Journal of cataract and refractive surgery · 2025
Improving refractive predictability with high-powered intraocular lenses: Refractive implications of various optic designs.
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Cooke David L, Seward Michael S, Cooke Timothy L
Аннотация
PURPOSE: To identify and reduce refractive prediction errors associated with several common intraocular lens (IOL) models.
SETTING: Multiple private and university practices across the United States.
DESIGN: Retrospective analysis of a large de-identified database.
METHODS: For all IOL models with at least 800 eyes, prediction errors were plotted as a function of IOL power using Olsen, Barrett, and Cooke K6 (K6) formulas. Plots were categorized as myopic, hyperopic, or neutral profiles, according to their prediction error trends at higher dioptric powers. Prediction modifications (PMODs) were developed with K6 formula, via optical calculations for one IOL (SA60AT), and by empirical data for all others. The PMODs were then tested for their applicability to other modern IOL formulas.
RESULTS: Data from 71,183 cataract surgeries were analyzed. At higher powers, Tecnis platform IOLs and one Acrysof model IOL produced unintended myopia. Conversely, enVista platform IOLs produced unintended hyperopia at higher powers. In all cases, results were improved when incorporating PMODs. In 12 of 13 cases, predictability as measured by root-mean-square error was improved (P < 0.03). This included measurements by seven modern formulas in the case of the Tecnis data. Overall, 1306 eyes (1.8%) required more than 0.25 D of adjustment to their predicted refractions. At highest available powers, the needed prediction corrections were -0.61, +0.76, and -0.69 for the Tecnis, enVista, and SN60AT IOLs, respectively.
CONCLUSIONS: Above 23.5 D, some IOLs cause either myopic or hyperopic prediction errors. Specific adjustments are recommended to improve refractive predictability for several common IOL models.
SETTING: Multiple private and university practices across the United States.
DESIGN: Retrospective analysis of a large de-identified database.
METHODS: For all IOL models with at least 800 eyes, prediction errors were plotted as a function of IOL power using Olsen, Barrett, and Cooke K6 (K6) formulas. Plots were categorized as myopic, hyperopic, or neutral profiles, according to their prediction error trends at higher dioptric powers. Prediction modifications (PMODs) were developed with K6 formula, via optical calculations for one IOL (SA60AT), and by empirical data for all others. The PMODs were then tested for their applicability to other modern IOL formulas.
RESULTS: Data from 71,183 cataract surgeries were analyzed. At higher powers, Tecnis platform IOLs and one Acrysof model IOL produced unintended myopia. Conversely, enVista platform IOLs produced unintended hyperopia at higher powers. In all cases, results were improved when incorporating PMODs. In 12 of 13 cases, predictability as measured by root-mean-square error was improved (P < 0.03). This included measurements by seven modern formulas in the case of the Tecnis data. Overall, 1306 eyes (1.8%) required more than 0.25 D of adjustment to their predicted refractions. At highest available powers, the needed prediction corrections were -0.61, +0.76, and -0.69 for the Tecnis, enVista, and SN60AT IOLs, respectively.
CONCLUSIONS: Above 23.5 D, some IOLs cause either myopic or hyperopic prediction errors. Specific adjustments are recommended to improve refractive predictability for several common IOL models.