Considerations of a thick lens formula for intraocular lens power calculation

The Influence of Lens Position, Vault Prediction, and Posterior Cornea on Phakic Posterior Chamber Intraocular Lens Power

BACKGROUND

Achieving precise refractive outcomes in phakic posterior chamber intraocular lens (pIOL) implantation is crucial for patient satisfaction. This study investigates factors affecting pIOL power calculations, focusing on myopic eyes, and evaluates the potential benefits of advanced predictive models.

DESIGN

Retrospective, single-center, algorithm improvement study.

METHODS

Various variations with different effective lens position (ELP) algorithms were analyzed. The algorithms included a fixed constant model, and a multiple linear regression model and were tested with and without incorporation of the posterior corneal curvature (Rcp). Furthermore, the impact of inserting the postoperative vault, the space between the pIOL and the crystalline lens, into the ELP algorithm was examined, and a simple vault prediction model was assessed.

RESULTS

Integrating Rcp and the measured vault into pIOL calculations did not significantly improve accuracy. Transitioning from constant model approaches to ELP concepts based on linear regression models significantly improved pIOL power calculations. Linear regression models outperformed constant models, enhancing refractive outcomes for both ICL and IPCL pIOL platforms.

CONCLUSIONS

This study underscores the utility of implementing ELP concepts based on linear regression models into pIOL power calculation. Linear regression based ELP models offered substantial advantages for achieving desired refractive outcomes, especially in lower to medium power pIOL models. For pIOL power calculations in both pIOL platforms we tested with preoperative measurements from a Scheimpflug device, we found improved results with the LION 1ICL formula and LION 1IPCL formula. Further research is needed to explore the applicability of these findings to a broader range of pIOL designs and measurement devices.

Prediction of IOL decentration, tilt and axial position using anterior segment OCT data

BACKGROUND

Intraocular lenses (IOLs) require proper positioning in the eye to provide good imaging performance. This is especially important for premium IOLs. The purpose of this study was to develop prediction models for estimating IOL decentration, tilt and the axial IOL equator position (IOLEQ) based on preoperative biometric and tomographic measures.

METHODS

Based on a dataset (N = 250) containing preoperative IOLMaster 700 and pre-/postoperative Casia2 measurements from a cataractous population, we implemented shallow feedforward neural networks and multilinear regression models to predict the IOL decentration, tilt and IOLEQ from the preoperative biometric and tomography measures. After identifying the relevant predictors using a stepwise linear regression approach and training of the models (150 training and 50 validation data points), the performance was evaluated using an N = 50 subset of test data.

RESULTS

In general, all models performed well. Prediction of IOL decentration shows the lowest performance, whereas prediction of IOL tilt and especially IOLEQ showed superior performance. According to the 95% confidence intervals, decentration/tilt/IOLEQ could be predicted within 0.3 mm/1.5°/0.3 mm. The neural network performed slightly better compared to the regression, but without significance for decentration and tilt.

CONCLUSION

Neural network or linear regression-based prediction models for IOL decentration, tilt and axial lens position could be used for modern IOL power calculation schemes dealing with 'real' IOL positions and for indications for premium lenses, for which misplacement is known to induce photic effects and image distortion.

Monte-Carlo simulation of a thick lens IOL power calculation

BACKGROUND

The purpose of this Monte-Carlo study is to investigate the effect of using a thick lens model instead of a thin lens model for the intraocular lens (IOL) on the resulting refraction at the spectacle plane and on the ocular magnification based on a large clinical data set.

METHODS

A pseudophakic model eye with a thin spectacle correction, a thick cornea (curvatures for both surfaces and central thickness) and a thick IOL (equivalent power PL derived from a thin lens IOL, Coddington factor CL (uniformly distributed from -1.0 to 1.0), either preset central thickness LT = 0.9 mm (A) or optic edge thickness ET = 0.2 mm, (B)) was set up. Calculations were performed on a clinical data set containing 21 108 biometric measurements of a cataractous population based on linear Gaussian optics to derive spectacle refraction and ocular magnification using the thin and thick lens IOL models.

RESULTS

A prediction model (restricted to linear terms without interactions) was derived based on the relevant parameters identified with a stepwise linear regression approach to provide a simple method for estimating the change in spectacle refraction and ocular magnification where a thick lens IOL is used instead of a thin lens IOL. The change in spectacle refraction using a thick lens IOL with (A) or (B) instead of a thin lens IOL with identical power was within limits of around ±1.5 dpt when the thick lens IOL was placed with its haptic plane at the plane of the thin lens IOL. In contrast, the change in ocular magnification from considering the IOL as a thick lens instead of a thin lens was small and not clinically significant.

CONCLUSION

This Monte-Carlo simulation shows the impact of using a thick lens model IOL with preset LT or ET on the resulting spherical equivalent refraction and ocular magnification. If IOL manufacturers would provide all relevant data on IOL design data and refractive index for all power steps, this would make it possible to perform direct calculations of refraction and ocular magnification.

Theoretical Impact of Intraocular Lens Design Variations on the Accuracy of IOL Power Calculations

To ascertain the theoretical impact of optical design variations of the intraocular lens (IOL) on the accuracy of IOL power formulas based on a single lens constant using a thick lens eye model. This impact was also simulated before and after optimization. We modeled 70 thick-lens pseudophakic eyes implanted with IOLs of symmetrical optical design and power comprised between 0.50 D and 35 D in 0.5-step increments. Modifications of the shape factor resulting in variations in the anterior and posterior radii of an IOL were made, keeping the central thickness and paraxial powers static. Geometry data from three IOL models were also used. Corresponding postoperative spherical equivalent (SE) were computed for different IOL powers and assimilated to a prediction error of the formula due to the sole change in optical design alone. Formula accuracy was studied before and after zeroization on a uniform and non-uniform realistic IOL power distribution. The impact of the incremental change in optic design variability depended on the IOL power. Design modifications theoretically induce an increase in the standard deviation (SD), Mean Absolute Error (MAE), and Root Mean Square (RMS) of the error. The values of these parameters reduce dramatically after zeroization. While the variations in optical design can affect refractive outcomes, especially in short eyes, the zeroization of the mean error theoretically reduces the impact of the IOL's design and power on the accuracy of IOL power calculation.