Ashraf M. Mahmoud

Screening of Forme Fruste Keratoconus with the Ocular Response Analyzer

PURPOSE To evaluate the performance of the Ocular Response Analyzer (ORA) in the screening of forme fruste keratoconus (FFKc). METHODS A retrospective comparative study was conducted involving 180 eyes. ORA preoperative data were analyzed for 125 normal control eyes (64 patients) undergoing laser in situ keratomileusis (LASIK) without corneal ectasia after 24 months of follow-up and 55 case eyes with unilateral keratoconus from a database (BCVA of 1.0, KISA index <60%). All eyes were matched in four groups of central corneal thickness (CCT): group 1, <500 microm; group 2, 500 to 539 microm; group 3, 540 to 579 microm; and group 4 >580 microm. Corneal hysteresis (CH), the corneal resistance factor (CRF), the air pressure curve, and the infrared signal were compared between FFKc and normal eyes in each group. RESULTS The mean CH was 9.1 +/- 1.8 mm Hg for FFKc and 10.3 +/- 1.9 mm Hg for control eyes (P < 0.001), and the mean CRF was 9.2 +/- 1.8 and 11.1 +/- 2 mm Hg (P < 0.001), respectively. Sensitivity in each group was as follows: group 1, CH < 9.5 mm Hg (91%) and CRF < 9.5 mm Hg (81%); group 2, CH < 10.5 mm Hg (91%) and CRF < 10 mm Hg (87%); group 3, CH < 11.5 mm Hg (79%) and CRF < 11 mm Hg (74%); group 4 had two cases of FFKc, and the difference was not significant. Air pressure levels at inward and outward applanation and the maximum air pressure level were significantly lower and shorter in time in FFKc (P < 0.001), whereas the shape of the infrared signal was more variable. CONCLUSIONS The ORA provides additional information in the screening of FFKc, with an accurate analysis of the corneal biomechanical properties according to CCT, air pressure, and infrared curves.

Intra- and postoperative variation in ocular response analyzer parameters in keratoconic eyes after corneal cross-linking.

PURPOSE To analyze intra- and postoperative variation in Ocular Response Analyzer (ORA, Reichert Ophthalmic Instruments) parameters in 24 keratoconic eyes undergoing corneal cross-linking (CXL). METHODS In a prospective clinical study, corneal hysteresis (CH), corneal resistance factor (CRF), peak 1 and peak 2 amplitude, corneal-compensated and Goldmann-correlated intraocular pressure (IOP) were evaluated using the ORA. The thinnest cornea point was measured with the Pentacam (Oculus Inc). Corneal topography and endothelial cell count were performed. Measurements were recorded at baseline; intraoperatively after epithelium removal, riboflavin impregnation, and ultraviolet A irradiation; and postoperatively after corneal re-epithelialization and at 1, 6, and 12 months. RESULTS A statistically significant reduction of the thinnest cornea point from 462±23.24 μm was observed at the end of the CXL procedure intraoperatively and at 1- and 6-month follow-up (P<.05). A significant increase in the thinnest cornea point to 624±31.72 μm was found after re-epithelialization (P<.05), and no significant changes were observed at 1 year postoperatively. Mean CH and CRF did not change significantly after de-epithelialization, but were noted to significantly increase after CXL intraoperatively and postoperatively at 1-month follow-up. At 6 and 12 months postoperatively, CH and CRF were not statistically significantly different from pre-operatively. Peak 1 and peak 2 decreased intraoperatively from 276±52 and 228±47 to 172±42 and 131±42, respectively, at the conclusion of CXL (P<.05), and were noted to increase to 493±41 and 444±51, respectively, at 6-month follow-up. Corneal-compensated IOP and Goldmann-correlated IOP increased at 1 month after CXL (P>.05). CONCLUSIONS The results showed a significant change in ORA parameters and the thinnest cornea point during and after the CXL procedure and a high correlation between peak amplitudes and corneal asymmetry, providing insight to the bioelastic and biomechanical behavior of the cornea during and after CXL.

Postoperative changes in intraocular pressure and corneal biomechanical metrics Laser in situ keratomileusis versus laser-assisted subepithelial keratectomy

PURPOSE: To compare intraocular pressure (IOP) and corneal biomechanical metric changes after myopic laser in situ keratomileusis and laser‐assisted subepithelial keratectomy (LASEK). SETTING: Private practice, St. Louis, Missouri, USA. METHODS: The IOP, corneal biomechanical markers, and Ocular Response Analyzer (ORA) waveform parameters were prospectively measured preoperatively and after 6 months in ablation‐matched myopic LASIK eyes (mLASIK group) and LASEK eyes (mLASEK group). A retrospectively identified cohort of low myopia LASIK eyes (lmLASIK group) and fellow unoperated eyes (control) were tested at a single postoperative visit. Statistical analysis compared the percentage change in parameters between groups. RESULTS: The mean postoperative Goldmann tonometry and Goldmann‐correlated IOPs were statistically significant reduced in the mLASIK and mLASEK groups (P<.03). Corneal‐compensated IOP, but not Pascal dynamic contour tonometry, was significantly reduced in the mLASIK group. The percentage change in corneal hysteresis (CH) and the corneal resistance factor (CRF) was greater in the mLASIK and mLASEK groups than in the lmLASIK group. The greatest percentage change in ORA signal parameters was in the mLASIK group and the smallest change, in the mLASEK group. On multivariate linear regression, the residual stromal bed was predictive of the percentage change in CH and CRF (P<.001). CONCLUSIONS: Microkeratome flap creation combined with deeper stromal ablation had the greatest effect on the ORA applanation signal, indicating corneas that are more readily deformable. The smallest change in the signal was in the group without a stromal flap (LASEK). There was a complex interaction between ablation location and depth that affected corneal biomechanical properties.

Total corneal power estimation: ray tracing method versus gaussian optics formula.

PURPOSE To evaluate with the use of corneal topographic data the differences between total corneal power calculated using ray tracing (TCP) and the Gaussian formula (GEP) in normal eyes, eyes that previously underwent laser in situ keratomileusis/photorefractive keratectomy (LASIK/PRK), and theoretical models. METHODS TCP and GEP using mean instantaneous curvature were calculated over the central 4-mm zone in 94 normal eyes, 61 myopic-LASIK/PRK eyes, and 9 hyperopic-LASIK/PRK eyes. A corneal model was constructed to assess the incident angles at the posterior corneal surface for both refracted rays and parallel rays. Corneal models with varying parameters were also constructed to investigate the differences between mean TCP and GEP (4-mm zone), and an optical design software validation was performed. RESULTS The TCP values tended to be less than GEP in normal and myopic-LASIK/PRK eyes, with the opposite relationship in some hyperopic-LASIK/PRK eyes having the highest anterior surface curvature. The difference between TCP and GEP was a function of anterior surface instantaneous radii of curvature and posterior/anterior ratio in postrefractive surgery eyes but not in normal eyes. In model corneas, posterior incident angles with parallel rays were greater than those with refracted rays, producing an overestimation of negative effective posterior corneal power; differences in magnitude between TCP and GEP increased with decreasing ratio of posterior/anterior radii of curvature, consistent with clinical results. CONCLUSIONS In eyes after refractive surgery, calculating posterior corneal power using the Gaussian formula and its paraxial assumptions introduces errors in the calculation of total corneal power. This may generate errors in intraocular lens power calculation when using the Gaussian formula after refractive surgery.

Response of the posterior corneal surface to laser in situ keratomileusis for myopia

Purpose: To describe the response of the posterior corneal surface in laser in situ keratomileusis (LASIK) and determine whether residual stromal bed thickness or treatment magnitude is predictive of the posterior corneal surface elevation after uneventful LASIK. Setting: A private hospital‐based refractive surgery practice, Hong Kong SAR, China. Methods: Orbscan I (Bausch & Lomb) videokeratography examinations were performed on 1124 patients before and 6 months after LASIK for myopia (mean −6.81 diopters [D] ± 2.52 [SD]; range −0.88 to −14.50 D). The best‐fit sphere (BFS) over the central 9.0 mm region of the posterior corneal surface before and after treatment was compared. The location and magnitude of the 1.0 mm diameter region of highest elevation above the BFS for the central 4.0 mm diameter zone were calculated before and after treatment and compared using a paired t test. Stepwise regression was used to model the best predictors of the posterior radius of the BFS and the central elevation of the corneal surface above the BFS before and after treatment. Results: The mean radius of curvature of the posterior surface BFS decreased 0.10 mm after LASIK, from 6.31 to 6.21 mm (P<.001). Elevation above this BFS was increased 10 μm within a 1.00 mm diameter region of interest, and this was correlated with postoperative corneal thickness, inferotemporal decentration of the highest point, residual myopia, and steeper central posterior radius of curvature. Conclusions: No eye was diagnosed with corneal ectasia at the time of the 6‐month postoperative visit. After LASIK, there was a decreased radius of curvature for the BFS of the posterior corneal surface, with the highest elevation point located paracentrally. These findings are similar to the anterior corneal surface changes observed in corneal ectasia after LASIK but smaller in magnitude.

CLMI The Cone Location and Magnitude Index

Purpose: To develop an index for the detection of keratoconic patterns in corneal topography maps from multiple devices. Methods: For development, an existing Keratron (EyeQuip) topographic dataset, consisting of 78 scans from the right eyes of 78 healthy subjects and 25 scans from the right eyes of 25 subjects with clinically diagnosed keratoconus, was retrospectively analyzed. The Cone Location and Magnitude Index (CLMI) was calculated on the available axial and tangential curvature data. Stepwise logistic regression analysis was performed to determine the best predictor(s) for the detection of keratoconus. A sensitivity and specificity analysis was performed by using the best predictor of keratoconus. Percent probability of keratoconus was defined as the optimal probability threshold for the detection of disease. For validation, CLMI was calculated retrospectively on a second distinct dataset, acquired on a different topographer, the TMS-1. The validation dataset consisted of 2 scans from 24 eyes of 12 healthy subjects with no ocular history and 4 scans from 21 eyes of 14 subjects with clinically diagnosed keratoconus. Probability of keratoconus was calculated for the validation set from the equation determined from the development dataset. Results: The strongest significant sole predictor in the stepwise logistic regression was aCLMI, which is CLMI calculated from axial data. Sensitivity and specificity for aCLMI on the development dataset were 92% and 100%, respectively. A complete separation of normals and keratoconics with 100% specificity and 100% sensitivity was achieved by using the validation set. Conclusions: CLMI provides a robust index that can detect the presence or absence of a keratoconic pattern in corneal topography maps from 2 devices.

Automated decision tree classification of corneal shape

Purpose. The volume and complexity of data produced during videokeratography examinations present a challenge of interpretation. As a consequence, results are often analyzed qualitatively by subjective pattern recognition or reduced to comparisons of summary indices. We describe the application of decision tree induction, an automated machine learning classification method, to discriminate between normal and keratoconic corneal shapes in an objective and quantitative way. We then compared this method with other known classification methods. Methods. The corneal surface was modeled with a seventh-order Zernike polynomial for 132 normal eyes of 92 subjects and 112 eyes of 71 subjects diagnosed with keratoconus. A decision tree classifier was induced using the C4.5 algorithm, and its classification performance was compared with the modified Rabinowitz–McDonnell index, Schwiegerling’s Z3 index (Z3), Keratoconus Prediction Index (KPI), KISA%, and Cone Location and Magnitude Index using recommended classification thresholds for each method. We also evaluated the area under the receiver operator characteristic (ROC) curve for each classification method. Results. Our decision tree classifier performed equal to or better than the other classifiers tested: accuracy was 92% and the area under the ROC curve was 0.97. Our decision tree classifier reduced the information needed to distinguish between normal and keratoconus eyes using four of 36 Zernike polynomial coefficients. The four surface features selected as classification attributes by the decision tree method were inferior elevation, greater sagittal depth, oblique toricity, and trefoil. Conclusion. Automated decision tree classification of corneal shape through Zernike polynomials is an accurate quantitative method of classification that is interpretable and can be generated from any instrument platform capable of raw elevation data output. This method of pattern classification is extendable to other classification problems.

Biomechanical and morphological corneal response to placement of intrastromal corneal ring segments for keratoconus

PURPOSE: To evaluate the biomechanical and morphological changes in keratoconic corneas after Intacs intrastromal corneal ring segment (ICRS) implantation. SETTING: Department of Ophthalmology, National Reference Center for Keratoconus, Bordeaux University, Bordeaux, France. METHODS: Keratoconic eyes were retrospectively analyzed after ICRS implantation; preoperative and 6‐month postoperative evaluation were done using the Ocular Response Analyzer (ORA) and the Orbscan II topographer. Biomechanical parameters included corneal hysteresis (CH), the corneal resistance factor (CRF), and other parameters extracted from the signal curves. Morphological parameters included simulated keratometry and the cone location magnitude index from the axial map (aCLMI) and tangential map (tCLMI). Parameters were extracted using software designed to read and process topographic maps. RESULTS: There were no significant differences between preoperatively and postoperatively in mean CH (7.7 mm Hg ± 1.4 [SD] versus 7.4 ± 1.4 mm Hg) or mean CRF (6.6 ± 1.8 mm Hg versus 6.1 ± 1.4 mm Hg). Only 2 ORA signal parameters were significantly different. Topographic parameters with significant decreases were minimum central keratometry (K) (mean change −5.8 ± 2.9 diopters [D]) (P<.001), minimum central K (mean change −5.8 ± 2.3 D) (P<.001), mean aCLMI (9.6 ± 2.7 preoperatively versus 7.7 ± 2.5 postoperatively) (P<.009), and mean tCLMI (18.9 ± 2.8 versus 12.9 ± 4.4) (P<.002). The only significant correlation between biomechanical and topographic parameters was postoperative ORA infrared signal peak 1 and postoperative aCLMI. CONCLUSIONS: Intrastromal corneal ring implantation significantly decreased corneal curvature, with preoperative values predicting magnitude of change. However, it did not alter the viscoelastic biomechanical parameters of CH and CRF.

Early biomechanical keratoconus pattern measured with an ocular response analyzer: Curve analysis

PURPOSE: To estimate the ability of the Ocular Response Analyzer parameters to aid in the diagnosis of keratoconus in pre‐laser in situ keratomileusis (LASIK) patients. SETTING: Department of Ophthalmology, Bordeaux 2 University, Bordeaux Cedex, France. DESIGN: Evaluation of diagnostic test. METHODS: This study compared eyes with mild stages of keratoconus (study group) with preoperative eyes that later had LASIK (control group). Corneas with a central thickness within 500 to 600 μm were targeted. The biomechanical measurements were acquired, and 12 parameters were analyzed after extraction from the signal data. RESULTS: The study group comprised 103 eyes and the control group, 97 eyes. The mean corneal hysteresis (CH) was 9.2 mm Hg in study eyes and 10.1 mm Hg in control eyes and the mean corneal resistance factor (CRF), 8.9 mm Hg and 10.6 mm Hg, respectively. For a threshold of 9.6, CH had a sensitivity of 66% with a specificity of 67%. For a threshold of 9.7, the CRF had a sensitivity of 72% and a specificity of 77%. For 6 biomechanical parameters, the probability that a patient would present with keratoconus was 3 in 1000 if 1 parameter was over the chosen threshold. CONCLUSIONS: If 1 of 6 parameters were over a chosen threshold, the probability that a patient would present with keratoconus would be almost 3 in 1000 instead of 9 in 1000 in a LASIK surgery cohort. Despite low sensitivity and specificity, some parameters provided by the corneal analyzer offered high negative likelihood ratios and deserve more study with bigger samples. Financial Disclosure: No author has a financial or proprietary interest in any material or method mentioned.

Prediction of flap response

Purpose: To find predictors of the induced biomechanical and optical effects of lamellar flap creation on the cornea. Setting: Optimed Eye and Laser Clinic, Pretoria, South Africa, and the Department of Ophthalmology and Biomedical Engineering Center, The Ohio State University, Columbus, Ohio, and Bausch & Lomb Vision Research Laboratory, Rochester, New York, USA. Methods: This prospective study monitored the refractive, wavefront aberration, and corneal topographic changes in 29 eyes of 15 patients for 3 months after the creation of a corneal lamellar flap. The main outcome measures for statistical analysis were refraction, total corneal thickness, residual corneal bed thickness, horizontal white‐to‐white corneal diameter, horizontal flap diameter, topography data, and wavefront data. Results: Statistically significant changes were seen in the autorefraction mode. Wavefront data showed significant change in 4 Zernike modes—90/180‐degree astigmatism, vertical coma, horizontal coma, and spherical aberration. The topography data indicated the corneal biomechanical response was significantly predicted by stromal bed thickness in the early follow‐up period and by total corneal pachymetry and flap diameter in a 2‐parameter statistical model in the late follow‐up period. Conclusions: Uncomplicated lamellar flap creation is responsible for systematic changes in corneal topography and induction of higher‐order optical aberrations. Predictors of this response include stromal bed thickness, flap diameter, and total corneal pachymetry.