Inter-Eye Association of Visual Field Defects in Glaucoma and Its Clinical Utility

Unraveling Visual Field Asymmetry: Insights Into Left-Right Differences in Glaucoma Patients

PURPOSE

Primary open-angle glaucoma (POAG) typically exhibits bilateral symmetry in visual field defects, while secondary glaucoma often manifests substantial left-right differences. This study investigates the diagnostic relevance of left-right differences in the Humphrey visual field (HVF) test and explores the factors influencing these differences.

STUDY DESIGN

This is a cross-sectional study.

METHODS

Parameters were assessed in 201 glaucoma patients, including age, sex, glaucoma disease type, central corneal thickness (CCT), corneal endothelial cell density (ECD), axial length, anterior chamber depth, refractive power, intraocular pressure (IOP), glaucoma drug score, and mean deviation (MD), pattern standard deviation (PSD), and visual field index (VFI) for both eyes in HVF. Patients were categorized into type 1 (POAG in both eyes) and type 2 (secondary glaucoma). Multivariable analysis was conducted to explore factors influencing left-right visual field test differences.

RESULTS

No significant differences were found between type 1 and type 2 in left-right MD, PSD, and VFI (p=0.13, 0.26, 0.09). Type 2 exhibited significant inter-eye differences in ECD, CCT, IOP, and glaucoma drug scores (p=0.02, <0.01, <0.001, 0.01). In the type 1 group, the left and right MD values were statistically significantly correlated (r=0.40, p<0.000001), but 24.6% of patients showed a left-right difference of 10 dB or more. Multivariable regression analysis identified anterior chamber depth as the sole significant factor influencing left-right MD differences in POAG (p=0.03).

CONCLUSION

Asymmetry in the visual field cannot distinguish between glaucoma disease types. In POAG, a shorter anterior chamber depth is associated with increased left-right MD differences, emphasizing its significance in understanding the progression of visual field defects.

Equitable artificial intelligence for glaucoma screening with fair identity normalization

Glaucoma is the leading cause of irreversible blindness globally. Research indicates a disproportionate impact of glaucoma on racial and ethnic minorities. Existing deep learning models for glaucoma detection might not achieve equitable performance across diverse identity groups. We developed fair identify normalization (FIN) module to equalize the feature importance across different identity groups to improve model performance equity. The optical coherence tomography (OCT) measurements were used to categorize patients into glaucoma and non-glaucoma. The equity-scaled area under the receiver operating characteristic curve (ES-AUC) was adopted to quantify model performance equity. With FIN for racial groups, the overall AUC and ES-AUC increased from 0.82 to 0.85 and 0.77 to 0.81, respectively, with the AUC for Blacks increasing from 0.77 to 0.82. With FIN for ethnic groups, the overall AUC and ES-AUC increased from 0.82 to 0.84 and 0.77 to 0.80, respectively, with the AUC for Hispanics increasing from 0.75 to 0.79.

Cohort Study of Race/Ethnicity and Incident Primary Open-Angle Glaucoma Characterized by Autonomously Determined Visual Field Loss Patterns

Purpose

We evaluated racial/ethnic differences in primary open-angle glaucoma (POAG) defined by machine-learning–derived regional visual field (VF) loss patterns.

Methods

Participants (N = 209,036) from the Nurses’ Health Study (NHS; 1980–2018), Nurses’ Health Study II (NHS2; 1989–2019), and Health Professionals Follow-Up Study (HPFS; 1986–2018) who were ≥40 years of age and free of glaucoma were followed biennially. Incident POAG cases (n = 1946) with reproducible VF loss were confirmed with medical records. Total deviation information from the earliest reliable glaucomatous VF for each POAG eye (n = 2564) was extracted, and machine learning analyses were used to identify optimal solutions (“archetypes”) for regional VF loss patterns. Each POAG eye was assigned a VF archetype based on the highest weighting coefficient. Multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using per-eye Cox proportional hazards models.

Results

We identified 14 archetypes: four representing advanced loss patterns, nine of early loss, and one of no VF loss. Compared to non-Hispanic whites, black participants had higher risk of early VF loss archetypes (HR = 1.98; 95% CI, 1.48–2.66) and even higher risk for advanced loss archetypes (HR = 6.17; 95% CI, 3.69–10.32; P-contrast = 0.0002); no differences were observed for Asians or Hispanic whites. Hispanic white participants had significantly higher risks of POAG with paracentral defects and advanced superior loss; black participants had significantly higher risks of all advanced loss archetypes and three early loss patterns, including paracentral defects.

Conclusions

Blacks, compared to non-Hispanic whites, had higher risks of POAG with early central and advanced VF loss.

Translational Relevance

In POAG, risks of VF loss regional patterns derived from machine learning algorithms showed racial differences.

Inter-eye correlation analysis of 24-h IOPs and glaucoma progression

Purpose

To determine whether 24-h IOP monitoring can be a predictor for glaucoma progression and to analyze the inter-eye relationship of IOP, perfusion, and progression parameters.

Methods

We extracted data from manually drawn IOP curves with HIOP-Reader, a software suite we developed. The relationship between measured IOPs and mean ocular perfusion pressures (MOPP) to retinal nerve fiber layer (RNFL) thickness was analyzed. We determined the ROC curves for peak IOP (T_max), average IOP(T_avg), IOP variation (IOP_var), and historical IOP cut-off levels to detect glaucoma progression (rate of RNFL loss). Bivariate analysis was also conducted to check for various inter-eye relationships.

Results

Two hundred seventeen eyes were included. The average IOP was 14.8 ± 3.5 mmHg, with a 24-h variation of 5.2 ± 2.9 mmHg. A total of 52% of eyes with RNFL progression data showed disease progression. There was no significant difference in T_max, T_avg, and IOP_var between progressors and non-progressors (all p > 0.05). Except for T_avg and the temporal RNFL, there was no correlation between disease progression in any quadrant and T_max, T_avg, and IOP_var. Twenty-four-hour and outpatient IOP variables had poor sensitivities and specificities in detecting disease progression. The correlation of inter-eye parameters was moderate; correlation with disease progression was weak.

Conclusion

In line with our previous study, IOP data obtained during a single visit (outpatient or inpatient monitoring) make for a poor diagnostic tool, no matter the method deployed. Glaucoma progression and perfusion pressure in left and right eyes correlated weakly to moderately with each other.