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Spherical equivalent prediction analysis in intraocular lens power calculations using Eyetemis: a comprehensive
Yoav Kan-Tor1, Adi Abulafia, David Zadok
1From the The Alexander Grass Center for Bioengineering, School of Computer Science and Engineering, Hebrew University of Jerusalem, Jerusalem, Israel (Kan-Tor); Department of Ophthalmology, Shaare Zedek Medical Center, and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel (Abulafia, Zadok); Department of Ophthalmology, Goethe University, Frankfurt, Germany (Kohnen); IRCCS Bietti Foundation, Rome, Italy (Savini); Department of Ophthalmology, Stein Eye Institute, University of California, Los Angeles, California (Hoffer); St. Mary's Eye Center, Santa Monica, California (Hoffer); Department of Statistics and Data Science, The Hebrew University of Jerusalem, Jerusalem, Israel (Benjamini).
Purpose:
To compare 2 different datasets, using Eyetemis, an online analytical tool designed for assessing the spherical equivalent prediction errors (SEQ-PEs) of intraocular lens (IOL) power calculation formulas after cataract surgery.
Setting:
Institutional.
Design:
Retrospective case series.
Methods:
The study comprised 2 distinct datasets of patients who had undergone successful cataract surgery. Dataset 1 includes standard eyes, whereas Dataset 2 includes eyes with keratoconus. An online tool was used for SEQ-PE analysis across the 2 datasets, adhering to ISO standards for evaluating accuracy based on trueness and precision. The tool incorporates robust t tests for comparing the trimmed mean of the data, adjusting for heteroscedasticity. IOL constants in Dataset 1 were optimized for the comparison of Hoffer Q, Holladay 1, SRK/T, Haigis, and Barrett Universal II (BUII) formulas. In Dataset 2, IOL constants from the IOLCon website were used for the comparison of the BUII and its designated KCN version: Barrett TrueK Keratoconus (TrueK [KCN]).
Results:
For Dataset 1, the trimmed mean SEQ-PE values of all formulas were not significantly different from zero. BUII had superior precision and accuracy compared with all other formulas, except from Haigis ( P ≤ .04). For Dataset 2, BUII's trimmed-mean SEQ-PE was significantly different from zero (0.59 diopters [D], P < .01), unlike the TrueK (KCN) (0.12 D, P = .10). In addition, TrueK (KCN) exhibited enhanced precision and accuracy relative to BUII ( P < .01).
Conclusions:
The online analysis tool provides a streamlined approach for assessing the prediction accuracy of SEQ refraction after cataract surgery, effectively evaluating trueness, precision, and overall accuracy through the use of advanced statistical methods.
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