Ability of corneal biomechanical metrics and anterior segment data in the differentiation of keratoconus and healthy corneas (original) (raw)
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Corneal biomechanical properties and anterior segment parameters in forme fruste keratoconus
European Journal of Ophthalmology, 2012
Purpose: To evaluate the sensitivity and specificity of corneal biomechanical metrics, anterior segment data and a combination model, in differentiating Forme Fruste Keratoconus (FFK) from healthy corneas. Methods: 50 FFK eyes were identified by calculation of the KISA index and recruited FFK group. Results were compared with 50 normal eyes (NG group) randomly selected from 50 patients. The following parameters were evaluated for their diagnostic capacity by evaluation of their receiver operating curves (ROC): CH, CRF, corneal astigmatism (Cyl), anterior chamber depth (ACD), corneal volume (CV), at 3mm (CV3) and at 5mm (CV5), maximum posterior elevation value (PEL), central corneal thickness (CCT), thinnest corneal thickness (TCT), and its co-ordinates (TCTx, TCTy), the ratio TCT/CCT, Pachymetric Progression Indexes (PPImin, PPIavg and PPImax), and Ambrosio's Relational Thickness (ARTmin, ARTavg and ARTmax,). Logistic regression was attempted for identification of a combined diagnostic model. Results: Significant differences were detected in all studied parameters except the CYL, ACD, TCTx and CV. Among individuals parameters the highest predictive accuracy was for ARTavg (AUC: 95.4%, Sensitivity: 90%, Specificity: 88.9%) and TCT (AUC:95,3%, Sensitivity: 90.9% and Specificity: 89%). Sufficient predictive accuracy (AUC: 99.4%, Sensitivity: 98.8% and Specificity: 94.6%) was identified in a diagnostic model that combine the CRF, ARTavg and PEL parameters. Conclusions: None of individual parameters provide sufficient diagnostic capacity in FFK. However, diagnostic models that combine biomechanical and tomographic data, seem to provide high accuracy in differentiating FFK from normal corneas.
Corneal Biomechanical Metrics and Anterior Segment Parameters in Mild Keratoconus
Ophthalmology, 2010
Purpose: To compare corneal hysteresis (CH), corneal resistance factor (CRF), spherical equivalent (SE), average central keratometry (K-Avg), corneal astigmatism (CA), corneal volume (CV), anterior chamber (AC) depth, and central corneal thickness (CCT) between patients with mild keratoconus and healthy controls and to estimate the sensitivity and specificity of CH and CRF in discriminating mild keratoconus from healthy corneas.
Eye and vision (London, England), 2016
Keratoconus is a bilateral, non-inflammatory, degenerative corneal disease. The occurrence and development of keratoconus is associated with corneal thinning and conical protrusion, which causes irregular astigmatism. With the disruption of the collagen organization, the cornea loses its shape and function resulting in progressive visual degradation. Currently, corneal topography is the most important tool for the diagnosis of keratoconus, which may lead to false negatives among the patient population in the subclinical phase. However, it is now hypothesised that biomechanical destabilisation of the cornea may take place ahead of the topographic evidence of keratoconus, hence possibly assisting with disease diagnosis and management. This article provides a review of the definition, diagnosis, and management strategies for keratoconus based on corneal biomechanics.
2019
Background To assess and compare corneal hysteresis (CH) and corneal resistance factor (CRF) in normal thin (NT) healthy corneas with central corneal thickness (CCT) 470-500 µm with matched thickness in keratoconus (KC) and keratoconus suspect (KCS) eyes. Methods A total of 66 eyes in three groups were included prospectively: NT, KCS and KC groups based on clinical examination and topography. Corneal hysteresis (CH) and corneal resistance factor (CRF) were measured by the ocular response analyzer. CH and CRF were compared between the three groups and statistically analyzed by variances test. Results The three groups consisted of 32 NT, 19 KCS, and 15 KC. The mean CH measured was 8.55± 1.77, 9.03± 1.119 and 8.06 ± 0.85 mm Hg in NT, KCS and KC eyes, respectively. The mean CRF was 8.39 ± 1.47, 8.27 ± 1.09 and 7.24 ± 1.27 mm Hg in NT, KCS and KC eyes, respectively. Within range of central corneal thickness (470 – 500 µm), only mean CRF was statistically significantly different between t...
Evaluation of Ocular Biomechanical Indices to Distinguish Normal from Keratoconus Eyes
International Journal of Keratoconus and Ectatic Corneal Diseases, 2012
Purpose: To compare and assess the ability of pressure-derived parameters and corneal deformation waveform signal-derived parameters of the ocular response analyzer (ORA) measurement to distinguish between keratoconus and normal eyes, and to develop a combined parameter to optimize the diagnosis of keratoconus. Materials and methods: One hundred and seventy-seven eyes (177 patients) with keratoconus (group KC) and 205 normal eyes (205 patients; group N) were included. One eye from each subject was randomly selected for analysis. Patients underwent a complete clinical eye examination, corneal topography (Humphrey ATLAS), tomography (Pentacam Oculus) and biomechanical evaluations (ORA Reichert). Differences in the distributions between the groups were assessed using the Mann-Whitney test. The receiver operating characteristic (ROC) curve was used to identify cutoff points that maximized sensitivity and specificity in discriminating keratoconus from normal corneas. Logistic regression was used to identify a combined linear model (Fisher 1.0). Results: Significant differences in all studied parameters were detected (p < 0.05), except for W2. For the corneal resistance factor (CRF): Area under the ROC curve (AUROC) 89.1%, sensitivity 81.36%, specificity 84.88%. For the p1area: AUROC 91.5%, sensitivity 87.1%, specificity 81.95%. Of the individual parameters, the highest predictive accuracy was for the Fisher 1.0, which represents the combination of all parameters (AUROC 95.5%, sensitivity 88.14%, specificity 93.17%). Conclusion: Waveform-derived ORA parameters displayed greater accuracy than pressure-derived parameters for identifying keratoconus. Corneal hysteresis (CH) and CRF, a diagnostic linear model that combines different parameters, provided the greatest accuracy for differentiating keratoconus from normal corneas.
Detection of Keratoconus With a New Biomechanical Index
Journal of Refractive Surgery, 2016
B I O M E C H A N I C S he early diagnosis of corneal ectasia is of foremost importance in both screening for refractive surgery and the early treatment of keratoconus. Topography or tomography analysis using either videokeratography or optical coherence tomography instruments can help detect alteration in the shape of the cornea such as thinning and increased curvature. However, these instruments cannot measure the mechanical stability, which is thought to be the initiating event of the disease, even before notable changes in corneal morphology take place. 1,2 For this reason, there has been increasing interest in developing instruments to measure the in vivo biomechanical properties of the cornea to aid the diagnosis of an ectasia in a "biomechanical" stage, when topography and tomography are nor-T
Understanding the Correlation between Tomographic and Biomechanical Severity of Keratoconic Corneas
BioMed Research International, 2015
Purpose. To evaluate correlation between tomographic gradation of keratoconus (KC) and its corresponding air-puff induced biomechanical response. Methods. Corneal tomography and biomechanics were measured with Scheimpflug imaging in 44 normal and 92 KC corneas. Deformation waveform was also analyzed with Fourier series. A custom KC severity scale was used from 1 to 3 with 3 as the most severe grade. Tomographic and biomechanical variables were assessed among the grades. Sensitivity and specificity of the variables were assessed using receiver operating characteristics (ROC). Results. Curvature variables were significantly different between normal and disease ( < 0.05) and among grades ( < 0.05). Biomechanical variables were significantly different between normal and disease ( < 0.05) but similar among grades 1 and 2 ( > 0.05). All variables had an area under the ROC curve greater than 0.5. The root mean square of the Fourier cosine coefficients had the best ROC (0.92, cut-off: 0.027, sensitivity: 83%, specificity: 88.6%). Spearman correlation coefficient was significant between most variables ( < 0.05). However, tomographic segregation of keratoconus did not result in concomitant biomechanical segregation of the grades. Conclusions. There was lack of significant biomechanical difference between mild disease grades, despite progressive corneal thinning. Mathematical models that estimate corneal modulus from air-puff deformation may be more useful.
Correlation Between Corneal Biomechanics and Anterior Segment Parameters in Healthy Saudi Females
2020
Background: The ocular response analyzer (ORA) can measure corneal hysteresis (CH), corneal resistance factor (CRF), Goldmann correlated IOP (IOPg) and corneal compensated intraocular pressure (IOPcc). 1 Anterior segment parameters such as central corneal thickness (CCT), thinnest corneal thickness (TCT), apex corneal thickness (Apex CT), corneal volume (CV), anterior chamber depth (ACD), anterior chamber volume (ACV) and corneal astigmatism (CA) can be measured by Pentacam which is a Scheimp ug imaging device. 2 Many studies 3-10 investigated the correlation between corneal biomechanics and anterior segment parameters in healthy eyes and demonstrated a strong correlation between CH, CRF with CCT. Hwang et al 2013 7 found that CV was positively correlated with CH, but not CRF. However, Çevik et al 2016 10 reported positive correlations between CH, CRF, and CV and negative correlations between CH, CRF and both of posterior steep and average posterior values. Hwang et al 2 did not show a signi cant association between CA and all the biomechanical properties. Conversely, Montard et al 4 reported a negative association between CA with CH and CRF. Therefore, there is still debate regarding evaluation of this relation. Up to our knowledge, the correlation between corneal biomechanics and anterior segment parameters in healthy eyes is never investigated in Saudi Arabia. The purpose of this study was conducted to evaluate the correlation between corneal biomechanics measured with ORA and anterior segment parameters assessed with Oculus Pentacam HR in healthy Saudi females. Methods: This study was a prospective, non-randomized, cross-sectional, observational and quantitative study. The study included 129 eyes of 129 healthy Saudi females from King Saud University, Riyadh, Saudi Arabia. The mean age was 19.87 ± 1.328 (18-29 years). All subjects underwent a comprehensive ophthalmologic examination including refraction, visual acuity measurement, slit-lamp biomicroscopic examination, IOP measurement with an air puffer tonometer, and funduscopy. In addition, anterior segment parameters were measured with Oculus Pentacam HR. Additionally, corneal biomechanical parameters were measured with ORA (Reichert Ophthalmic Instruments). All data was analyzed using a Statistical Package for the Social Sciences (SPSS) version 22.0 software (SPSS Inc., Chicago, II, USA). Associations between corneal biomechanical parameters and anterior segment parameters were analyzed by Pearson's Correlation coe cients. P < 0.05 was considered a statistically signi cant. Results: In this study, mean (±SD) spherical equivalent (SE) was-1.62 ± 2.15 diopters and mean (±SD) CCT was 552.41 ± 58.90 μm. Mean (±SD) CH and CRF were 11.61 ± 1.80 and 11.26 ± 1.99 mm Hg, respectively. Correlation between ORA parameters and the anterior segment parameters using Pearson's Correlation Coe cient for all eyes in this study showed only highly signi cant positive correlation between CCT and each of CH, CRF, IOP g (r = 0.381, P < 0.0001) (r = 0.395, P < 0.0001) (r = 0.304, P < 0.0001) respectively. On the other hand, no signi cant association was detected between IOP cc and anterior segment parameters in this study Conclusion: This work is the rst one in Saudi Arabia to evaluate the correlation between corneal biomechanics and anterior segment parameters in healthy Saudi females. This study reported a positive correlation between CCT and each of CH, CRF, IOPg. Mild myopic eyes in this study showed a positive association between ASKV and each of CH and CRF. In addition, the mild myopic eyes demonstrated a positive relation between IOP g and ACV. Future prospective studies including males, different ethnic populations, different age groups with large sample sizes, using different imaging techniques, are recommended.
Turkish Journal of Ophthalmology, 2021
Objectives: To determine corneal biomechanical and tomographic factors associated with keratoconus (KC) progression. Materials and Methods: This study included 111 eyes of 111 KC patients who were followed-up for at least 1 year. Progression was defined as the presence of progressive change between the first two consecutive baseline visits in any single parameter (A, B, or C) ≥95% confidence interval or two parameters ≥80% confidence interval for the KC population evaluated by the Belin ABCD progression display. The eye with better initial tomographic findings was chosen as the study eye. Analyzed Pentacam parameters were maximum keratometry (Kmax), minimum pachymetry (Kmin), central corneal thickness, thinnest corneal thickness, 90° vertical anterior and posterior coma data in Zernike analysis, and Belin Ambrosio Enhanced Ectasia Display Final D value. Corneal hysteresis (CH) and corneal resistance factor (CRF) were analyzed together with the waveform parameters obtained with Ocular Response Analyzer (ORA). Factors related to KC progression were evaluated using t-tests and logistic regression tests. Statistical significance was accepted as p<0.05. Results: There were 44 (mean age: 27.1±8.5 years, female: 25) and 67 (mean age: 31.1±9.1 years, female: 36) patients in the progressive and non-progressive groups, respectively. Although Pentacam parameters along with CH and CRF were similar between the two groups, ORA waveform parameter derived from the second applanation signal p2area was statistically significantly lower in the progressive group (p=0.02). Each 100-unit decrease in p2area increased the likelihood of keratoconus progression by approximately 30% in the logistic regression analysis (β=0.707, p=0.001, model r2=0.27). Conclusion: Parameters derived from the second applanation signal of ORA may be superior to conventional ORA parameters and corneal tomography in predicting KC progression.