Current eye research · 2015
Predictive Analysis Between Topographic, Pachymetric and Wavefront Parameters in Keratoconus, Suspects and Normal Eyes: Creating Unified Equations to Evaluate Keratoconus.
Сравнительное исследованиеСтатьяНазвание и аннотация: Перевод пока не подготовлен
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Prakash Gaurav, Suhail Muhammad, Srivastava Dhruv
Аннотация
PURPOSE: To perform prediction analysis between topographic, pachymetric and wavefront parameters in keratoconus, suspects, and normal cases and to look at the possibility of a unified equation to evaluate keratoconus.
METHODS: This cross-sectional, observational study was done in cornea services of a specialty hospital. Fifty eyes of 50 candidates with a diagnosis of normal, keratoconus suspect, and keratoconus were included in each group (total 150 eyes). All eyes underwent detailed analysis on Scheimplug + Placido device (Sirius, CSO, Italy). Main parameters evaluated were topographic [maximum keratometry (Max Km), average keratometry and astigmatism at 3, 5, and 7 mm], pachymetric [central and minimum corneal thickness (MCT) and their difference, corneal volume] and corneal aberrations [higher order aberrations root-mean-square (HOARMS), coma, spherical, residual].Central tendency, predictive fits and regression models, were computed.
RESULTS: The measured variables had a significant difference in mean between the three groups (Kruskal-Wallis, p < 0.001). Max Km, MCT, and HOARMS had significant fits with other topographic, pachymetric and wavefront parameters, respectively. Inter-relations between these three (Max Km, MCT, and HOARMS) were also stronger for keratoconus (R(2) from 0.75 to 0.33) compared to suspect/normal eyes (R(2) from 0.15 to 0.003). These three variables (Max Km, MCT and HOARMS) were used as representative variables to create the unified equations. The equation for the pooled data was (Kmax = 59.5 + 2.3 × HOARMS-0.03 × MCT; R(2)= 0.7, p < 0.001).
CONCLUSIONS: Major variables used for grading keratoconus (MaxKm, MCT, HOARMS) can be linked by linear regression equations to predict the pathology's behavior.
METHODS: This cross-sectional, observational study was done in cornea services of a specialty hospital. Fifty eyes of 50 candidates with a diagnosis of normal, keratoconus suspect, and keratoconus were included in each group (total 150 eyes). All eyes underwent detailed analysis on Scheimplug + Placido device (Sirius, CSO, Italy). Main parameters evaluated were topographic [maximum keratometry (Max Km), average keratometry and astigmatism at 3, 5, and 7 mm], pachymetric [central and minimum corneal thickness (MCT) and their difference, corneal volume] and corneal aberrations [higher order aberrations root-mean-square (HOARMS), coma, spherical, residual].Central tendency, predictive fits and regression models, were computed.
RESULTS: The measured variables had a significant difference in mean between the three groups (Kruskal-Wallis, p < 0.001). Max Km, MCT, and HOARMS had significant fits with other topographic, pachymetric and wavefront parameters, respectively. Inter-relations between these three (Max Km, MCT, and HOARMS) were also stronger for keratoconus (R(2) from 0.75 to 0.33) compared to suspect/normal eyes (R(2) from 0.15 to 0.003). These three variables (Max Km, MCT and HOARMS) were used as representative variables to create the unified equations. The equation for the pooled data was (Kmax = 59.5 + 2.3 × HOARMS-0.03 × MCT; R(2)= 0.7, p < 0.001).
CONCLUSIONS: Major variables used for grading keratoconus (MaxKm, MCT, HOARMS) can be linked by linear regression equations to predict the pathology's behavior.