Disclosure 2/1/2012

2/1/2012
Disclosure
y Equipment support Heidelberg engineering
Pinakin Gunvant Davey OD, PhD, FAAO
Associate Professor, Western University of Health Sciences
Adjunct Associate Professor, University of Louisville
Adjunct Associate Professor, University of Memphis
Why visual fields lecture
Outline of the lecture
y Old technology‐ going through lot of changes
y ABC’s of perimetry
y ABC’s specific to Humphrey Field Analyzer
y Single Field Analysis‐ Humphrey and Octopus
y Staging of Visual field
y Progression and visual fields
y Targeting specific ganglion cells –M or K‐cells
Visual fields background
Do we still need visual fields in glaucoma management ???
y Octopus was the first automated visual
field analyzer.
y Humphrey Field Analyzer is the accepted
universal “gold standard”
y Are some perimeters superior than other?
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Automated static perimetry
y Why automate perimetry?
Hill of Vision:
y Drawback?
Central vision greatest
Vision decreases as one moves away from center to periphery.
away from center to periphery
g
y Advantages?
Low threshold = high sensitivity = weak stimulus (dim light) is also seen.
High threshold = low sensitivity = strong stimulus (bright light) is needed to be detected or seen.
Best perimetry
Other points
y If threshold is performed in all possible locations that y Color of stimulus
can see in retina but it is not possible.
y Attentiveness y Moving versus non‐moving
y Moving targets are more visible than non moving targets (Riddoch phenomenon)
y Duration of stimulus (1/100 of second compared to 2/100 of a second)
y Beyond a certain critical period duration of stimulus has little effect on visibility (approx 1/3 second)
Units of light intensity
y Apostilib (asb) european unit for luminance is the standard for perimetry for a long time
y Absolute unit does not change from one perimeter to y When dimmer light is required a neutral density filter is used to dim the light.
y Decibel (dB) is the logarithmic unit that is used for another
y HVF max luminosity (intensity) 10,000 asb
y 10 dB stimulus = 1/10 as intense of max stimulus
y Goldmann 1000 asb
y 20 dB stimulus = 1/100 as intense of max stimulus y Octopus (original) 1000 asb, all new ones 10,000 asb
y 30 dB stimulus = 1/1000 as intense of max stimulus
convenience
y 40 dB stimulus = 1/10,000 as intense of max stimulus
y So if all perimeters reported Apostilib for all points tested it will be great!
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Threshold
y Threshold is defined as the intensity that is just marginally visible.
y An infrathreshold (weaker stimuli)for a point will not be visible
y A suprathreshold stronger stimuli Reasons for test retest differences?
y If measured multiple number of times there are bound to be slight variations.
y This is the test retest variability or short term fluctuation.
y Because physiologic frequency‐of‐seeing curve has a some short term fluctuations
y Locations with reduced sensitivity has greater or broader seeing curve.
y Patients responses are not the most reliable (inattentiveness or inconsistent).
y Fatigue has a role to play.
Troxler’s effect
y When one fixates a particular point, after about 20 seconds or so, a stimulus away from the fixation point, in peripheral vision, will fade away and disappears.
y The effect is enhanced if the stimulus is small, is of low contrast or equiluminant, or is blurred.
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Sources of error
y Miosis: decreases threshold peripherally, increases variability centrally
y Lens opacities
y Uncorrected refractive error –decrease in contrast U
t d f ti d
i t t sensitivity
y Spectacles – lens artefact
y Ptosis
y Inadequate adaptation: if VF performed soon after ophthalmoscopy
Why do we need perimetry?
y To diagnose if there is problem (diagnosis)
y Once diagnosed to see if the intervention doing its job (progression)
SITA
Visual field modeling
y Factors that contribute to time saving
y Visual field modeling
y Information index to determine threshold endpoints
y Test paced to patients needs
T d i
d
y Post test recomputation of threshold values
y Reduction in “catch trials “ needed to determine reliability indices
y Starts with a prior probability models of normal and abnormal fields
y This model is y age corrected normative data of normals and glaucoma d i d f l d l
patients
y Frequency of seeing curves around threshold
y Correlations between adjuscent test points
y Testing is adjusted continuously based on the patient responses.
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Reaction time
Fixation monitoring‐ in HFA
y SITA determines continuously the average and y Perimetrist observation
standard deviation of time required to respond and alters the duration between two stimuli.
y Heijl‐Krakau blind spot method
y Gaze monitor
y This speeds up test for fast responder and slows it down if responses are slower.
y Most importantly the “patient runs the test not vice versa” Heijl‐Krakau blind spot method
Problems with blind spot
y A bright stimulus is y Head tilt during the test.
presented where the blind spot is identified
y 5% of the stimulus presented are used to check for fixation
y Depends on the accuracy of the presumed location of blind spot
y Other eye not patched The gaze monitor
Schematic of gaze tracking
properly
y Remember size of R
b i f stimulus is not an issue!
y Uses two image analysis method to loactate center of pupil.
y Infrared light source to get corneal reflexes
y Gaze monitor initialization at the beginning of the test G i i i i li i h b i i f h is used for calibration and adjusting the system for individual patient.
y Patient has to look at the fixation and not blink.
y Procedure takes approximately 20 seconds.
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Examples of gaze tracking
Gaze monitor cont…2
y Upward line full (max length) represents a fixation error of 10 degrees
y Downward line represents a situation when information on the position of eye is not available y Blinks y Gaze tracking is not recorded when stimulus is not presented.
Examples of gaze tracking
Examples of gaze tracking ‐2
Octopus Features:
Fixation Control
Octopus Features:
Auto Eye Tracking
True Fixation Control
Correct
fixation
Fixation
lost
Correct fixation
y No stimuli during fixation loss
y Automatic repetition of stimuli after blinking or darting
y Most accurate test possible
Eye movement
Automatic
readjustment
™ The perimeter centers the patient automaticaly to the optical axis ™ Less interrupts, less time to finish
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Catch trials‐ False positive
False positives issues
y A response when no stimulus was presented
y Poorly instructed patient
y Trigger happy patient
y Expectancy of a stimulus to be present
y Older models made noise but no stimulus was y A noise of shutter in the machine may be perceived as presented
d
y SITA des not measure false positive but calculates false positives
y Trigger happy patient
a stimulus presentation
i l i
y “Education is the key” in threshold tests theoretically 50% of the stimulus presented should not be seen!!!
False negative error
Acceptable catch trial rate
y Proportion of visible stimuli to which the patients fail to respond.
y Stimuli are presented at a location 9 dB brighter than previously “seen level” to which the patient response is previously seen level to which the patient response is “cannot see”
y Research studies varies on y SITA acceptable rates are
y <20% Fixation loss
% Fi ti l
y < 20% false positives
y <20% false negatives
their criterion of acceptable levels
y Generally
y <15% Fixation loss
y < 15% false positives
y <15% false negatives
Other important consideration
Mydiriatics and pupil size
y Mydriatics and pupil size
y Hippus: No effect as both light intensity of stimulus y Refractive error
y Testing environment
and background changes with hippus
y Retinal adaptation: Amount of light reaching the retina hence not totally independent of pupil
retina, hence not totally independent of pupil
y Pupil size also determines which part of the crystalline lens is used.
y With cataract the scatter increases
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To dilate or not to dilate!
Refractive correction
y Best not to dilate.
y Humphrey normative data was collected with undilated pupil
y Exceptions cataract that severely affects vision
y Less than 1 D best ignored.
y Patients that under the effect of cycloplegia will need add HFA II is 3o cms
y Consistency in pupil size is most important so if patient examined dilated best to always dilate
Testing environment
y Low to dark environment
y Machine does indicate if room illumination is too bright
y During calibration (turning the machine on) room D i lib i (
i h hi ) should be the same illumination that is used for the testing.
Example of VF
Parts of VF
HFA details
Octopus details
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Grey‐scale
Grey scale
y To be looked at for 2‐3 seconds not more
y A overall idea of the field.
field
y Not all points showed on the field are tested.
y The interpolation of data is done Grey scale‐ HFA Grey scale‐ Octopus
•The grey scale should be interpreted very carefully
•The grey scale is represented in gradations of 5 dB and range from 1‐40 dB
Options to have either color or pure grey scale
Threshold sensitivity values –
raw numerical data
Raw data‐1
y Very important to look at.
y Non manipulated or least manipulated data; so patients “true” response.
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Raw data‐2
Raw data‐3 y Centrally one expects a threshold of lower 30’s dB generally (rule of thumb).
y As seen before threshold data y Peripherally one expects a threshold of upper 20’s dB is greater centrally and lesser in the periphery
y 40 dB is the most that a trained observer in a laboratory can see
y If any threshold is seen greater than 40 in central is probably due to “trigger happy patient”
generally (rule of thumb)
Total deviation plot
Total deviation plot
Octopus
HFA
Probability plot Threshold deviation
y Difference between each point of patients threshold and median age matched normal values.
y A rule of thumb 5 dB lesser values than age matched normal should be viewed “suspicious”
Pattern Deviation
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Pattern deviation numerical plot
Global Indices y To expose localized defects which may be masked by
y elevated hill of vision (some locations of abnormally high sensitivity)
y Generalized depression (cataract)
Mean deviation
HFA
Octopus
Mean deviation
mean defect
Pattern standard
deviation
Loss of variance
Mean deviation cont…2
y Mean deviation which is an index of average severity is y Measure of entire visual field loss (elevation or depression)
y Average of severity of loss
affected by
y Degree of loss
y Number of locations
N b f l ti
y A positive number indicates an average sensitivity is above‐
average normal for age
y A negative number indicates an average sensitivity is below‐
average normal value for age Pattern Standard Deviation
Glaucoma hemifield test zones
y Degree to which the total deviation plot points are not similar to each other
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5
y Pattern standard deviation quantifies localized loss as a single value
y Measure of focal loss or variability within the field 2
3
1
y Takes into account generalized depression of field 1
3
2
4
5
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Results of Glaucoma hemifield test (GHT)
Glaucoma hemifield test
y Plain language analysis
4
paths of nerve fiber bundle test fine tuned bundle‐
for glaucoma not neurologic fields
y Test zones in superior hemifield compared to inferior hemifield
5
y Outside normal limits (p<0.01)
y Borderline (p<0.03)
y Chooses points along the 2
y Generalized reduction in sensitivity (low sensitivity 3
1
p<0.05)
p<0 05)
1
y Abnormally high sensitivity (high sensitivity p<0.05)
3
2
4
y Within normal limits
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Bebie curve
Diffuse defect – DD
Local defects – LD
Detection of false positives
Why is staging important?
y Treatment issues
y Management issues
y Prognosis
y Research 12
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Staging based on MD
y Better than‐6 db‐ Mild
y Worse than ‐6.o dB but better than ‐12 dB – Moderate
y Worse than ‐12.0 dB severe Criteria for glaucomatous damage
y GHT outside normal limits in at least two occasions
y A cluster of three or more non‐edge points (pattern deviation plot) all of which are depressed at a p<5% and one of which is depressed at a p<1% on two occasions (respecting horizontal meredian)
y PSD < 5% of normal individuals
y This criterion was written for 30‐2, if 24‐2 field is analyzed edge points are included.
Progression Guided Progression analysis
y Consensus is limited
y Early changes
y Paracentral
y Nasal step
N l y Arcuate defects
y Enargement of scotomas
y Deepening of scotomas
y More than 4 point change in AGIS (later on considered too much)
y More advanced statistical analysis
y Makes clinical sense
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Guided progression Analysis cont…2 Guided progression Analysis cont…3 y Baseline:
y 30‐2 and 24‐2 can be used. If 24‐2 is the follow‐up y First two tests (automatic) are needed and average to make the baseline y If you don’t want to use the first two tests you can If d ’ h fi manually chose other tests
fields then all field reports are used as 24‐2 (extra points of 30‐2 is not used)
y For example: Learning curve, poor test taker
y Ocular intervention like, High IOP which was treated during first test
GPA cont …5
GPA cont ‐6
y Example of additional y Symbols used
y Open triangle p<5%
y Half filled p<5% two occasions y Solid triangle p<5% three occasions
y X out of range
information with your single field printout
y Possible progression –
three or more points show change at least two consecutive tests
y Likely progression –
three or more points show change in at least three consecutive tests
Visual Field Index
y Percentage of normal age adjusted field
y Greater the number more normal
y Trend over time is given with a probability values as well
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Cluster analysis
Why cluster analysis?
Trend analysis
•Individual points may vary
•Overall clusters are more stable
•Also close representation to various bundles of RNFL
•So in some respect better structure function relationship.
Global rate of progression
Global trends
Color codes
Worsening at the 5%
Improvement at the 5%
Fluctuation at the 5%
,1%
level
,1%
,1%
level
level
Scale
Grey:
Progression rate
Normality
15dB: Seriously impaired vision
25dB: Considered legally blind
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Polar graph
y M cells 10%
y P cells 80%
y K cells 9%
y This may explain why selective targeting of ganglion cells may be ideal method
y Low spatial frequency sinusoidal grating
greater than 15 Hz
counterphase flicker
y Counterphase flicker
y It appears to have twice I h i number of black and white bars
Nonlinear
Response
}
1 cycle/ degree or less
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FDT Matrix and similarly magno
cell specific perimetry
Stimulus - Flicker Defined Form (FDF)
Contour‐Illusion Stimulus
Phase 1
+
Phase 2
=
Illusory “Edge” or
Contour
y Is shown to detect glaucomatous damage early
y Why is it not used more often?
y Data cannot be interchanged!
+
=
SWAP y High luminance (100 cd/m2 )uniform yellow background
y Blue target size V
y These isolates koniocellular layer (blue cone system)
Th i l
k i ll l l
(bl )
SWAP
SWAP
y More difficult test
y Short Wavelength Automated Perimetry
y Requires greater learning time
y Isolates and measures the sensitivity of the short‐
y SITA options are available y Clinically useful in a good visual field taker
y Reported to detect glaucoma earlier than standard wavelength‐sensitive (blue‐sensitive) visual pathways by presenting a large blue stimuli on a bright yellow background.
automated perimetry
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SWAP (cont 2)
SWAP Deficits
y The bright yellow background:
y Are more prevalent in high‐risk ocular hypertensives; y DEPRESSES the sensitivity of the middle (green) and less prevalent in medium to low‐risk ocular hypertensives
long (red) wavelength mechanisms
y Precede visual field loss for SAP; but, are predictive of y PERMITS the sensitivity of the short wavelength future deficits for SAP
sensitive mechanisms to be evaluated
SWAP Deficits (cont 2)
Disadvantages of SWAP
y Are more extensive than those found for SAP
y Short‐wavelength sensitivity is reduced by:
y Age‐related lens yellowing
y Macular pigment
y Cataract
C
y Other ocular media opacities
y SWAP has greater inter‐ and intraindividual variation y Progress at a higher rate than for SAP losses
y Are correlated with structural abnormalities of the optic disc
compared to SAP
Advantages of SWAP
Global Summary
y SWAP is more sensitive to change than standard y White on white perimetry is till gold standard in perimetry. Progression is identified 1‐3 years earlier.
perimetry in glaucoma diagnosis and progression detection
y Newer perimetry techniques give additional insights into pathogenesis in glaucoma
y Newer algorithms help make structure function relationships become clearer and more clinically useful.
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