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

Analityc observational cross-sectional and comparative study.

Only left eyes were considered.

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

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 \(i\).

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