Change detection based on an individual patient s variability
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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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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

Department of Ophthalmology

Dalhousie University, Canada


Can theory become practice

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!


Frequency of seeing fos curve

Frequency of Seeing (FOS) Curve


Frequency of seeing fos curve1

Frequency of Seeing (FOS) Curve


Frequency of seeing fos curve2

Frequency of Seeing (FOS) Curve


Variability and thresholds

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

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%


Full threshold stair start 25 db

Full Threshold (stair start = 25 dB)


Method

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)


Change detection based on an individual patient s variability

GCP

IPoC

Visit

3

Visit

4

Visit

5


Change detection based on an individual patient s variability

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


Conclusion

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

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


Plr at visit 4

PLR at visit 4

Slope = -1 p = 0.487


Change detection based on an individual patient s variability

Weighted PLR

  • Black is high probability of true threshold given all previous measured thresholds, FOS and algorithm details

  • (Not simple probability distributions from before)


Change detection based on an individual patient s variability

WLR at visit 5

Slope = -1.4783 p < 0.00001


Change detection based on an individual patient s variability

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


Change detection based on an individual patient s variability

Slope-Threshold Relationship

Flat

Grey area is 95% CI from population data Henson et al IOVS 2000

Steep


Change detection based on an individual patient s variability

Slope-Threshold Relationship

Flat

Steep


Change detection based on an individual patient s variability

Slope-Threshold Relationship

Flat

Steep


Change detection based on an individual patient s variability

Patient Data

FOS measured using a short MOCS at the 6 red locations


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