Air-pulse corneal applanation signal curve parameters for the characterisation of keratoconus

Mikielewicz, Marek; Kotliar, Konstantin; Barraquer, Rafael I.; Michael, Ralph

doi:10.1136/bjo.2010.188300

Cited by 66 publications

(74 citation statements)

References 28 publications

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“…These results confirm those of a previous study 16 and may be related to findings that suggest that the CRF correlates best with the optical aberrations of keratoconus.…”

Section: Discussionsupporting

confidence: 82%

Evaluation of Ocular Biomechanical Indices to Distinguish Normal from Keratoconus Eyes

Lopes¹,

Luz²,

Fontes³

et al. 2012

International Journal of Keratoconus and Ectatic Corneal Diseases

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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 MannWhitney 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.

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“…These results confirm those of a previous study 16 and may be related to findings that suggest that the CRF correlates best with the optical aberrations of keratoconus.…”

Section: Discussionsupporting

confidence: 82%

Evaluation of Ocular Biomechanical Indices to Distinguish Normal from Keratoconus Eyes

Lopes¹,

Luz²,

Fontes³

et al. 2012

International Journal of Keratoconus and Ectatic Corneal Diseases

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“…It analyzes corneal behavior during a bidirectional applanation process induced by an air jet, and produces estimates of corneal hysteresis and corneal resistance factor, along with a set of 36 waveform-derived parameters. [13][14][15] The Corvis ST (Oculus Optikgeräte GmbH; Wetzlar, Germany) was later introduced as a noncontact tonometer, which monitors the response of the cornea to an air pressure pulse using an ultra-highspeed Scheimpflug camera, and uses the captured image sequence to produce estimates of IOP and deformation response parameters. 16 Several articles have recently been published on the possible applications of the Corvis ST, particularly evaluating possible biomechanical differences in the cornea after undergoing refractive surgery procedures, [17][18][19][20][21][22] between normal and keratoconic patients, [23][24][25][26] after cross-linking, 27 and in patients with glaucoma.…”

Section: Resultsmentioning

confidence: 99%

Influence of Pachymetry and Intraocular Pressure on Dynamic Corneal Response Parameters in Healthy Patients

et al. 2016

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n 1619, Scheiner provided the first precise description of the corneal shape using glass balls of known curvatures.1 From that first description, many other diagnostic tools have been developed to describe corneal shape, from keratometry to corneal topography (front surface curvature maps), 2 then into three-dimensional corneal tomography systems.3 More recently, it has been shown that corneal biomechanical behavior plays an important role in maintaining corneal shape, which is necessary for light refraction and clear vision, 4 and should therefore be considered in understanding the development of ectatic diseases 5,6 and the results of surgery. 4,7 Until recently, the evaluation of corneal biomechanical properties had been restricted to ex vivo laboratory studies 5,8 and mathematical corneal models. METHODS: Seven hundred five healthy patients were included in this multicenter retrospective study. The biomechanical response data were analyzed to obtain normative values with their dependence on corrected and clinically validated intraocular pressure estimates developed using the finite element method (bIOP), central corneal thickness (CCT), and age, and to evaluate the influence of bIOP, CCT, and age. RESULTS:The results showed that all DCRs were correlated with bIOP except deflection amplitude (DefA) ratio, highest concavity (HC) radius, and inverse concave radius. The analysis of the relationship of DCRs with CCT indicated that HC radius, inverse concave radius, deformation amplitude (DA) ratio, and DefA ratio were correlated with CCT (rho values of 0.343, -0.407, -0.444, and -0.406, respectively). The age group subanalysis revealed that primarily whole eye movement followed by DA ratio and inverse concave radius were the parameters that were most influenced by age. Finally, custom software was created to compare normative values to imported examinations.CONCLUSIONS: HC radius, inverse concave radius, DA ratio, and DefA ratio were shown to be suitable parameters to evaluate in vivo corneal biomechanics due to their independence from IOP and their correlation with pachymetry and age. The creation of normative values allows the interpretation of an abnormal examination without the need to match every case with another normal patient matched for CCT and IOP.[J Refract Surg. 2016;32(8):550-561.]

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“…3 The ORA measures corneal behavior during a bidirectional applanation process induced by an air jet and produces estimates of corneal hysteresis and corneal resistance factor, along with a set of 38 waveformderived parameters. [4][5][6] The capability of the ORA to diagnose keratoconus was tested in several articles [6][7][8] but never reached the gold standard.…”

Section: Discussionmentioning

confidence: 99%

Detection of Keratoconus With a New Biomechanical Index

Vinciguerra¹,

Ambrósio²,

Elsheikh³

et al. 2016

J Refract Surg

409

346

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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 ABSTRACT PURPOSE: To evaluate the ability of a new combined biomechanical index called the Corvis Biomechanical Index (CBI) based on corneal thickness profile and deformation parameters to separate normal from keratoconic patients.METHODS: Six hundred fifty-eight patients (329 eyes in each database) were included in this multicenter retrospective study. Patients from two clinics located on different continents were selected to test the capability of the CBI to separate healthy and keratoconic eyes in more than one ethnic group using the Corvis ST (Oculus Optikgeräte GmbH, Wetzlar, Germany). Logistic regression was employed to determine, based on Database 1 as the development dataset, the optimal combination of parameters to accurately separate normal from keratoconic eyes. The CBI was subsequently independently validated on Database 2. RESULTS:The CBI included several dynamic corneal response parameters: deformation amplitude ratio at 1 and 2 mm, applanation 1 velocity, standard deviation of deformation amplitude at highest concavity, Ambrósio's Relational Thickness to the horizontal profile, and a novel stiffness parameter. The receiver operating characteristic curve analysis of the training database showed an area under the curve of 0.983. With a cut-off value of 0.5, 98.2% of the cases were correctly classified with 100% specificity and 94.1% sensitivity. In the validation dataset, the same cut-off point correctly classified 98.8% of the cases with 98.4% specificity and 100% sensitivity. CONCLUSIONS:The CBI was shown to be highly sensitive and specific to separate healthy from keratoconic eyes. The presence of an external validation dataset confirms this finding and suggests the possible use of the CBI in everyday clinical practice to aid in the diagnosis of keratoconus.[J Refract Surg. 2016;32(12):803-810.]

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Air-pulse corneal applanation signal curve parameters for the characterisation of keratoconus

Abstract: Most of the new waveform parameters demonstrated good ability to distinguish between KC and normal eyes. The changes in the parameter values after CXL and ICR treatments were smaller than expected.

Cited by 66 publications

References 28 publications

Evaluation of Ocular Biomechanical Indices to Distinguish Normal from Keratoconus Eyes

Evaluation of Ocular Biomechanical Indices to Distinguish Normal from Keratoconus Eyes

Influence of Pachymetry and Intraocular Pressure on Dynamic Corneal Response Parameters in Healthy Patients

Detection of Keratoconus With a New Biomechanical Index

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