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Cor 1232 g dx presentation, aao 06

  1. 1. Guided Progression Analysis™ (GPA) for GDx
  2. 2. GPA TM Progression tools can help <ul><li>Helps address key clinical needs: </li></ul><ul><li>Identifying RNFL progression </li></ul><ul><li>Determining rate of progression </li></ul><ul><li>Assessing treatment effectiveness </li></ul>Why GPA TM Progression Analysis for GDx
  3. 3. <ul><li>GDx is ideal for measuring Progression. </li></ul><ul><li>Excellent reproducibility 1 </li></ul><ul><li>Operator independence 1 </li></ul><ul><li>Large images allow precise scan alignment </li></ul>1 Frenkel, Slonim, et al. Operator learning effect and interoperator reproducibility of the scanning laser polarimeter with variable corneal compensation. Ophthalmology . 2005 Feb;112(2):257-61 Why GPA TM Progression Analysis for GDx
  4. 4. = Possible Progression = Likely Progression (design specificity 95%) with clear, concise summary of Progression. Identify progressing patients Simulated data.
  5. 5. <ul><li>using three approaches for varying defect shapes </li></ul>Identify progressing patients Diffuse defects TSNIT Progression Graph Image Progression Map Simulated data. Deep, narrow focal defects Summary Parameter Chart Shallower, broader focal defects
  6. 6. Determine the rate of progression Simulated data. to identify patients at risk.
  7. 7. Assess treatment effectiveness Simulated data. by comparing rates before and after treatment. Before : -3.9 microns/yr ± 1.1 After : -1.8 microns/yr ± 1.1
  8. 8. <ul><li>Minimum cluster size is 150 pixels (2% of image area) </li></ul><ul><li>95% design specificity </li></ul>Simulated data. Image Progression Map: For deep, narrow focal defects
  9. 9. <ul><li>Divides ring around optic nerve into 64 segments </li></ul><ul><li>3 adjacent segments must show change. </li></ul><ul><li>95% design specificity </li></ul>Simulated data. TSNIT Progression Graph: For broader, shallow focal defects
  10. 10. <ul><li>Regression line drawn for Likely Progression and p<5% </li></ul><ul><li>95% design specificity </li></ul><ul><li>TSNIT Average, Superior Average, and Inferior Average </li></ul>Simulated data. Parameter Progression Charts: For diffuse defects
  11. 11. Courtesy of Robert N. Weinreb, MD Felipe A. Medeiros, MD, PhD Hamilton Glaucoma Center University of California at San Diego GDx Progression: Case Examples
  12. 12. <ul><li>Deep, narrow wedge defect (~10 degrees) </li></ul><ul><li>Only identified by Image Progression Map </li></ul>Data presented using research format. Example 1: deep, narrow focal defect
  13. 13. <ul><li>Shallow inferior defect </li></ul><ul><li>Only identified by TSNIT Progression Graph </li></ul>Data presented using research format. Example 2: shallow, broad focal defect
  14. 14. <ul><li>Inferior defect </li></ul><ul><li>Sufficient area and depth to be identified by all three approaches </li></ul>Data presented using research format. Example 3: larger defect identified by all
  15. 15. GPA TM tools integrate your structure and function analysis by using consistent terms and rules. Extending GPA TM across structure and function
  16. 16. GPA TM Progression for GDx <ul><li>GPA ™ Progression Analysis for GDx is a comprehensive, simple tool to help you: </li></ul><ul><li>Make more confident treatment decisions </li></ul><ul><li>Effectively educate patients </li></ul>Summary