The Conclusion of the analysis was: No consistent statistically significant dependency between postoperative regression and the considered preoperative parameters could be detected. Stability of postoperative results after ReLEx SMILE is equally good for all included cases.

1. Breyer, Kaymak & Klabe Eye Surgery and Premium Eyes are Consulting, Study Center & MAB for:
Abott, Alcon, AlimeraSciences, Allergan, AMO, Bayer, Carl Zeiss Meditec, Ellex, Fluoron, Geuder, iOptics, LensAR, Medicem,
Novartis, Oculentis, Oertli, Revision Optics, Santen, Staar Surgical,
Sifi Medtech, Thea, Topcon, Visufarma, Ziemer
Impact of Percent Tissue Altered and Other Surgical and Corneal
Parameters on Regression after Refractive Lenticule Extraction
(ReLEx) SMILE.
Hakan Kaymak, D.R.H. Breyer, K. Klabe, P.R. Hagen, L. Beckers, F.T.A. Kretz, G.U. Auffarth
Breyer, Kaymak & Klabe Eye Surgery and Premium Eyes are Consulting, Study Center & MAB for:
Abott, Alcon, AlimeraSciences, Allergan, Alkahest, AMO, Bayer, Carl Zeiss Meditec, Ellex, Fluoron,
Gesundheitsamt Rhein-Neuss-Kreis, Geuder, Glaukos, Hangzhou Classon Tec, HOYA, iOptics,
KangHong Biotec., LensAR, Medicem, Novartis, Oculentis, Oertli, OMNITM, Optos, PharmaStulln,
PhysIOL, Revision Optics, Santen, Staar Surgical, Sifi Medtech, Teleon Surgical Optics, Thea, Topcon,
Visufarma, Ziemer

2. Regression after ReLEx SMILE
• So far: no relevant regression found in our over-all 5-year data
Examples: predictability and safety
Stable 5-year results also
reported in literature:
• Questions:
Are there any subgroups that display regression?
Which parameters could be relevant to regression?
-1.00
-0.75
-0.50
-0.25
0.00
0.25
0.50
0.75
1.00
1D
(n=171)
1W
(n=290)
1M
(n=810)
3M
(n=222)
6M
(n=176)
1Y
(n=272)
2Y
(n=91)
3Y
(n=50)
5Y
(n=26)
∆SE[D]
∆SE = SE(target)-SE(post)
-0.20
-0.10
0.00
0.10
0.20
1D
(n=171)
1W
(n=288)
1M
(n=808)
3M
(n=223)
6M
(n=174)
1Y
(n=271)
2Y
(n=89)
3Y
(n=50)
5Y
(n=26)
∆CDVA[logMAR]
∆CDVA=CDVA(post)-CDVA(pre)

3. Which Parameters could impact Regression after ReLEx SMILE?
• Corneal parameters
No cornea with preoperative ectasia, keratoconus, etc. but:
Do preoperative ectasia-related parameters influence the postoperative stability of the cornea and the
regression of the results?
Consider measured values from:
 Scheimpflug tomography including
Belin Ambrósio Enhanced Ectasia Display
(Pentacam HR, Oculus)
 Non-contact tonometer including
measurement of corneal deformation
(Corvis ST, Oculus)
• Surgical parameters
Does depth and thickness of the lenticule impact the postoperative stability of the cornea?
Consider all parameters as relative parameters (relative to the central corneal thickness)

4. Materials and Methods
• Inclusion criteria: consecutive ReLEx SMILE eyes
owithout prior ophthalmosurgical intervention and
owith existing data on
 Manifest refraction and/or
 Pentacam HR (& Corvis ST)
oat the following points in time:
 preoperative,
 T1 = 3 months postoperative as well as
 T2 = 1-5 years postoperative (as late as possible)
Number of eyes 389
Age [years] 33.6 ± 7.8
♀ : ♂ [%] 44 : 56
Spherical Equivalent (SE) [D] -4.34 ± 1.89
Cylinder [D] -0.66 ± 0.66
BCDVA [logMAR] -0.03 ± 0.05
CT [µm] 138 ± 13
LT [µm] 92 ± 29
CCT [µm] 548 ± 32
Target refraction SE [D] -0.06 ± 0.36
 Preoperative patient data

5. Linear Regression Model
• Age, corneal parameters
and surgical parameters:
• Regression parameters:
• Linear model:
• Approach for each regression parameter y_j :
Set coefficient m_ji with smallest signifance to zero until only statistically
significant (p<0.05) and uncorrelated quantities remain.

6. Linear Regression Model: Results
p-value
R²
SE‘ CDVA‘ K‘mean K‘max P‘min
age - - - - -
Df - - -
0.031
5%
-
Db -
0.030
2%
- - -
Dp - -
0.006
9%
- -
Dt - - - - -
Da - - - - -
BADD - - - - -
CBI - - - - -
TBI - - - - -
CTrel - - - - -
LTrel
0.002
4%
- - - -
PTA - - - - -
ETAF - - - - -
• Only 4 out of 65 coefficients statistically
are significantly different from 0:
Corresponds to 6.2% which is roughly
the expected rate of type 1 errors
(p<α=5%)
• No systematic behavior:
E.g.: SE‘ should display similar
dependencies as K‘mean
• Proportion of regression that could
be explained by model is always
smaller than 10%.

7. Linear Regression Model: Results
Example: Regression of SE
• Assumption:
Dependency of SE‘ of relative lenticule
thickness is real and not a statistical
artefact:
Increasing the relative lenticule thickness
by 0.05 on avarage induces a 5-year
regression of -0.25 D.
This mean value does only explain 4% of
the observed variation in the postoperative
regression.
y = -0.99x + 0.12
R² = 0.04
-1
-0.75
-0.5
-0.25
0
0.25
0.5
0.75
1
0.05 0.1 0.15 0.2 0.25 0.3 0.35
SE‘[D/Jahr]
LT/CCT

8. Summary and Outlook
Conclusion of the analysis:
• No consistent statistically significant depencency between postoperative regression and the considered
preoperativen parameters could be detected.
Stability of postoperative results after ReLEx SMILE is equally good for all included cases
Disadvantages of retrospective approach:
• No standard plan for follow up visits within 5 years interval
Only small proportion of all SMILE eyes with complete data set within this interval
• Corvis ST TBI software update was performed only ~1 year ago
Too little long term data for these patients so far
Further investigations:
• Collect more long term data (especially 5 year data)
• Examine influence of further quantities (axial length,…)
• How large is proportion of the variation in the postoerative „regression“ that originates
from measurement inaccuracy
• What does the same analysis yield for Femto-LASIK?