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

Can Theory Become Practice? In theory we know how to customise change probability maps for individuals Turpin & 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!

Frequency of Seeing (FOS) Curve

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

What are the outcomes? % Seen Not Seen 67.24% 60% 36.00% % 100% Seen 40% 85% Not Seen 18.00% 34.00%

Full Threshold (stair start = 25 dB)

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)

Visit 3 Visit 4 Visit 5 GCPIPoC

1015 GCP: 4 loc, 3-of IPoC: 2 loc, 2-of-2 78 Number of visits to detect progression GCP only IPoC only Both No flagged per field GCP: 4 loc, 2-of

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

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

PLR at visit 4 Slope = -1 p = 0.487

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

WLR at visit 5 Slope = p <

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

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

Slope-Threshold Relationship Flat Steep

Slope-Threshold Relationship Flat Steep

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