To describe a novel method called “three variable optimization” that entails a process of doing just one calculation to zero out the mean prediction error of an entire dataset (regardless of size), using only 3 variables: (1) the constant used, (2) the average intraocular lens (IOL) power, and (3) the average prediction error (PE as actual refraction - predicted refraction).

Development, evaluation, and testing of a method to optimize personal IOL constants.

A dataset of 876 eyes was used as a training set, and another dataset of 1,079 eyes was used to test the method. The Barrett Universal II, Cooke K6, Haigis, RBF 3.0, Hoffer Q, Holladay 1, Holladay 2, SRK/T, and T2 were analyzed. The same dataset was also divided into 3 subgroups (short, medium, and long eyes). The three variable optimization process was applied to each dataset and subset, and the obtained optimized constants were then used to obtain the mean PE of each dataset. We then compared those results with those obtained by zeroing out the mean PE in the classical method.

The three variable optimization showed similar results to classical optimization with less data needed to optimize and no clinically significant difference. Dividing the dataset into subsets of short, medium and long eyes, also shows that the method is useful even in those situations. Finally, the method was tested in multiple formulas and it was able to reduce the PE with no clinically significant difference from classical optimization.

This method could then be applied by surgeons to optimize their constants by reducing the mean prediction error to zero without prior technical knowledge and it is available online for free at wwww.ioloptimization.com.