Ability of corneal biomechanical metrics and anterior segment data in the differentiation of keratoconus and healthy corneas

Fontes, Bruno Machado; Ambrósio, Renato; Jardim, Daniela; Velarde, Guillermo Coca; Nosé, Walton

doi:10.1590/s0004-27492010000400006

Cited by 43 publications

(24 citation statements)

References 34 publications

(45 reference statements)

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“…The timing of the spikes indicates when the applanation events occurred within the 25-millisecond measurement (24) . The ORA pressure parameters had significantly lower mean values in keratoconus compared to normal eyes, supporting the results of previous studies (25)(26) . CH, CRF, and IOPg exhibited significantly different distributions in normal and keratoconic eyes (Mann-Whitney U-test, p<0.0001) in the analysis of the waveform signal and mean of consecutive measurements.…”

Section: Discussionsupporting

confidence: 90%

Best waveform score for diagnosing keratoconus

Luz¹,

Fontes²,

Lopes³

et al. 2013

Rev. bras.oftalmol.

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Purpose: To test whether corneal hysteresis (CH) and corneal resistance factor (CRF) can discriminate between keratoconus and normal eyes and to evaluate whether the averages of two consecutive measurements perform differently from the one with the best waveform score (WS) for diagnosing keratoconus. Methods: ORA measurements for one eye per individual were selected randomly from 53 normal patients and from 27 patients with keratoconus. Two groups were considered the average (CH-Avg, CRF-Avg) and best waveform score (CH-WS, CRF-WS) groups. The Mann-Whitney U-test was used to evaluate whether the variables had similar distributions in the Normal and Keratoconus groups. Receiver operating characteristics (ROC) curves were calculated for each parameter to assess the efficacy for diagnosing keratoconus and the same obtained for each variable were compared pairwise using the Hanley-McNeil test. Results: The CH-Avg, CRF-Avg, CH-WS and CRF-WS differed significantly between the normal and keratoconus groups (p<0.001). The areas under the ROC curve (AUROC) for CH-Avg, CRF-Avg, CH-WS, and CRF-WS were 0.824, 0.873, 0.891, and 0.931, respectively. CH-WS and CRF-WS had significantly better AUROCs than CH-Avg and CRF-Avg, respectively (p=0.001 and 0.002). Conclusion: The analysis of the biomechanical properties of the cornea through the ORA method has proved to be an important aid in the diagnosis of keratoconus, regardless of the method used. The best waveform score (WS) measurements were superior to the average of consecutive ORA measurements for diagnosing keratoconus.Keywords: Cornea/physiopathology; Keratoconus/diagnosis; Biomechanics/physiology; Dilatation, pathologic; Diagnostic techniques, ophthalmologic RESUMOObjetivo: Testar se a histerese corneana (CH) e o fator de resistência corneano (CRF) podem discriminar olhos com ceratocone e avaliar se a média de duas medidas consecutivas apresenta desempenho diferente da medida única com a melhor waveform score para diagnósticar o ceratocone. Métodos: Foram realizadas medidas do ORA de um olho por indivíduo, selecionados aleatoriamente a partir de 53 pacientes normais e de 27 pacientes com ceratocone. Dois grupos foram considerados: a média (CH-médio, o CRFmédio) e melhor waveform score (CH-WS, CRF-WS). O teste de Mann-Whitney U-teste foi utilizado para avaliar se as variáveis apresentaram distribuições semelhantes entre os grupos. As curvas (ROC) foram calculadas para cada parâmetro para avaliar eficácia no diagnóstico e as obtidas para cada variável foram comparadas usando o teste de Hanley-McNeil. Resultados: CH-médio, CRF-médio, CH-WS e CRF-WS diferiram significativamente entre os grupos (p<0,001). Já as áreas sob a curva ROC para CHmédio, CRF-médio, CH-WS, e CRF-WS foram 0,824, 0,873, 0,891, 0,931, respectivamente. CH-WS e CRF-WS obtiveram AUROCs significativamente melhores do que CH-médio e CRF-médio (p=0,001 e 0,002). Conclusão: A análise das propriedades biomecânicas da córnea através do ORA demonstrou ser um método auxiliar importante no diagnóstico de cera...

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Section: Discussionsupporting

confidence: 90%

Best waveform score for diagnosing keratoconus

Luz¹,

Fontes²,

Lopes³

et al. 2013

Rev. bras.oftalmol.

Self Cite

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“…5 Therefore, for corneas with keratoconus, lower values were found for most parameters. 3,14,15 However, our data showed that some parameters derived from the waveform signal; i.e. path 1, 2, 11, 21 and aplhf, were higher in patients with keratoconus.…”

Section: Discussioncontrasting

confidence: 59%

“…Corneal hysteresis (CH) and the corneal resistance factor (CRF), which are the corneal biomechanical metrics generated by the ORA, have been the subjects of several recent publications. 3,4 Luce presented data (Luce D, ORA waveform analysis and beyond. Presented at: American Society of Cataract and Refractive Surgery Annual Meeting, April 3 to 8,2009; San Francisco, California) indicating that waveform parameters provided from the ORA signal may be more sensitive than CH or the CRF in discriminating abnormal corneas.…”

Section: 5005/jp-journals-10025-1028mentioning

confidence: 99%

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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“…Other hopeful but as yet unvalidated techniques include biomechanical characterization using the Ocular Response Analyzer (Reichert, Buffalo, NY), which, although theoretically promising, 10 has demonstrated limited ability in screening for ectatic diseases, 29,30 and the recently FDA-approved Oculus Corvis ST. 31 Ambrósio & Randleman/Editorial SUMMARY: SCREENING TODAY So, what should we be using to screen our patients in 2013? Well-established Placido-based criteria remain an integral part of screening until this is defi nitively surpassed by other technology.…”

Section: Additional Screening Toolsmentioning

confidence: 99%