Revisiting Javal’s rule: a fresh and improved power vector approach according to age

Abstract

Abstract Purpose The scientific community has established Javal’s rule as a model linking refractive (RA) and keratometric (KA) astigmatism since its appearance more than 100 years ago. The aim was to improve the accuracy of this relationship according to subject’s age by applying the power vector analysis. Posterior corneal curvature has also been studied. Methods The IOLMaster 700 optical biometer was used to measure the corneal thickness and the radius of curvature of the anterior and posterior corneal surfaces. Refractive error was determined by a non-cycloplegic subjective refraction process with trial lenses. Linear regression analyses were applied using J0 and J45 power vector components. An evaluation was carried out according to the subject’s age resulting into eight regression relationships for each astigmatic vector component for each relationship. Results A total of 2254 right eyes from 2254 healthy subjects were evaluated. A trend towards against-the-rule astigmatism (ATR) was found with aging, both for refractive astigmatism (RA) and keratometric astigmatism (KA), with 95.2% of subjects under 20 years old having with-the-rule (WTR) KA, and only 22.8% above 79 years old. The following regression equations were found between RA and KA: $${J}_0^{\textrm{RA}}$$ J 0 RA = 0.73 × $${J}_0^{\textrm{KA}}$$ J 0 KA − 0.18 (R = 0.78) and $${J}_{45}^{\textrm{RA}}$$ J 45 RA = 0.70 × $${J}_{45}^{\textrm{KA}}$$ J 45 KA + 0.04 (R = 0.69) and between RA and total corneal astigmatism (TCA): $${J}_0^{\textrm{RA}}$$ J 0 RA = 0.73 × $${J}_0^{\textrm{TCA}}$$ J 0 TCA + 0.13 (R=0.78) and $${J}_{45}^{\textrm{RA}}$$ J 45 RA = 0.70 × $${J}_{45}^{\textrm{TCA}}$$ J 45 TCA − 0.06 (R = 0.68) for the whole sample, but with sensible differences among age groups, both in the slope and in the intercept. Conclusion Ignoring the age of the subject when using Javal’s rule could lead to an error in the final cylinder calculation that would increase in high astigmatisms. Applying this new power vector approach based on subject’s age could improve the accuracy of the astigmatism prediction.