Comparing Perimetric Loss at Different Target Intraocular Pressures for Patients with High-Tension and Normal-Tension Glaucoma

The Impact of Achieving Target Intraocular Pressure on Glaucomatous Retinal Nerve Fiber Layer Thinning in a Treated Clinical Population

PURPOSE

To estimate the effect of being below and above the clinician-set target intraocular pressure (IOP) on rates of glaucomatous retinal nerve fiber layer (RNFL) thinning in a treated real-world clinical population.

DESIGN

Retrospective cohort study.

METHODS

A total of 3256 eyes (1923 patients) with ≥5 reliable optical coherence tomography scans and 1 baseline visual field test were included. Linear mixed-effects modeling estimated the effects of the primary independent variables (mean target difference [measured IOP - target IOP] and mean IOP, mm Hg) on the primary dependent variable (RNFL slope, µm/y) while accounting for additional confounding variables (age, biological sex, race, baseline RNFL, baseline pachymetry, and disease severity). A spline term accounted for differential effects when above (target difference >0 mm Hg) and below (target difference ≤0 mm Hg) target pressure.

RESULTS

Eyes below and above target had significantly different mean RNFL slopes (-0.44 vs -0.71 µm/y, P < .001). Each 1 mm Hg increase above target had a 0.143 µm/y faster rate of RNFL thinning (P < .001). Separating by disease severity, suspect, mild, moderate, and advanced glaucoma had 0.135 (P = .002), 0.116 (P = .009), 0.203 (P = .02), and 0.65 (P = .22) µm/y faster rates of RNFL thinning per 1 mm Hg increase, respectively.

CONCLUSIONS

Being above the clinician-set target pressure is associated with more rapid RNFL thinning in suspect, mild, and moderate glaucoma. Faster rates of thinning were also present in advanced glaucoma, but statistical significance was limited by the lower sample size of eyes above target and the optical coherence tomography floor effect.

Precision Medicine in Glaucoma: Artificial Intelligence, Biomarkers, Genetics and Redox State

Glaucoma is a multifactorial neurodegenerative illness requiring early diagnosis and strict monitoring of the disease progression. Current exams for diagnosis and prognosis are based on clinical examination, intraocular pressure (IOP) measurements, visual field tests, and optical coherence tomography (OCT). In this scenario, there is a critical unmet demand for glaucoma-related biomarkers to enhance clinical testing for early diagnosis and tracking of the disease's development. The introduction of validated biomarkers would allow for prompt intervention in the clinic to help with prognosis prediction and treatment response monitoring. This review aims to report the latest acquisitions on biomarkers in glaucoma, from imaging analysis to genetics and metabolic markers.

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.

Methods

We developed a Kalman filter (KF) with tonometry and perimetry data (KF-TP) and another KF with tonometry, perimetry, and global RNFL data (KF-TPO), comparing these models with one another and with 2 linear regression (LR) models for predicting mean deviation (MD) and pattern standard deviation values 36 months into the future for patients with OAG and glaucoma suspects. We also compared KF model performance when trained on individuals of European and African descent and tested on patients of the same versus the other race.

Main Outcome Measures

Predictive accuracy (percentage of MD values forecasted within the 95% repeatability interval) differences among the models.

Results

Among 362 eligible patients, the mean ± standard deviation age at baseline was 71.3 ± 10.4 years; 196 patients (54.1%) were women; 202 patients (55.8%) were of European descent, and 139 (38.4%) were of African descent. Among patients with OAG (n = 296), the predictive accuracy for 36 months in the future was higher for the KF models (73.5% for KF-TP, 71.2% for KF-TPO) than for the LR models (57.5%, 58.0%). Predictive accuracy did not differ significantly between KF-TP and KF-TPO (P = 0.20). If the races of the training and testing set patients were aligned (versus nonaligned), the mean absolute prediction error of future MD improved 0.39 dB for KF-TP and 0.48 dB for KF-TPO.

Conclusions

Adding global RNFL data to existing KFs minimally improved their predictive accuracy. Although KFs attained better predictive accuracy when the races of the training and testing sets were aligned, these improvements were modest. These findings will help to guide implementation of KFs in clinical practice.

The Effect of Achieving Target Intraocular Pressure on Visual Field Worsening

PURPOSE

To estimate the effect of achieving target intraocular pressure (IOP) values on visual field (VF) worsening in a treated clinical population.

DESIGN

Retrospective analysis of longitudinal data.

PARTICIPANTS

A total of 2852 eyes of 1688 patients with glaucoma-related diagnoses treated in a tertiary care practice. All included eyes had at least 5 reliable VF tests and 5 IOP measures on separate visits along with at least 1 target IOP defined by a clinician on the first or second visit.

METHODS

The primary dependent variable was the slope of the mean deviation (MD) over time (decibels [dB]/year). The primary independent variable was mean target difference (measured IOP - target IOP). We created simple linear regression models and mixed-effects linear models to study the relationship between MD slope and mean target difference for individual eyes. In the mixed-effects models, we included an interaction term to account for disease severity (mild/suspect, moderate, or advanced) and a spline term to account for the differing effects of achieving target IOP (target difference ≤0) and failing to achieve target IOP (target difference >0).

MAIN OUTCOME MEASURES

Rate of change in MD slope (changes in dB/year) per 1 mmHg change in target difference at different stages of glaucoma severity.

RESULTS

Across all eyes, a simple linear regression model demonstrated that a 1 mmHg increase in target difference had a -0.018 dB/year (confidence interval [CI], -0.026 to -0.011; P < 0.05) effect on MD slope. The mixed-effects model shows that eyes with moderate disease that fail to achieve their target IOP experience the largest effects, with a 1 mmHg increase in target difference resulting in a -0.119 dB/year (CI, -0.168 to -0.070; P < 0.05) worse MD slope. The effects of missing target IOP on VF worsening were more pronounced than the effect of absolute level of IOP on VF worsening, where a 1 mmHg increase in IOP had a -0.004 dB/year (CI, -0.011 to 0.003; P > 0.05) effect on the MD slope.

CONCLUSIONS

In treated patients, failing to achieve target IOP was associated with more rapid VF worsening. Eyes with moderate glaucoma experienced the greatest VF worsening from failing to achieve target IOP.