Abstract

BackgroundTo investigate which factors are correlated with the visual improvement achieved with a specific model of scleral contact lens (SCL) in keratoconus (KC) eyes and to define a model to predict such improvement according to the pre-fitting data. In addition, the changes occurred with the fitting of a specific model of SCL during a period of 3 months in corneas with KC have been investigated.MethodsLongitudinal retrospective study including 30 eyes of 18 patients (age, 14–65 years) with KC fitted with the SCL ICD16.50 (Paragon Vision Sciences). Visual, refractive, corneal tomographic and ocular aberrometric changes were evaluated during a 3-month follow-up. Likewise, the characterization of the post-lens meniscus was performed by optical coherence tomography (OCT) with the measurement of central, nasal and temporal vaults.ResultsThe visual acuity increased significantly from a mean pre-fitting value with spectacles of 0.23 ± 0.07 logarithm of minimal angle of resolution (logMAR) to a mean value of 0.10 ± 0.04 logMAR after 1 month of SCL wear (P < 0.001). An improvement of 1 or more lines of visual acuity with the SCL occurred in 62.1% of the eyes. A significant decrease in central, nasal, and temporal vault was observed after 1 month of SCL wear (P ≤ 0.046). Likewise, there was a significant difference between nasal and temporal vaults during the first month of SCL use (P = 0.008). Furthermore, a significant reduction of ocular high order (P = 0.028) and primary coma root mean square (P = 0.018) was found with the SCL. A predicting linear equation of the change in visual acuity achievable with the SCL was obtained (P < 0.001, R2 = 0.878) considering the pre-fitting spectacle corrected distance visual acuity, and the power and sagittal lens of SCL.ConclusionsThe scleral contact lens evaluated provides an efficacious visual rehabilitation in KC due to the improvement of visual acuity and the correction of low and high-order ocular aberrations. This visual acuity improvement can be predicted from some pre-fitting variables.

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