This document describes a logistic regression model developed to distinguish keratoconic eyes from normal eyes based on analysis of Orbscan topography parameters. The model uses maximum posterior elevation, anterior elevation to best fit sphere ratio, and inferior-superior difference in 3mm keratometry values. When applied to 207 normal eyes and 42 keratoconic eyes, the model achieved 99% accuracy with high sensitivity and specificity. The authors conclude the model may help detect subtle corneal abnormalities and risk of ectasia.

1. Logistic regression model for distinguishingLogistic regression model for distinguishing
keratoconus eyes based on analysis ofkeratoconus eyes based on analysis of
Orbscan parametersOrbscan parameters
Yuri Oleynikov MD,PhDYuri Oleynikov MD,PhD
Fellow – Cornea & RefractiveFellow – Cornea & Refractive
SurgerySurgery
Baris Sonmez, M.D.Baris Sonmez, M.D.
International Fellow – Cornea & RefractiveInternational Fellow – Cornea & Refractive
SurgerySurgery
D. Rex Hamilton, M.D., M.S.D. Rex Hamilton, M.D., M.S.
Director, UCLA Laser Refractive CenterDirector, UCLA Laser Refractive Center
Assistant Professor of OphthalmologyAssistant Professor of Ophthalmology
Presenters have no financial interest in the technology presented

2. Introduction
• Analysis of corneal morphology is
essential to identify corneas at risk
for post-LASIK ectasia
• Obvious keratoconus is detectable
at the slit lamp
• Forme fruste keratoconus (FFKCN)
can be subtle and easily missed on
topography
• Quantitative analysis may assist the
refractive surgeon in identifying
subtle cases of FFKCN

3. Purpose
• To quantitatively evaluate differences in
corneal shape of normal and KCN eyes
using Orbscan IIz slit-beam topography
system.
• To formulate a reliable model
distinguishing normal from KCN corneal
morphology.
• Retrospective study of 207 normal eyes of 108
patients who presented for refractive surgery
evaluation and 42 eyes of 24 patients with KCN
• Keratoconus was defined based on the
Collaborative Longitudinal Evaluation of KCN
clinical findings

4. Methods
Multiple parameters were recorded:
1. amount and axis of astigmatism
2. central corneal power
3. irregularity indices at 3 and 5mm zones
4. location and magnitude of maximal
posterior elevation
5. magnitude of maximal central anterior
elevation
6. location and magnitude of thinnest
optical pachymetry
7. anterior and posterior best-fit sphere
8. keratometry values at 3 and 5mm zones
at 30, 60, 90, 120, 150, 210, 240, 270,
300, and 330 degrees on both
keratometric and tangential topographic
maps
9. Skewing of radial axis (SRAX) at 3 and
5mm zones.

5. Methods
1. anterior elevation / best fit sphere radius
ratio
2. I-S (inferior-superior) difference, defined
as the average superior (30, 60, 90, 120,
and 150 degrees) subtracted from the
average inferior (210, 240, 270, 300,
and 330 degrees) keratometry values for
both the keratometric and tangential
topographic maps

6. Patient characteristics
* Chi-square test.
† T-test.
‡ Age was available for 106 normal patients.
§ Spherical equivalent was available for 38 keratoconic eyes.
0.755†-3.82 + 4.95§-3.56 + 2.85Spherical equivalent
42207Number of eyes
0.413†41 + 1039 + 11‡Age (years)
0.026*18 (75%)54 (50%)Gender: Male
24108Number of patients
P-valueKeratoconusNormalCharacteristics

7. <0.000181.0 + 38.339.5 + 42.6SRAX 5mm
<0.000164.5 + 41.834.6 + 39.1SRAX 3mm
<0.00013.94 + 3.060.67 + 1.00IS-T 5mm
<0.00016.56 + 6.160.41 + 0.64IS-T 3mm
<0.00014.14 + 3.830.34 + 0.42IS-K 5mm
<0.00013.24 + 3.360.18 + 0.32IS-K 3mm
<0.00010.94 + 0.470.27 + 0.08AER
<0.000141.6 + 21.911.6 + 3.5AE
<0.00010.81 + 0.020.83 + 0.02Ratio ABFS/PBFS
<0.000154.2 + 3.051.9 + 2.1PBFS
0.00244.0 + 1.943.0 + 1.3ABFS
0.0210.85 + 0.320.71 + 0.45TOP radius
0.203216 + 52204 + 78TOP meridian
<0.0001472 + 57548 +35TOP magnitude
0.9001.11 + 0.411.12 + 0.75MPE radius
0.0005238 + 35210 + 84MPE meridian
<0.000181.2 + 36.128.2 + 7.1MPE magnitude
<0.00014.50 + 2.441.33 + 0.36Irregularity index 5mm
<0.00014.20 + 2.151.04 + 0.33Irregularity index 3mm
<0.000149.2 + 5.543.4 + 6.9Central corneal power
<0.00013.62 + 2.601.02 + 0.85Amount of astigmatism
P-value*Keratoconic eyes(N=42)Normal eyes(N=207)Orbscan indices
* T-tests
Orbscan IIz quantitative indices between normal and
keratoconic eyes

8. Multiple logistic regression modelThe multiple logistic regression model for distinguishing KCN eyes from normal controls
(MPE (p=0.030), AER (p=0.050), IS-K 3mm (p=0.015), and the area under ROC curve = 0.99)
Pr(KCN) = 1 / (1 + exp(21.4177 - 0.1474*MPE - 25.4821*AER - 5.1761*IS-K 3mm))
where Pr(KCN) is the predicted probability of KCN,
MPE = Maximum posterior elevation, measured as the highest elevation of posterior float
over best-fit sphere.
AER = Ratio of highest anterior elevation to the anterior best fit sphere in diopters
IS-K 3mm is inferior-superior K difference ratio.
Area under ROC is 99% - high sensitivity and specificity.
96.896.110019980420.02
99.299.597.620611410.20
99.610097.620701410.45
NegativePositive*NegativePositive*
ACSPSE
Normal controls (N=207)KCN patients (N=42)Cut-off values for
Pr(KCN)
Pr(KCN) = Predicted probability of being KCN using logistic regression models shown above;
SE = Sensitivity = # of KCN eyes with positive results / # of KCN eyes;
SP = Specificity = # of normal control eyes with negative results / # of normal control eyes;
AC = Total accuracy = (# of KCN eyes with positive results + # of normal control eyes with negative results) / all eyes.
* Positive means Pr(KCN) greater than the cut-off value.
Group separation results based on several cut-off points of Pr(KCN):Group separation results based on several cut-off points of Pr(KCN):

9. Normal cornea
AER=0.289
MPE=19 um
IS-K=0.09
PrKCN = 0.021%

10. Keratoconic cornea
AER=1.58
MPE=142um
IS-K=6.21
PrKCN = 100%

11. Analysis of atypical topography
AER=0.36
MPE=28
IS-K=0.87
PrKCN = 2.5506%

12. Conclusion
• A logistical regression model using Orbscan
parameters may be useful in detecting abnormal
corneal morphology
• Our cut-off value of 0.02 was highly sensitive and
specific for distinguishing normal from abnormal
morphology
• Further training of the model using larger data
sets and forme fruste keratoconus eyes may
allow for improved detection of subtle corneal
morphologic abnormalities