June 2013
Volume 54, Issue 15
Free
ARVO Annual Meeting Abstract  |   June 2013
The correlation between ocular response analyzer keratoconus match index and subjective topographic assessment using the Orbscan to screen for keratoconus
Author Affiliations & Notes
  • Ryan Vasan
    Ophthalmology, Weill Cornell Medical College, New York, NY
  • Ryan St Clair
    Ophthalmology, Weill Cornell Medical College, New York, NY
  • Syed Hussnain
    Ophthalmology, Weill Cornell Medical College, New York, NY
  • Ana Alzaga Fernandez
    Ophthalmology, Weill Cornell Medical College, New York, NY
  • Christopher Starr
    Ophthalmology, Weill Cornell Medical College, New York, NY
  • Footnotes
    Commercial Relationships Ryan Vasan, None; Ryan St Clair, None; Syed Hussnain, None; Ana Alzaga Fernandez, None; Christopher Starr, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 1627. doi:
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      Ryan Vasan, Ryan St Clair, Syed Hussnain, Ana Alzaga Fernandez, Christopher Starr; The correlation between ocular response analyzer keratoconus match index and subjective topographic assessment using the Orbscan to screen for keratoconus. Invest. Ophthalmol. Vis. Sci. 2013;54(15):1627.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose: To evaluate the correlation between the Keratoconus Match Index (KMI) of the Ocular Response Analyzer (ORA) and cornea fellowship-trained graders using the Orbscan topographer when evaluating patients for keratoconus (KCN)

Methods: This was a retrospective study including patients over the age of 18 who underwent both Orbscan and ORA testing. The ORA KMI software compares the corneal biomechanical properties of the examined eye to a normative database and assigns the eye to one of five categories: normal, KCN suspect, mild KCN, moderate KCN, and severe KCN. Three masked MD observers (graders A, B, and C), all of whom are cornea fellowship trained, examined the Orbscan topography of each of the eyes, and classified each eye into the same 5 categories. The agreement between observers’ classification and classification by KMI, as well as agreement between the individual observers, was calculated using overall agreement and free-marginal kappa statistic.

Results: The KMI software identified 26 eyes as normal, compared to 16, 32, and 28 eyes for graders A, B, and C. 16 eyes were classified as “KCN suspect” by KMI, compared to 14, 3, and 7 eyes by graders A, B, and C respectively. 3 eyes were classified as “mild KCN” by KMI, compared to 7, 5, and 6 eyes by graders A, B, and C, respectively. 1 eye was classified as “moderate KCN” by KMI, compared to 5, 4, and 3 eyes by graders A, B, and C, respectively. No eyes were classified as “severe KCN” by KMI, compared to 4, 2, and 2 eyes by graders A, B, and C, respectively. The three graders agreed with each other 55% of the time, with a free-marginal kappa of 0.448 (moderate agreement). Grader A and KMI agreed 30% overall, with a free-marginal kappa of 0.130 (slight agreement). Grader B and KMI agreed 46% overall, with a free-marginal kappa of 0.321 (fair agreement). Grader C and the KMI agreed 46% overall, with a free-marginal kappa of 0.321. KMI agreed with all 3 graders 13% of the time and with at least one grader 63% of the time. The KMI did not agree with any grader 37% of the time.

Conclusions: There is only slight to fair agreement between observers and the KMI, compared to moderate inter-observer agreement. The KMI alone is not well correlated with the subjective interpretation of topography, and should be used in conjunction with other modalities to evaluate a possible keratoconus suspect.

Keywords: 574 keratoconus • 550 imaging/image analysis: clinical • 733 topography  
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