A No-History Multi-Formula Approach to Improve the IOL Power Calculation after Laser Refractive Surgery: Preliminary Results

Abstract

This retrospective comparative study proposes a multi-formula approach by comparing no-history IOL power calculation methods after myopic laser-refractive-surgery (LRS). One-hundred-thirty-two eyes of 132 patients who had myopic-LRS and cataract surgery were examined. ALMA, Barrett True-K (TK), Ferrara, Jin, Kim, Latkany and Shammas methods were evaluated in order to back-calculate refractive prediction error (PE). To eliminate any systematic error, constant optimization through zeroing-out the mean error (ME) was performed for each formula. Median absolute error (MedAE) and percentage of eyes within ±0.50 and ±1.00 diopters (D) of PE were analyzed. PEs were plotted with corresponding mean keratometry (K), axial length (AL), and AL/K ratio; then, different ranges were evaluated. With optimized constants through zeroing-out ME (90 eyes), ALMA was better when K ≤ 38.00 D-AL > 28.00 mm and when 38.00 D < K ≤ 40.00 D-26.50 mm < AL ≤ 29.50 mm; Barrett-TK was better when K ≤ 38.00 D-AL ≤ 26.50 mm and when K > 40.00 D-AL ≤ 28.00 mm or AL > 29.50 mm; and both ALMA and Barrett-TK were better in other ranges. (p < 0.05) Without modified constants (132 eyes), ALMA was better when K > 38.00 D-AL ≤ 29.50 mm and when 36.00 < K ≤ 38.00 D-AL ≤ 26.50 mm; Barrett-TK was better when K ≤ 36.00 D and when K ≤ 38.00 D with AL > 29.50 mm; and both ALMA and Barrett-TK were better in other ranges (p < 0.05). A multi-formula approach, according to different ranges of K and AL, could improve refractive outcomes in post-myopic-LRS eyes.

Keywords: IOL power calculation; cataract surgery; multi-formula approach; refractive surgery.

Figure 1

Locally estimated scatterplot smoothing (LOESS) curve between refractive prediction errors generated for each examined formula and the related mean keratometry value. (Group A).

Figure 2

Locally estimated scatterplot smoothing (LOESS) curve between refractive prediction errors generated for each examined formula and the related axial length value. (Group A).

Figure 3

Locally estimated scatterplot smoothing (LOESS) curve between refractive prediction errors generated for each examined formula and the related axial length (AL)/mean keratometry (K) value. (Group A).

Figure 4

Locally estimated scatterplot smoothing (LOESS) curve between refractive prediction errors generated for each examined formula and the related mean keratometry value. (Group B).

Figure 5

Locally estimated scatterplot smoothing (LOESS) curve between refractive prediction errors generated for each examined formula and the related axial length value. (Group B).

Figure 6

Locally estimated scatterplot smoothing (LOESS) curve between refractive prediction errors generated for each examined formula and the related axial length (AL)/mean keratometry (K) value. (Group B).