# Objetives

Develop a staging system for glaucoma and a predictive model to classify new patiens in the glaucoma stages using optical coherence tomography (OCT) data.

# Type of study

Analityc observational cross-sectional and comparative study.

# Inclusion criteria

Only left eyes were considered.

# Sample size

Total number of eyes: 1001 (766 Healthy and 235 with Glaucoma).

# Data preprocessing

It is known that the thickness of the retinal nerve fiber layers depends on age and BMO area. To avoid this dependency a linear transformation provided by the OCT company was applied to every variable. This linear transformations was obtained fitting a linear regression model on a normative group of healthy eyes.

$z_i=(x_i-\bar x-b_{xe}(e_i-\bar e)-b_{xa}(a_i-\bar a))/s_x$ where:

• $$x_i$$ is the value of variable $$x$$ on eye $$i$$.
• $$\bar x$$ is the mean of $$x$$.
• $$s_x$$ is the standard deviation of $$x$$.
• $$e_i$$ is the age of individual $$i$$.
• $$\bar e$$ is the mean of the age in the normative database of healthy eyes.
• $$b_{xe}$$ is the slope of the regression line of variable $$x$$ on the age.
• $$a_i$$ is the BMO area of eye $$i$$.
• $$\bar a$$ is the mean of the BMO area in the normative database of healthy eyes.
• $$b_{xa}$$ is the slope of the regression line of variable $$x$$ on the BMO area.
• $$z_i$$ is the standardized value of variable $$x$$ on eye $$i$$.

# Glosary

• OAG: Glaucoma de ángulo abierto
• OCT: Optical coherence tomography
• BMO: Bruch Membrane Opening