Multivariate Analysis of the Ocular Response Analyzer’s Corneal Deformation Response Curve for Early Keratoconus Detection

Purpose. To thoroughly analyze corneal deformation responses curves obtained by Ocular Response Analyzer (ORA) testing in order to improve subclinical keratoconus detection. Methods. Observational case series of 87 control and 73 subclinical keratoconus eyes. Examination included corneal topography,...

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Main Authors: Jonatán D. Galletti, Pablo R. Ruiseñor Vázquez, Fernando Fuentes Bonthoux, Tomás Pförtner, Jeremías G. Galletti
Format: Article
Language:English
Published: Wiley 2015-01-01
Series:Journal of Ophthalmology
Online Access:http://dx.doi.org/10.1155/2015/496382
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author Jonatán D. Galletti
Pablo R. Ruiseñor Vázquez
Fernando Fuentes Bonthoux
Tomás Pförtner
Jeremías G. Galletti
author_facet Jonatán D. Galletti
Pablo R. Ruiseñor Vázquez
Fernando Fuentes Bonthoux
Tomás Pförtner
Jeremías G. Galletti
author_sort Jonatán D. Galletti
collection DOAJ
description Purpose. To thoroughly analyze corneal deformation responses curves obtained by Ocular Response Analyzer (ORA) testing in order to improve subclinical keratoconus detection. Methods. Observational case series of 87 control and 73 subclinical keratoconus eyes. Examination included corneal topography, tomography, and biomechanical testing with ORA. Factor analysis, logistic regression, and receiver operating characteristic curves were used to extract combinations of 45 corneal waveform descriptors. Main outcome measures were corneal-thickness-corrected corneal resistance factor (ccCRF), combinations of corneal descriptors, and their diagnostic performance. Results. Thirty-seven descriptors differed significantly in means between groups, and among them ccCRF afforded the highest individual diagnostic performance. Factor analysis identified first- and second-peak related descriptors as the most variable one. However, conventional biomechanical descriptors corneal resistance factor and hysteresis differed the most between control and keratoconic eyes. A combination of three factors including several corneal descriptors did not show better diagnostic performance than a combination of conventional indices. Conclusion. Multivariate analysis of ORA signals did not surpass simpler models in subclinical keratoconus detection, and there is considerable overlap between normal and ectatic eyes irrespective of the analysis model. Conventional biomechanical indices seem to already provide the best performance when appropriately considered.
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institution Kabale University
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spelling doaj-art-83847ed80eab47f69cd336a1c49157862025-02-03T01:11:40ZengWileyJournal of Ophthalmology2090-004X2090-00582015-01-01201510.1155/2015/496382496382Multivariate Analysis of the Ocular Response Analyzer’s Corneal Deformation Response Curve for Early Keratoconus DetectionJonatán D. Galletti0Pablo R. Ruiseñor Vázquez1Fernando Fuentes Bonthoux2Tomás Pförtner3Jeremías G. Galletti4ECOS (Clinical Ocular Studies) Laboratory, Pueyrredón 1716, 1119 Buenos Aires, ArgentinaECOS (Clinical Ocular Studies) Laboratory, Pueyrredón 1716, 1119 Buenos Aires, ArgentinaECOS (Clinical Ocular Studies) Laboratory, Pueyrredón 1716, 1119 Buenos Aires, ArgentinaECOS (Clinical Ocular Studies) Laboratory, Pueyrredón 1716, 1119 Buenos Aires, ArgentinaECOS (Clinical Ocular Studies) Laboratory, Pueyrredón 1716, 1119 Buenos Aires, ArgentinaPurpose. To thoroughly analyze corneal deformation responses curves obtained by Ocular Response Analyzer (ORA) testing in order to improve subclinical keratoconus detection. Methods. Observational case series of 87 control and 73 subclinical keratoconus eyes. Examination included corneal topography, tomography, and biomechanical testing with ORA. Factor analysis, logistic regression, and receiver operating characteristic curves were used to extract combinations of 45 corneal waveform descriptors. Main outcome measures were corneal-thickness-corrected corneal resistance factor (ccCRF), combinations of corneal descriptors, and their diagnostic performance. Results. Thirty-seven descriptors differed significantly in means between groups, and among them ccCRF afforded the highest individual diagnostic performance. Factor analysis identified first- and second-peak related descriptors as the most variable one. However, conventional biomechanical descriptors corneal resistance factor and hysteresis differed the most between control and keratoconic eyes. A combination of three factors including several corneal descriptors did not show better diagnostic performance than a combination of conventional indices. Conclusion. Multivariate analysis of ORA signals did not surpass simpler models in subclinical keratoconus detection, and there is considerable overlap between normal and ectatic eyes irrespective of the analysis model. Conventional biomechanical indices seem to already provide the best performance when appropriately considered.http://dx.doi.org/10.1155/2015/496382
spellingShingle Jonatán D. Galletti
Pablo R. Ruiseñor Vázquez
Fernando Fuentes Bonthoux
Tomás Pförtner
Jeremías G. Galletti
Multivariate Analysis of the Ocular Response Analyzer’s Corneal Deformation Response Curve for Early Keratoconus Detection
Journal of Ophthalmology
title Multivariate Analysis of the Ocular Response Analyzer’s Corneal Deformation Response Curve for Early Keratoconus Detection
title_full Multivariate Analysis of the Ocular Response Analyzer’s Corneal Deformation Response Curve for Early Keratoconus Detection
title_fullStr Multivariate Analysis of the Ocular Response Analyzer’s Corneal Deformation Response Curve for Early Keratoconus Detection
title_full_unstemmed Multivariate Analysis of the Ocular Response Analyzer’s Corneal Deformation Response Curve for Early Keratoconus Detection
title_short Multivariate Analysis of the Ocular Response Analyzer’s Corneal Deformation Response Curve for Early Keratoconus Detection
title_sort multivariate analysis of the ocular response analyzer s corneal deformation response curve for early keratoconus detection
url http://dx.doi.org/10.1155/2015/496382
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