Longitudinal visual field variability and the ability to detect glaucoma progression in black and white individuals

A Novel Approach To Predict Glaucomatous Impairment in the Central 10° Visual Field, Excluding the Effect of Cataract

Purpose

Our previous study predicted genuine glaucomatous visual field (VF) impairment in the central 10° VF, excluding the effect of cataract, using visual acuity (VA) and global indexes of VF more accurately than pattern deviation (PD). This study aimed to improve the accuracy by using pointwise total deviation (TD) values with the machine-learning method of random forest model (RFM) and to investigate whether incorporating optical coherence tomography-measured ganglion cell-inner plexiform layer (GCIPL) thickness is useful.

Methods

This retrospective study included 89 eyes with open-angle glaucoma that underwent successful cataract surgery (with or without iStent implantation or ab interno trabeculotomy). Postoperative TD in each of the 68 VF points was predicted using preoperative (1) PD, (2) VA and VF with a linear regression model (LM), and (3) VA and VF with RFM, and averaged as predicted mean TD (mTDpost). Further prediction was made by incorporating the preoperative GCIPL into the best model.

Results

The mean absolute error (MAE) between the actual and predicted mTDpost with RFM (1.25 ± 1.03 dB) was significantly smaller than that with PD (3.20 ± 4.06 dB, p < 0.01) and LM (1.42 ± 1.06 dB, p < 0.05). The MAEs with the model incorporating GCIPL into RFM (1.24 ± 1.04 dB) and RFM were not significantly different.

Conclusions

Accurate prediction of genuine glaucomatous VF impairment was achieved using pointwise TD with RFM. No merit was observed by incorporating the GCIPL into this model.

Translational Relevance

This pointwise RFM could clinically reduce cataract effect on VF.

Impact of Demographics on Regional Visual Field Loss and Deterioration in Glaucoma

Purpose

To elucidate the impact of demographics, including gender, race, ethnicity, and preferred language, on regional visual field (VF) loss and progression in glaucoma.

Methods

Multivariable linear mixed regressions were performed to determine the impact of race, ethnicity, and preferred language on regional VF loss with adjustment for age and gender. Regional VF loss was defined by pointwise total deviation values and VF loss patterns quantified by an unsupervised machine learning method termed archetypal analysis. All cross-sectional and longitudinal analyses were performed both without and with adjustment for VF mean deviation, which represented overall VF loss severity. P values were corrected for multiple comparisons.

Results

All results mentioned had corrected P values less than 0.05. Asian and Black patients showed worse pointwise VF loss than White patients with superior hemifield more affected. Patients with a preferred language other than English demonstrated worse pointwise VF loss than patients with English as their preferred language. Longitudinal analyses revealed Black patients showed worse VF loss/year compared to White patients. Patients with a preferred language other than English demonstrated worse VF loss/year compared to patients preferring English.

Conclusions

Blacks and non-English speakers have more severe VF loss, with superior hemifield being more affected and faster VF worsening.

Translational Relevance

This study furthered our understanding of racial, ethnic, and socioeconomic disparities in glaucoma outcomes. Understanding the VF loss burden in different racial, ethnic, and socioeconomic groups may guide more effective glaucoma screening and community outreach efforts. This research could help reduce vision loss and improve quality of life in disproportionately affected populations by guiding public health efforts to promote glaucoma awareness and access to care.

Big data for imaging assessment in glaucoma

Glaucoma is the leading cause of irreversible blindness worldwide, with many individuals unaware of their condition until advanced stages, resulting in significant visual field impairment. Despite effective treatments, over 110 million people are projected to have glaucoma by 2040. Early detection and reliable monitoring are crucial to prevent vision loss. With the rapid development of computational technologies, artificial intelligence (AI) and deep learning (DL) algorithms are emerging as potential tools for screening, diagnosing, and monitoring glaucoma progression. Leveraging vast data sources, these technologies promise to enhance clinical practice and public health outcomes by enabling earlier disease detection, progression forecasting, and deeper understanding of underlying mechanisms. This review evaluates the use of Big Data and AI in glaucoma research, providing an overview of most relevant topics and discussing various models for screening, diagnosis, monitoring disease progression, correlating structural and functional changes, assessing image quality, and exploring innovative technologies such as generative AI.

The Impact of Social Vulnerability on Structural and Functional Glaucoma Severity, Worsening, and Variability

PURPOSE

To determine the associations between social vulnerability index (SVI) and baseline severity, worsening, and variability of glaucoma, as assessed by visual field (VF) and OCT.

DESIGN

Retrospective longitudinal cohort study.

PARTICIPANTS

Adults with glaucoma or glaucoma suspect status in 1 or both eyes. Visual fields were derived from 7897 eyes from 4482 patients, while OCTs were derived from 6271 eyes from 3976 patients. All eyes had a minimum of 5 tests over follow-up using either the Humphrey Field Analyzer or the Cirrus HD-OCT.

METHODS

Social vulnerability index, which measures neighborhood-level environmental factors, was linked to patients' addresses at the census tract level. Rates of change in mean deviation (MD) and retinal nerve fiber layer (RNFL) thickness were computed using linear regression. The slope of the regression line was used to assess worsening, while the standard deviation of residuals was used as a measure of variability. Multivariable linear mixed-effects models were used to investigate the impact of SVI on baseline, worsening, and variability in both MD and RNFL. We further explored the interaction effect of mean intraocular pressure (IOP) and SVI on worsening in MD and RNFL.

MAIN OUTCOME MEASURES

Glaucoma severity defined based on baseline MD and RNFL thickness. Worsening defined as MD and RNFL slope. Variability defined as the standard deviation of the residuals obtained from MD and RNFL slopes.

RESULTS

Increased (worse) SVI was significantly associated with worse baseline MD (β = -1.07 dB, 95% confidence interval [CI]: [-1.54, -0.60]), thicker baseline RNFL (β = 2.46 μm, 95% CI: [0.75, 4.17]), greater rates of RNFL loss (β = -0.12 μm, 95% CI: [-0.23, -0.02]), and greater VF variability (β = 0.16 dB, 95% CI: [0.07, 0.24]). Having worse SVI was associated with worse RNFL loss with increases in IOP (βinteraction = -0.07, 95% CI: [-0.12, -0.02]).

CONCLUSIONS

Increased SVI score is associated with worse functional (VF) loss at baseline, higher rates of structural (OCT) worsening over time, higher VF variability, and a greater effect of IOP on RNFL loss. Further studies are needed to enhance our understanding of these relationships and establish their cause.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Assessment of Remote Training, At-Home Testing, and Test-Retest Variability of a Novel Test for Clustered Virtual Reality Perimetry

OBJECTIVE

To assess the feasibility of remotely training glaucoma patients to take a 10-session clustered virtual reality (VR) visual field (VF) test (Vivid Vision Perimetry [VVP-10]) at home, analyze results for test-retest variability, and assess correspondence with conventional perimetry.

DESIGN

Cross-sectional study.

SUBJECTS

Twenty-one subjects with glaucoma were enrolled and included in the feasibility assessment of remote training. Thirty-six eyes were used for test-retest analysis and determination of concordance with the Humphrey Field Analyzer (HFA).

METHODS

Subjects were provided with a mobile VR headset containing the VVP-10 test software and trained remotely via video conferencing. Subjects were instructed to complete 10 sessions over a 14-day period.

MAIN OUTCOME MEASURES

Feasibility was determined by the number of subjects who were able to independently complete VVP-10 over the 14-day period after 1 remote training session. The intraclass correlation coefficient (ICC) for average fraction seen across 10 sessions and the standard error (SE) of the mean were primary outcome measures for assessing test-retest variability. Correlation with HFA mean sensitivity (MS) across eyes, was a secondary outcome measure.

RESULTS

Twenty subjects (95%) successfully completed the VVP-10 test series after 1 training session. The ICC for VVP-10 was 0.95 (95% confidence interval [CI], 0.92-0.97). The mean SE in units of fraction seen was 0.012. The Spearman correlations between VVP-10 average fraction seen and HFA MS were 0.87 (95% CI, 0.66-0.98) for moderate-to-advanced glaucoma eyes, and decreased to 0.67 (95% CI, 0.28-0.94) when all eyes were included.

CONCLUSIONS

Remote training of patients at home is feasible, and subsequent remote clustered VF testing using VVP-10 by patients on their own, without any further interactions with caregivers or study staff, was possible. At-home VVP-10 results demonstrated low test-retest variability. Future studies must be conducted to determine if VVP-10, taken at home as convenient for the patient, may be a viable supplement to provide equivalent or complementary results to that of standard in-clinic assessment of visual function in glaucoma.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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.

Methods

Using the established User-Centered Design Process framework, we conducted semistructured interviews with 5 clinicians that addressed the context of use and design requirements for a glaucoma CDS system. We analyzed the interviews using inductive thematic analysis and grounded theory to generate themes regarding the context of use and design requirements. We created design solutions to address these requirements and used iterative design cycles with the clinicians to refine the CDS prototype.

Main Outcome Measures

Themes regarding decision support for determining the timing of visual field testing for patients with glaucoma, CDS design requirements, and CDS design features.

Results

We identified 9 themes that addressed the context of use for the CDS system, 9 design requirements for the prototype CDS system, and 9 design features intended to address these design requirements. Key design requirements included the preservation of clinician autonomy, incorporation of currently used heuristics, compilation of data, and increasing and communicating the level of certainty regarding the decision. After completing 3 iterative design cycles using this preliminary CDS system design solution, the design was satisfactory to the clinicians and was accepted as our prototype glaucoma CDS system.

Conclusions

We used a systematic design process based on the established User-Centered Design Process to rigorously develop a prototype glaucoma CDS system, which will be used as a starting point for a future, large-scale iterative refinement and implementation process. Clinicians who care for patients with glaucoma need CDS systems that preserve clinician autonomy, compile and present data, incorporate currently used heuristics, and increase and communicate the level of certainty regarding the decision.

Financial Disclosures

Proprietary or commercial disclosure may be found after the references.

The Frequency of Visual Field Testing in a US Nationwide Cohort of Individuals with Open-Angle Glaucoma

PURPOSE

Visual field testing that is not frequent enough results in delayed identification of open-angle glaucoma (OAG) progression. Guidelines recommend at least annual testing. It is not known how frequently patients with OAG across the United States receive visual field testing and how patient characteristics and circumstances influence this frequency. If US patients with OAG do not receive visual field tests frequently enough, interventions to increase this frequency or to develop other forms of testing visual function may reduce unidentified OAG vision loss.

DESIGN

Retrospective cohort study.

PARTICIPANTS

The TruvenHealth MarketScan Commercial Claims Database (IBM) contains demographic and claims data for > 160 million individuals across the United States from 2008 to 2017. We identified enrollees in the database with a recorded diagnosis of OAG (International Classification of Diseases, Ninth Revision, Clinical Modification and International Classification of Diseases, Tenth Revision, Clinical Modification codes 356.1x and H40.1x, respectively). We excluded those aged < 40 years at the time of their first OAG diagnosis, those without at least 1 confirmatory OAG diagnosis at a subsequent visit, and those with < 4 years of follow-up data after OAG diagnosis.

METHODS

We calculated the number of visual field tests that each enrollee with OAG underwent per year and categorized the enrollees based on that number (0, > 0 to < 0.9, ≥ 0.9 to ≤ 1.1, > 1.1 to ≤ 2.1, and > 2.1). We used negative binomial regression to investigate the demographic or health variables that were associated with the frequency of visual field tests that enrollees with OAG received.

MAIN OUTCOME MEASURES

Frequency of visual field testing among enrollees with OAG.

RESULTS

Of the 380 029 enrollees included in the study, 33 267 (8.8%) did not receive a visual field test during the study period, 259 349 (68.2%) underwent > 0 to < 0.9 visual field tests per year, 42 129 (11.1%) underwent ≥ 0.9 to ≤ 1.1 visual field tests per year, 42 301 (11.1%) underwent > 1.1 to ≤ 2.1 visual field tests per year, and 2983 (0.8%) underwent ≥ 2.1 visual field tests per year. The median number of visual field tests per year was 0.63 (interquartile range, 0.33-0.88; mean, 0.65).

CONCLUSIONS

More than 75% of enrollees with OAG received < 1 visual field test per year and, thus, did not receive guideline-adherent glaucoma monitoring.

Racial and Ethnic Disparities in Cost-Related Barriers to Medication Adherence Among Patients With Glaucoma Enrolled in the National Institutes of Health All of Us Research Program

IMPORTANCE

Ability to afford medication is a major determinate of medication adherence among patients.

OBJECTIVE

To determine cost-related barriers to medication adherence by race and ethnicity in a nationwide cohort of patients with glaucoma.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study included patients with glaucoma enrolled in the National Institutes of Health All of Us Research Program, a nationwide longitudinal cohort of US adults, with more than 300 000 currently enrolled. Individuals with a diagnosis of glaucoma based on electronic health record diagnosis codes who participated in the Health Care Access and Utilization survey and had complete data on all covariates were studied. Data were collected from June 2016 to March 2021, and data were analyzed from August to November 2021.

EXPOSURES

Race and ethnicity defined as non-Hispanic African American, non-Hispanic Asian, Hispanic, and non-Hispanic White.

MAIN OUTCOMES AND MEASURES

Logistic regression was used to evaluate the association between reported cost-related barriers to medication adherence (could not afford prescription medication, skipped medication doses to save money, took less medication to save money, delayed filling a prescription to save money, asked for lower-cost medication to save money, bought prescriptions from another country to save money, and used alternative therapies to save money) and race and ethnicity, adjusting multivariable models by age, gender, health insurance status, education, and income. Odds ratios of these barriers were obtained by race and ethnicity, with non-Hispanic White race as the reference group.

RESULTS

Of 3826 included patients with glaucoma, 481 (12.6%) were African American, 119 (3.1%) were non-Hispanic Asian, 351 (9.2%) were Hispanic, and 2875 (75.1%) were non-Hispanic White. The median (IQR) age was 69 (60-75) years, and 2307 (60.3%) were female. After adjusting for confounders, non-Hispanic African American individuals (odds ratio, 1.82; 95% CI, 1.34-2.44) and Hispanic individuals (odds ratio, 1.77; 95% CI, 1.25-2.49) were more likely than non-Hispanic White individuals to report not being able to afford medications. Further, despite having the lowest rate of endorsing difficulty affording medications, non-Hispanic White individuals were equally likely to ask for lower-cost medication from their clinicians as individuals of racial and ethnic minority groups.

CONCLUSIONS AND RELEVANCE

In this study, there was significantly higher odds of self-reported difficulty affording medications among non-Hispanic African American and Hispanic individuals compared with non-Hispanic White individuals. Clinicians should be proactive and initiate discussions about costs in an effort to promote medication adherence and health equity among patients.