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CHANGE DETECTION BASED ON AN INDIVIDUAL PATIENT’S VARIABILITY

CHANGE DETECTION BASED ON AN INDIVIDUAL PATIENT’S VARIABILITY. Allison McKendrick Department of Optometry and Vision Science University of Melbourne. Andrew Turpin School of Computer Science and Information Technology RMIT University, Melbourne. Balwantray Chauhan

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CHANGE DETECTION BASED ON AN INDIVIDUAL PATIENT’S VARIABILITY

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  1. CHANGE DETECTION BASED ON AN INDIVIDUAL PATIENT’S VARIABILITY Allison McKendrick Department of Optometry and Vision Science University of Melbourne Andrew Turpin School of Computer Science and Information Technology RMIT University, Melbourne Balwantray Chauhan Department of Ophthalmology Dalhousie University, Canada

  2. Can Theory Become Practice? • In theory we know how to customise change probability maps for individualsTurpin & McKendrick, Vis Res 45, Nov 2005 • How well does it work in practice? • The method relies on measuring FOS curves at baseline in some number of locations (is this clinically viable)? • Where do we get a longitudinal dataset that has FOS at baseline…Bal!

  3. Frequency of Seeing (FOS) Curve

  4. Frequency of Seeing (FOS) Curve

  5. Frequency of Seeing (FOS) Curve

  6. Variability and Thresholds • Flat FOS curve means less certain responses, wider range of outcomes on a perimeter • Steep FOS curve, more certain, smaller number of outcomes on a perimeter

  7. What are the outcomes? Not Seen 28 32 Seen 30 67.24% 36.00% 82% 82% 60% 60% Not Seen 24 Seen 26 18.00% 34.00% 100% 18% 85% 40%

  8. Full Threshold (stair start = 25 dB)

  9. Method • Given 2 baseline fields and 6 FOS per patient • Compute slope-threshold relationship • Compute individual probability distributions per location • Event based • Flag any locations that fall outside that 95% CI of the probability distribution, compare with GCP • Trend based • Use probability distributions (plus a bit of maths) as weights in linear regression, compare with PLR • (No time to discuss in this talk)

  10. GCP IPoC Visit 3 Visit 4 Visit 5

  11. GCP only IPoC only Both No flagged per field GCP: 4 loc, 3-of-3 10 15 IPoC: 2 loc, 2-of-2 4 7 8 8 9 11 GCP: 4 loc, 2-of-3 5 9 9 7 4 Number of visits to detect progression

  12. Conclusion • IPoC event based flagging makes good use of FoS • Flags many less points • Agrees with GCP definition of progression • IPoC still relies on a definition of baseline • Learning effects will hurt, just as for GCP • Does FoS slope change over time? • IPoC still at the mercy of unreliable thresholding algorithms and/or false responses

  13. Trend based - PLR Slope = 0.1818 p = 0.682 For progression, slope < -1 and p < 0.01 using 3-omitting scheme Gardiner & Crabb, IOVS 43, 2002

  14. PLR at visit 4 Slope = -1 p = 0.487

  15. Weighted PLR • Black is high probability of true threshold given all previous measured thresholds, FOS and algorithm details • (Not simple probability distributions from before)

  16. WLR at visit 5 Slope = -1.4783 p < 0.00001

  17. Summary • WLR flags at least one location in every patient as progressing (slope < -1, p < 1%) at Visit 4 • Full Threshold is too noisy to establish baseline after 2 visits (shown in our Vision Research paper) • Could use different criteria (eg at least 2 locations) • Just need more data, or less noise, otherwise classification subject to arbitrary criteria and errors

  18. Slope-Threshold Relationship Flat Grey area is 95% CI from population data Henson et al IOVS 2000 Steep

  19. Slope-Threshold Relationship Flat Steep

  20. Slope-Threshold Relationship Flat Steep

  21. Patient Data FOS measured using a short MOCS at the 6 red locations

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