Visual Field Prediction Tool for Glaucoma: A Multicenter Study Involving Patients With Glaucoma in the United Kingdom.

Purpose: A previously developed machine-learning approach with Kalman filtering technology accurately predicted the disease trajectory for patients with various glaucoma types and severities using clinical trial data. This study assesses performance of the KF approach with real-world data.

Design: Retrospective cohort study.

Incidence of Uveitis Following Initiation of Prostaglandin Analogs versus Other Glaucoma Medications: A Study from the Sight Outcomes Research Collaborative Repository.

Purpose: To evaluate the risk of incidence rates of uveitis among patients starting topical glaucoma therapy.

Design: Retrospective database study utilizing the Sight Outcomes Research Collaborative (SOURCE) Ophthalmology Data Repository.

Participants: Adult glaucoma patients who were recently started on topical glaucoma therapy.

Identifiable Historic and Observable Factors May Predict Progression to Exfoliation Glaucoma in Newly Diagnosed Exfoliation Patients.

Objective: To identify clinical factors associated with conversion to exfoliation glaucoma (XFG) in exfoliation syndrome (XFS) patients who are most at risk of progression to XFG within 3 years for increased surveillance and early preventive interventions.

Design: A retrospective patient cohort study design was employed.

Subjects: A source population of XFS patients ≥ 50 years was identified from electronic medical records in the Utah Population Database.

Big data and electronic health records for glaucoma research.

The digitization of health records through electronic health records (EHRs) has transformed the landscape of ophthalmic research, particularly in the study of glaucoma. EHRs offer a wealth of structured and unstructured data, allowing for comprehensive analyses of patient characteristics, treatment histories, and outcomes. This review comprehensively discusses different EHR data sources, their strengths, limitations, and applicability towards glaucoma research.

Effectiveness and Safety of Trabeculectomy Versus Tube Shunt Implantation for Uveitic Glaucoma: A Systematic Review and Meta-Analysis.

Purpose: To evaluate the effectiveness and safety of trabeculectomy compared to glaucoma drainage devices (GDDs) in managing uveitic glaucoma (UG).

Design: Systematic review.

Methods: We searched seven electronic databases (PubMed, Scopus, Web of Science, ScienceDirect, EMBASE, CENTRAL, and Google Scholar) to compare trabeculectomy with various GDDs in UG.

Race, Social Determinants of Health, and the Quality of Diabetic Eye Care.

Importance: Besides race, little is known about how other social determinants of health (SDOH) affect quality of diabetic eye care.

Objective: To evaluate the association between multiple SDOH and monitoring for diabetic retinopathy (DR) in accordance with clinical practice guidelines (CPGs).

Design, Setting, And Participants: This cohort study was conducted in 11 US medical centers and included adult patients (18-75 years old) with diabetes.

Comparison of Diagnosis Codes to Clinical Notes in Classifying Patients with Diabetic Retinopathy.

Purpose: Electronic health records (EHRs) contain a vast amount of clinical data. Improved automated classification approaches have the potential to accurately and efficiently identify patient cohorts for research. We evaluated if a rule-based natural language processing (NLP) algorithm using clinical notes performed better for classifying proliferative diabetic retinopathy (PDR) and nonproliferative diabetic retinopathy (NPDR) severity compared with International Classification of Diseases, ninth edition (ICD-9) or 10th edition (ICD-10) codes.

Demographic and Metabolic Risk Factors Associated with Development of Diabetic Macular Edema among Persons with Diabetes Mellitus.

Purpose: Diabetic macular edema (DME), a leading cause of visual impairment, can occur regardless of diabetic retinopathy (DR) stage. Poor metabolic control is hypothesized to contribute to DME development, although large-scale studies have yet to identify such an association. This study aims to determine whether measurable markers of dysmetabolism are associated with DME development in persons with diabetes.

Gap Analysis of Glaucoma Examination Concept Representations within Standard Systemized Nomenclature of Medicine - Clinical Terms.

Objective/purpose: Standardization of eye care data is important for clinical interoperability and research. We aimed to address gaps in the representations of glaucoma examination concepts within Systemized Nomenclature of Medicine - Clinical Terms (SNOMED-CT), the preferred terminology of the American Academy of Ophthalmology.

Design: Study of data elements.

Incidence of Acute Cystoid Macular Edema after Starting a Prostaglandin Analog Compared with Other Classes of Glaucoma Medications.

Purpose: There is a longstanding belief that prostaglandin analogs (PGAs) may predispose patients with glaucoma to develop acute cystoid macular edema (CME). However, there is little solid evidence supporting this notion. The purpose of this study is to compare CME incidence rates among patients initiating treatment with different glaucoma medication classes.

Assessment of Social Vulnerabilities of Care and Prognosis in Adult Ocular Melanomas in the US.

Background: Prior works have studied the impact of social determinants on various cancers but there is limited analysis on eye-orbit cancers. Current literature tends to focus on socioeconomic status and race, with sparse analysis of interdisciplinary contributions. We examined social determinants as measured by the Centers for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI), quantifying eye and orbit melanoma disparities across the United States.

Prediction Models for Glaucoma in a Multicenter Electronic Health Records Consortium: The Sight Outcomes Research Collaborative.

Purpose: Advances in artificial intelligence have enabled the development of predictive models for glaucoma. However, most work is single-center and uncertainty exists regarding the generalizability of such models. The purpose of this study was to build and evaluate machine learning (ML) approaches to predict glaucoma progression requiring surgery using data from a large multicenter consortium of electronic health records (EHR).

Using Natural Language Processing to Identify Different Lens Pathology in Electronic Health Records.

Purpose: Nearly all published ophthalmology-related Big Data studies rely exclusively on International Classification of Diseases (ICD) billing codes to identify patients with particular ocular conditions. However, inaccurate or nonspecific codes may be used. We assessed whether natural language processing (NLP), as an alternative approach, could more accurately identify lens pathology.

Reliability of Intraocular Pressure Measurements in a Low-Contact Drive-Through Setting.

Prcis: Drive-through intraocular pressure (IOP) measurement using iCare tonometry is a promising method of low-contact, high-throughput IOP monitoring. However, owing to its vulnerability to variable measurement technique and local air currents, the iCare may overestimate IOPs.

Purpose: During the COVID-19 pandemic, a drive-through IOP measurement protocol using the iCare tonometer was established to facilitate low-contact monitoring of select glaucoma patients.

MAEPI and CIR: New Metrics for Robust Evaluation of the Prediction Performance of AI-Based IOL Formulas.

Purpose: To develop a class of new metrics for evaluating the performance of intraocular lens power calculation formulas robust to issues that can arise with AI-based methods.

Methods: The dataset consists of surgical information and biometry measurements of 6893 eyes of 5016 cataract patients who received Alcon SN60WF lenses at University of Michigan's Kellogg Eye Center. We designed two types of new metrics: the MAEPI (Mean Absolute Error in Prediction of Intraocular Lens [IOL]) and the CIR (Correct IOL Rate) and compared the new metrics with traditional metrics including the mean absolute error (MAE), median absolute error, and standard deviation.

Systematic User-centered Design of a Prototype Clinical Decision Support System for Glaucoma.

Purpose: To rigorously develop a prototype clinical decision support (CDS) system to help clinicians determine the appropriate timing for follow-up visual field testing for patients with glaucoma and to identify themes regarding the context of use for glaucoma CDS systems, design requirements, and design solutions to meet these requirements.

Design: Semistructured qualitative interviews and iterative design cycles.

Participants: Clinicians who care for patients with glaucoma, purposefully sampled to ensure a representation of a range of clinical specialties (glaucoma specialist, general ophthalmologist, optometrist) and years in clinical practice.

Augmenting Kalman Filter Machine Learning Models with Data from OCT to Predict Future Visual Field Loss: An Analysis Using Data from the African Descent and Glaucoma Evaluation Study and the Diagnostic Innovation in Glaucoma Study.

Purpose: To assess whether the predictive accuracy of machine learning algorithms using Kalman filtering for forecasting future values of global indices on perimetry can be enhanced by adding global retinal nerve fiber layer (RNFL) data and whether model performance is influenced by the racial composition of the training and testing sets.

Design: Retrospective, longitudinal cohort study.

Participants: Patients with open-angle glaucoma (OAG) or glaucoma suspects enrolled in the African Descent and Glaucoma Evaluation Study or Diagnostic Innovation in Glaucoma Study.