Journal of Refractive Surgery

Biomechanics Supplemental Data

Keratoconus Screening With Dynamic Biomechanical In Vivo Scheimpflug Analyses: A Proof-of-Concept Study

Johannes Steinberg, MD; Nazanin Eslami Amirabadi; Andreas Frings, MD; Juliane Mehlan, MD; Toam Katz, MD; Stephan J. Linke, MD

Abstract

PURPOSE:

This proof-of-concept study was designed to analyze the ability of in vivo biomechanical corneal analyses with the corneal visualization Scheimpflug technology (CorvisST; Oculus Optikgeräte, Wetzlar, Germany) to differentiate between normal eyes and eyes with manifest keratoconus after strictly eliminating the potential confounding factors intraocular pressure (IOP) and central corneal thickness (CCT).

METHODS:

In this retrospective, cross-sectional study, after pairwise matching for CCT and IOP, 29 normal eyes and 29 keratoconic eyes (one eye from each patient) were selected as study population. Older CorvisST parameters and the new Corvis Biomechanical Index (CBI), including several biomechanical and one tomographic parameter, as well as an adjusted CBI (aCBI) (including only biomechanical parameters), were compared regarding their discriminative ability between both groups.

RESULTS:

None of the CorvisST parameters of the former software version demonstrated statistically significant differences between normal and keratoconic eyes. On the other hand, the CBI and aCBI reached accuracies of 0.91 and 0.93, respectively, to discriminate between CCT- and IOP-matched normal and keratoconic eyes (CBI: [AUC/sensitivity/specificity]: 0.961/0.90/0.93; aCBI: [AUC/sensitivity/specificity]: 0.986/0.93/0.93).

CONCLUSIONS:

This study demonstrated that the concept of keratoconus screening with the CorvisST is effective in differentiating keratoconic from non-keratoconic eyes. The next steps will be testing the indices in subclinical keratoconus cases and hopefully combining biomechanical analyses with already established topography and tomography indices to further improve current keratoconus screening.

[J Refract Surg. 2017;33(11):773–778.]

Abstract

PURPOSE:

This proof-of-concept study was designed to analyze the ability of in vivo biomechanical corneal analyses with the corneal visualization Scheimpflug technology (CorvisST; Oculus Optikgeräte, Wetzlar, Germany) to differentiate between normal eyes and eyes with manifest keratoconus after strictly eliminating the potential confounding factors intraocular pressure (IOP) and central corneal thickness (CCT).

METHODS:

In this retrospective, cross-sectional study, after pairwise matching for CCT and IOP, 29 normal eyes and 29 keratoconic eyes (one eye from each patient) were selected as study population. Older CorvisST parameters and the new Corvis Biomechanical Index (CBI), including several biomechanical and one tomographic parameter, as well as an adjusted CBI (aCBI) (including only biomechanical parameters), were compared regarding their discriminative ability between both groups.

RESULTS:

None of the CorvisST parameters of the former software version demonstrated statistically significant differences between normal and keratoconic eyes. On the other hand, the CBI and aCBI reached accuracies of 0.91 and 0.93, respectively, to discriminate between CCT- and IOP-matched normal and keratoconic eyes (CBI: [AUC/sensitivity/specificity]: 0.961/0.90/0.93; aCBI: [AUC/sensitivity/specificity]: 0.986/0.93/0.93).

CONCLUSIONS:

This study demonstrated that the concept of keratoconus screening with the CorvisST is effective in differentiating keratoconic from non-keratoconic eyes. The next steps will be testing the indices in subclinical keratoconus cases and hopefully combining biomechanical analyses with already established topography and tomography indices to further improve current keratoconus screening.

[J Refract Surg. 2017;33(11):773–778.]

Identifying patients with ectatic predispositions is crucial in corneal refractive surgery.1,2 Further, a reliable and easy to handle screening device could enable screening programs for keratoconus in adolescents to facilitate early treatment of the otherwise sight-threatening disease.3,4 Despite noticeable improvements of topography and tomography analysis, keratoconus screening remains challenging in borderline cases.5 Most devices are expensive, large, and prone to interference and require a calm positioning of the patients for several seconds to enable good quality results, which can be difficult, especially in younger and developmentally delayed patients.

The corneal visualization Scheimpflug technology (CorvisST; Oculus Optikgeräte, Wetzlar, Germany) is a new device that enables non-contact, in vivo corneal biomechanical analyses. Theoretically, these analyses can help to gain further information to diagnose the ectatic pathology before clinical or topographical changes occur.6

Recently, we applied the CorvisST to differentiate between normal eyes and topographically unsuspicious fellow eyes of patients with keratoconus (ie, subclinical keratoconic eyes) with limited outcome.7 Since publishing our results, new CorvisST indices have been developed combining single parameters to improve the screening accuracy of the device. Further, new analyses have been implemented to better compensate for the main biasing factors intraocular pressure (IOP) and central corneal thickness (CCT).8,9 The current study was designed to differentiate between IOP- and CCT-matched manifest keratoconic and normal eyes using the Corvis Biomechanical Index (CBI). By excluding the main confounding factors for in vivo biomechanical analyses, this proof-of-concept study design analyzed the ability of the CBI to differentiate between groups based on the corneal characteristics, independently of the IOP and CCT.10–12 We additionally generated an adjusted pure biomechanical index (aCBI) by extracting the only tomographic parameter included in the CBI (Ambrósio's Relational Thickness to the horizontal profile [ARTh]) to analyze the extracted biomechanical discriminative power of the CBI.

Patients and Methods

This study was performed as part of a cooperation between the zentrumsehstärke Outpatient Clinic and Research Center, the Care Vision Eye Clinics, and the Department of Ophthalmology, University Medical Center Hamburg-Eppendorf. CorvisST analyses of keratoconic and normal eyes were retrospectively analyzed. Our study adhered to the tenets of the Declaration of Helsinki. Informed consent for retrospective data analysis and approval of the local ethics committee for the study were obtained.

The inclusion criteria for keratoconic eyes were defined as follows: a KISA% index greater than 100 with an inferior–superior difference of 1.40 diopters (D) or greater and a maximum keratometry value (Kmax) of greater than 47.00 D, as well as at least one of the following biomicroscopic keratoconus signs: Vogt striae, Fleischer ring, or stromal thinning. The respective inclusion criteria for our normal eyes were a KISA% index of less than 60% with an inferior–superior difference of less than 1.40 D, and a Kmax of 47.00 D or less.13,14 Further, all normal eyes displayed a maximum elevation of the posterior corneal surface at the thinnest point of less than 10 μm in both eyes corresponding with the results of a former study of our group analyzing the correlation of the KISA% index and Scheimpflug tomography in “normal,” “subclinical,” “keratoconus-suspect,” and “clinically manifest” keratoconic eyes.15

No history of (further) eye disease or ocular surgery was present. Normal and keratoconic eyes were selected for statistical analyses if there was an absence of contact lens wear for at least 3 (rigid contact lenses) or 2 (soft contact lenses) weeks. Only one eye of each patient was selected for statistical analysis.

The technique of the Pentacam and CorvisST analyses has been described in previous studies.16,17 The two strongest confounding factors for in vivo biomechanical analyses with the CorvisST are IOP and CCT.9,11 Therefore, we pairwise matched the included normal and keratoconic eyes regarding their CCT and corrected IOP (Dresdner correction) measured with the CorvisST before further analyses were conducted. The maximum CCT and IOP difference between the pairs were 9 μm (CCT) and 0.5 mm Hg (IOP), respectively. All Pentacam and CorvisST analyses presented “OK” in all automated quality tests and were personally reviewed by an experienced user of both devices (JS) before exporting to a spreadsheet program by the original software of the device and recalculating using the research software developed by Oculus Optikgeräte (version 1.3b1345).

The CBI was developed by Vinciguerra et al.8 and combines several biomechanical parameters and the corneal thickness profile in the temporal-nasal direction (ARTh) based on a logistic regression analysis. To analyze the biomechanical and tomographical components of the CBI, we also tested the CBI as an aCBI after extracting the ARTh as a single index.

A detailed description of the CBI is given in Table A (available in the online version of this article).

Explanation of the Corvis Biomechanical Index (CBI)a

Table A:

Explanation of the Corvis Biomechanical Index (CBI)

Statistical Analysis

For statistical analyses, the general purpose statistical software (STATA version 11.0; StataCorp LLC, College Station, TX) was applied.

We applied either the t test for two independent samples or the non-parametric Mann–Whitney U test depending on whether the assumption of normality regarding the distribution of sn examined variable within normal or keratoconic eyes was satisfied. Moreover, we used receiver operating characteristics (ROC) analysis to estimate the explanatory power of each variable measured by its corresponding area under the curve (AUC) value. The cut-off was defined where sensitivity and specificity were most similar (thus regarding both types of error equally important: misclassification of normal eyes into a group of keratoconic eyes or vice versa). A P value less than .05 was considered statistically significant.

Results

After matching for IOP and CCT, 29 normal eyes (normal group) and 29 manifest keratoconic eyes (keratoconus group) were included in the statistical analyses. The mean age of the normal and keratoconus groups was 36 ± 12 years (range: 21 to 52 years) and 33 ± 9 years (range: 15 to 49 years), respectively (P > .05). In the normal group, 19 women and 10 men were included. The keratoconus group was composed of 8 women and 21 men. Topography and tomography descriptive values of both groups are displayed in Table 1.

Topography and Tomography Parameters

Table 1:

Topography and Tomography Parameters

The topographic and tomographic parameters displayed highly significant differences between our normal and keratoconus groups, which was attributed to our inclusion and exclusion criteria. No significant differences could be demonstrated for the CCT and IOP.

Table 2 displays the descriptives and ROC curves of the CBI, ARTh, aCBI (CBI – ARTh), and CorvisST parameters of the former software version (version 6.07r08) displaying the highest accuracy to discriminate between normal and manifest keratoconic eyes. The ROC curves of the CBI, ARTh, and aCBI are displayed in Figure 1.

New CorvisST Indices and Parameters Derived From the Former CST Software Version

Table 2:

New CorvisST Indices and Parameters Derived From the Former CST Software Version

Display of the area under the curve (AUC) of the new Corvis Biomechanical Index (CBI), Ambrósio's Relational Thickness (ARTh), and the adjusted aCBI (ie, CBI after extraction of ARTh).

Figure 1.

Display of the area under the curve (AUC) of the new Corvis Biomechanical Index (CBI), Ambrósio's Relational Thickness (ARTh), and the adjusted aCBI (ie, CBI after extraction of ARTh).

Discussion

Our study was designed as a proof-of-concept study to analyze the ability of the CorvisST to differentiate between normal and keratoconic eyes on the basis of their corneal characteristics unbiased by the IOP and CCT. Because of inconsistent results in the past when it comes to early keratoconus screening with the CorvisST, we deliberately chose to compare normal and manifest keratoconic eyes. To enable as far as possible unbiased corneal analyses, normal and keratoconic eyes were matched in regard to the IOP and CCT, the main known biasing factors in corneal biomechanical non-contact analysis.

Whereas the single CorvisST parameters failed to differentiate between both groups in our IOP- and CCT-matched setting on a statistically significant level, the newly developed CBI, combining biomechanical and tomographical data, and the aCBI demonstrated a statistically significant difference between both groups with an accuracy of 0.91 (CBI) and 0.93 (aCBI).

Primarily constructed as a non-contact tonometry device to analyze the intraocular pressure with regard to the biomechanical characteristics of the cornea and its thickness, the CorvisST also enables analysis of the air-puff–induced oscillating cornea.17 It was hoped that this feature would identify eyes with subclinical keratoconus even before topographical and/or tomographical changes occur. Unfortunately, early studies in this area demonstrated diverse results.10,18,19 In our opinion, this is primarily caused by three different reasons. First, there is the heterogeneity of the applied keratoconus classification in modern keratoconus studies, a topic we already discussed in a former publication.15 Second, as technically advanced as the CorvisST is, building a good hardware is challenging and developing parameters that enable accurate analysis is probably an even greater challenge. As our results (Table 2) demonstrate, even the parameters provided with the former version were not able to pass our proof-of-concept test scenario comparing IOP- and CCT-matched eyes with advanced keratoconic and normal eyes on a statistically significant level. Third, regardless of the efforts to improve the CorvisST parameters, the challenge of bypassing the two major influencing factors of in vivo biomechanical corneal analysis (IOP and CCT) has proved to be difficult.11,12

In 2016, new studies with further insights and potential improvements of the CorvisST analyses were published. Joda et al.20 developed a promising method to correct IOP measurements obtained using the CorvisST for the effects of CCT and age by using numerical, finite element simulations of the CorvisST procedure applied on human eye models (biomechanically corrected IOP [bIOP]). Vinciguerra et al.9 subsequently implemented the bIOP in their retrospective study to generate normative CorvisST data and identify CorvisST parameters with robust characteristics to IOP and CCT. Whereas the approach of defining normative data in respect to IOP and CCT seems promising, none of their reported parameters was able to distinguish between normal eyes and keratoconic eyes on a statistically significant level in our proof-of-concept study design comparing IOP-and CCT-matched eyes.

The most recent development in CorvisST analyses is the CBI by Vinciguerra et al.8 To calculate the CBI, Vinciguerra et al. applied logistic regression analyses to combine several parameters with the aim of defining an index to discriminate between normal and advanced keratoconic eyes. Next to single CorvisST parameters, this new index includes a so-called “stiffness parameter” considering the bIOP and incorporates the pachymetric progression along the recorded Scheimpflug image captured by the CorvisST at the beginning of the measurement (ARTh). This is important because the ARTh is not a biomechanical parameter, but a tomographic characterization of the cornea. They rightfully justified the implementation of the ARTh because of the known importance of the corneal thickness as a potential biasing factor in biomechanical analyses. To create the CBI by using logistic regression, they started with the parameter with the highest discriminative ability (the ARTh). Using only the ARTh, they achieved an accuracy of 93.9%. By adding biomechanical parameters, they were able to improve the accuracy in their training data set to 98.2%. Then they tested the CBI in an independent dataset as a cross-validation to analyze whether the accuracy is robust outside their training dataset. They could then confirm their excellent findings (accuracy in the independent data set reached 98.8%) and concluded the usefulness of the CBI as an additional aid in keratoconus screening in everyday clinical practice. Vinciguerra et al.8 found a cut-off of 0.5 to be most effective to differentiate between both entities.

However, despite their detailed analyses and impressive findings, the potentially strong influence of the thinner cornea/different pachymetric progression in the eyes with advanced keratoconus (and the IOP distribution) might have led to, or at least might have influenced, their results. Unfortunately, the corneal thickness and thickness progression values of their normal and keratoconic eyes were not reported.8

We decided to perform a proof-of-concept study to test the accuracy of the single CorvisST parameter and the new CBI by comparing IOP- and CCT-matched normal eyes and advanced keratoconic eyes. Because we also wanted to analyze whether the CorvisST is able to differentiate between normal and keratoconic eyes based solely on their corneal biomechanical characteristics, we also modified the CBI by extracting the only tomographic parameter of this index (ARTh) and applied this aCBI in our test-scenario. Further, we tested the ability of the ARTh to differentiate between normal eyes and keratoconic eyes as a single parameter to analyze its influence within the CBI.

To ensure a device-independent classification of normal and keratoconic eyes, we used the KISA% index (topography-based classification system) to classify both keratoconic and normal eyes and added an elevation of the posterior corneal surface at the thinnest point of the cornea of less than 10 μm in both eyes of the normal patients as additional criteria to exclude eyes with normal topography but suspicious tomography.15

As displayed in Table 1, the difference between both groups was obvious regarding their topographic and tomographic characteristics. When interpreting our results, it is important to keep in mind that the study was not designed to improve the keratoconus screening above the current level of topographic and tomographic screening, but to answer the omnipresent question of whether the non-contact corneal analyses with the CorvisST can be used for keratoconus screening at all or to demonstrate its insufficiency because of their inability to compensate for IOP and CCT.

Taking all of this into consideration and analyzing our results displayed in Table 2 and Figure 1, we came to the following conclusions. First, the CorvisST demonstrated to be effective in differentiating between normal eyes and advanced keratoconic eyes unimpaired by the CCT and IOP.8 Second, our proof-of-concept study design probably falsely decreased the accuracy of the CBI by matching the corneal thickness of advanced keratoconic eyes and normal eyes. Vinciguerra et al.8 could demonstrate a much higher accuracy analyzing eyes in clinical practice in two different data sets. By applying the CBI in our study population, we incidentally conducted a cross-validation of their results. Considering this, it is interesting that we identified the same cut-off of 0.5 to be the most effective one in differentiating between our groups.

However, unfortunately, a legitimate comparison between Vinciguerra et al. and our results is not possible because of the different choice of inclusion and exclusion criteria. Third, as the aCBI analysis could demonstrate, the concept of solely biomechanical differentiation of keratoconic and normal eyes with the CorvisST proved to be successful. The even higher accuracy of the aCBI compared to the CBI should not be misinterpreted because the sensitivity/specificity acquired with our proof-of-concept study design is not eligible to be transferred to a “real-time” scenario. Fourth, the high accuracy of the ARTh as a single parameter supports the method of combining ARTh and the aCBI (as done in the original CBI) to improve the robustness of the parameter. As Vinciguerra et al. demonstrated, the CBI was able to demonstrate a high accuracy not only in their training data set, but also in an independent data set. Further studies have to show whether the strong influence of the ARTh on the CBI results might lead to a critical decrease of the accuracy when differentiating between normal eyes and eyes with early or even subclinical forme fruste keratoconus.

A potential limitation of our proof-of-concept study design is its tendency to be overcritical because of matching advanced keratoconic eyes and normal eyes regarding their CCT. We falsely negated the fact of decreasing biomechanical stability due to thinning cornea as a part of the natural pathogenesis of keratoconus. Otherwise, when not matching regarding the CCT, it is unknown whether the decreasing biomechanical parameters/indices are due to real destabilization of the cornea or only their thinning. Further, matching normal and keratoconic eyes in regard to their IOP influences the selection of the keratoconic eyes, because eyes with advanced altered biomechanics tend to (falsely) display a low IOP and are less likely to be included in our study. However, only manifest keratoconic eyes were selected with a CCT of greater than 500 μm and the median IOP of 13 mm Hg was within normal limits of non-keratoconic eyes. In general, because of our proof-of-concept study design, it is of utmost importance that reliability and cut-off values for the parameters of interest should not be used in the clinical setting until investigators have a better understanding of such indices in a larger sample size and eyes with mild and borderline keratoconus.

Our study demonstrated that, with the CBI, keratoconus screening with the non-contact in vivo CorvisST device in combination with tomographic parameters can be independent from the biasing factors IOP and CCT and the concept of biomechanical keratoconus identification proved to be successful. Further studies with earlier stages of keratoconic eyes have been initiated to test the CBI and ideally improve current topography- and tomography-based keratoconus screening protocols by combining them with biomechanical analyses.

References

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Topography and Tomography Parameters

ParameterNormal Eyes (n = 29)Manifest Keratoconic Eyes (n = 29)Pa


MedianQ25/Q75Min/MaxMedianQ25/Q75Min/Max
K1 (D)43.0042.00/44.0041.00/46.0044.0043.00/46.0038.00/48.00< .001
K2 (D)44.0043.00/45.0042.00/48.0048.0046.00/49.0043.00/53.00< .001
Kmax (D)45.0044.00/45.0042.00/47.0054.0051.00/56.0048.00/60.00< .001
KISA%32/50.6/18.5731325/1,46390/5,026< .001
Ele_b_TP (μm)53/70/105745/6418/119< .001
Ele_f_TP (μm)21/30/52419/319/42< .001
BAD_D1.30.8/1.60/37.76.1/9.73.5/14.4< .001
ARTmax409360/457287/556175147/ 21361/280< .001
IOP (mm Hg)1312/1410/181312/1410/181.00
CCT (μm)511502/531464/544509501/525457/540.264

New CorvisST Indices and Parameters Derived From the Former CST Software Version

ParameterNew CST SoftwareFormer CST Software


CBIaCBIARThaA1_DA (mm)A2_DA (mm)A1_DL (mm)
Normal eyes
  Median0.02814790.100.100.39
  Q25/Q750.00/0.0979/83371/5480.09/0.100.10/0.122.33/2.49
Manifest keratoconus
  Median1.0922040.110.122.51
  Q25/Q750.98/1.088/97165/2370.10/0.120.11/0.132.32/2.63
Pb< .001< .001< .001.06.96.018
  Cut-off0.5863470.100.122.45
  ROC-Area0.9610.9860.9550.840.800.66
  Sensitivity0.900.930.930.720.710.70
  Specificity0.930.930.930.760.700.69
  Accuracy0.910.930.930.740.710.71

Explanation of the Corvis Biomechanical Index (CBI)a

The CBI is calculated as a logistic regression: CBI = EXP (Beta) / (1+ EXP[Beta]).
EXP: exponential function with a base of 10
Beta: −59.487*A1Velocity (ms) – 0.027*ARTh – 0.092*Stiffness Parameter – 27.169*DARatioMax1mm + 5.472*DARatioMax2mm – 0.599*D_Def.Amp.Max+46.576
A1_Velocity [m/s]: velocity of the corneal apex at the first Applanation
Ambrósio Relational Thickness through the horizontal meridian (ARTh) is based on the thickness profile in the temporal-nasal direction as follows:

Corneal thickness is calculated at points with 0.2 mm spacing and the percentage thickness increase (PTI) is calculated at each point relative to the smallest value

The ratio between the percentage values (PTIs) and the corresponding normative values is calculated for each position along the complete thickness profile

The average ratio for all positions provides the Pachymetric Progression Index: a value higher than one indicates a faster thickness increase than usual and a lower value indicates a slower thickness increase toward the periphery than usual

The ratio between corneal thickness at the thinnest point and the Pachymetric Progression Index provides ARTh (ARTh = CT thinnest / Pachymetric Progression Index); a smaller value indicates a thinner cornea and/or a faster thickness increase toward the periphery

Stiffness parameter (SP): was developed, defined as resultant pressure (Pr) divided by deflection amplitude at time of first applanation: SP = (adj. AP1 – bIOP) / A1 Defl. Amp
Resultant pressure was calculated as the air pressure impinging on the cornea at the time of applanation minus bIOP1. This value (bIOP) is a new IOP value derived by finite element simulations that take central corneal thickness, age, and dynamic corneal response (into account. This value has been validated both experimentally and clinically. By dividing resultant pressure by A1 deflection amplitude, one divides a measure associated with load by a measure of out of plane deformation. The parameter is therefore directly associated with out-of-plane stiffness.
DA Ratio (1mm): maximum value of the ratio between deformation amplitude at the apex and at 1 mm (average between nasal and temporal)
DA Ratio (2mm): maximum value of the ratio between deformation amplitude at the apex and at 2 mm (average between nasal and temporal)
Def. Amp. Max [mm]: maximum corneal deformation amplitude
Authors

From zentrumsehstärke Outpatient Clinic and Research Center, Hamburg, Germany (JS, SJL); the Department of Ophthalmology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany (JS, NEA, JM, TK, SJL); Care-Vision Germany, University Medical Center Hamburg-Eppendorf, Hamburg, Germany (JS, TK, SJL); and the Department of Ophthalmology, University Hospital Düsseldorf, Heinrich-Heine University, Düsseldorf, Germany (AF).

The authors have no financial or proprietary interest in the materials presented herein.

The authors thank Vasyl Druchkiv for providing statistical assistance.

AUTHOR CONTRIBUTIONS

Study concept and design (JS, SJL); data collection (JS, NEA); analysis and interpretation of data (JS, NEA, AF, JM, TK); writing the manuscript (JS, NEA); critical revision of the manuscript (JS, AF, JM, TK, SJL); statistical expertise (AF); supervision (JS, SJL)

Correspondence: Johannes Steinberg, MD, zentrumsehstärke Outpatient Clinic and Research Center, Martinistrasse 64, 20251 Hamburg, Germany. E-mail: steinberg@zentrumsehstaerke.de

Received: February 11, 2017
Accepted: July 28, 2017

10.3928/1081597X-20170807-02

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