Monte-Carlo simulation for calculating phakic supplementary lenses based on a thick and thin lens model using anterior segment OCT data

• Published in: Graefe's archive for clinical and experimental ophthalmology 2024-05, Vol.262 (5), p.1553-1565
• Main Authors: Langenbucher, Achim, Cayless, Alan, Kormanyos, Kitti, Wendelstein, Jascha, Hoffmann, Peter, Szentmáry, Nóra
• Format: Article
• Language: English
• Subjects: Archives & records; Biometrics; Cataract; Cataracts; Contraindications; Cornea; Datasets; Eye surgery; Medicine; Medicine & Public Health; Monte Carlo simulation; Ophthalmology; Regression analysis; Simulation
• ISSN: 0721-832X; 1435-702X
• DOI: 10.1007/s00417-023-06331-7

Background Phakic lenses (PIOLs, the most common and only disclosed type being the implantable collamer lens, ICL) are used in patients with large or excessive ametropia in cases where laser refractive surgery is contraindicated. The purpose of this study was to present a strategy based on anterior segment OCT data for calculating the refraction correction (REF) and the change in lateral magnification (Δ M ) with ICL implantation. Methods Based on a dataset ( N  = 3659) containing Casia 2 measurements, we developed a vergence-based calculation scheme to derive the REF and gain or loss in Δ M on implantation of a PIOL having power PIOLP. The calculation concept is based on either a thick or thin lens model for the cornea and the PIOL. In a Monte-Carlo simulation considering, all PIOL steps listed in the US patent 5,913,898, nonlinear regression models for REF and Δ M were defined for each PIOL datapoint. Results The calculation shows that simplifying the PIOL to a thin lens could cause some inaccuracies in REF (up to ½ dpt) and Δ M for PIOLs with high positive power. The full range of listed ICL powers (− 17 to 17 dpt) could correct REF in a range from − 17 to 12 dpt with a change in Δ M from 17 to − 25%. The linear regression considering anterior segment biometric data and the PIOLP was not capable of properly characterizing REF and Δ M , whereas the nonlinear model with a quadratic term for the PIOLP showed a good performance for both REF and Δ M prediction. Conclusion Where PIOL design data are available, the calculation concept should consider the PIOL as thick lens model. For daily use, a nonlinear regression model can properly predict REF and Δ M for the entire range of PIOL steps if a vergence calculation is unavailable.