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Li Wang, Sabite Gokce, John Zeiter, Mitchell P Weikert, Warren Hill, Douglas D Koch; Comparison of six IOL power calculation formulas in eyes with axial length ≤22 mm. Invest. Ophthalmol. Vis. Sci. 2016;57(12):919.
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© ARVO (1962-2015); The Authors (2016-present)
To compare the accuracy of refractive prediction of six IOL power calculation formulas in eyes with axial length (AL) ≤22 mm.
We evaluated six IOL power calculation formulas: 4 standard formulas (Holladay 2, Holladay 1, Hoffer Q, and Haigis) and 2 newer formulas (Olsen and Barrett). Consecutive cases that had cataract surgery from January 2011 to November 2015 with AL ≤22 mm were reviewed. Inclusion criteria were: 1) biometric measurements with Lenstar (Haag-Streit AG), 2) no previous ocular surgery or intraoperative or postoperative complications, and 3) postoperative best-spectacle corrected visual acuity of 20/30 or better at 3 weeks or longer. The refractive prediction error (RPE) was calculated as the difference between the actual refractive outcome postoperatively and the predicted refraction using each formula. Lens constants in each formula were optimized. The median absolute refractive prediction error was calculated.
In 77 eyes, for Holladay 2, Holladay 1, Hoffer Q, Haigis, Olsen and Barrett, respectively, the mean RPE values were -0.31 ± 0.47 D, -0.03 ± 0.50 D, -0.21 ± 0.49 D, -0.09 ± 0.54 D, -0.03 ± 0.49 D, and +0.28 ± 0.50 D; the mean RPEs with Holladay 2, Hoffer Q, and Olsen were significantly different from zero (all P<0.05). The median RPE values were 0.38 D, 0.38 D, 0.39 D, 0.41 D, 0.39 D, and 0.41 D, respectively, there were no significant differences among formulas (P>0.05); % of eyes within 0.5 D and 1.0 D of RPE were 71% and 97%, 73% and 97%, 68% and 99%, 64% and 90%, 70% and 95%, and 64% and 95%, respectively, there were no significant differences among formulas (P>0.05).
There were no significant differences among the standard and newer formulas in short eyes. Further studies exploring factors contributing to refractive prediction errors are desirable.
This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.
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