Comparison of 2 modern swept-source optical biometers—IOLMaster 700 and Anterion

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.

Differences Between Keratometry and Total Keratometry Measurements in a Large Dataset Obtained With a Modern Swept Source Optical Coherence Tomography Biometer

PURPOSE

This study aimed to explore the concept of total keratometry (TK) by analyzing extensive international datasets representing diverse ethnic backgrounds. The primary objective was to quantify the disparities between traditional keratometry (K) and TK values in normal eyes and assess their impact on intraocular lens (IOL) power calculations using various formulas.

DESIGN

Retrospective multicenter intra-instrument reliability analysis.

METHODS

The study involved the analysis of biometry data collected from ten international centers across Europe, the United States, and Asia. Corneal power was expressed as equivalent power and astigmatic vector components for both K and TK values. The study assessed the influence of these differences on IOL power calculations using different formulas. The results were analyzed and plotted using Bland-Altman and double angle plots.

RESULTS

The study encompassed a total of 116,982 measurements from 57,862 right eyes and 59,120 left eyes. The analysis revealed a high level of agreement between K and TK values, with 93.98% of eyes exhibiting an absolute difference of 0.25 D or less. Astigmatism vector differences exceeding 0.25 D and 0.50 D were observed in 39.43% and 1.08% of eyes, respectively.

CONCLUSIONS

This large-scale study underscores the similarity between mean K and TK values in healthy eyes, with rare clinical implications for IOL power calculation. Noteworthy differences were observed in astigmatism values between K and TK. Future investigations should delve into the practicality of TK values for astigmatism correction and their implications for surgical outcomes.

Repeatability of biometric measures from the IOLMaster 700 in a cataractous population

PURPOSE

The purpose of this study was to investigate the repeatability of biometric measures and also to assess the interactions between the uncertainties in these measures for use in an error propagation model, using data from a large patient cohort.

METHODS

In this cross-sectional non-randomised study we evaluated a dataset containing 3379 IOLMaster 700 biometric measurements taken prior to cataract surgery. Only complete scans with at least 3 successful measurements for each eye performed on the same day were considered. The mean (Mean) and standard deviations (SD) for each sequence of measurements were derived and analysed. Correlations between the uncertainties were assessed using Spearman rank correlations.

RESULTS

In the dataset with 677 eyes matching the inclusion criteria, the within subject standard deviation and repeatability for all parameters match previously published data. The SD of the axial length (AL) increased with the Mean AL, but there was no noticeable dependency of the SD of any of the other parameters on their corresponding Mean value. The SDs of the parameters are not independent of one another, and in particular we observe correlations between those for AL, anterior chamber depth, aqueous depth, lens thickness and corneal thickness.

CONCLUSIONS

The SD change over Mean for AL measurement and the correlations between the uncertainties of several biometric parameters mean that a simple Gaussian error propagation model cannot be used to derive the effect of biometric uncertainties on the predicted intraocular lens power and refraction after cataract surgery.

Influence and predictive value of optional parameters in new-generation intraocular lens formulas

PURPOSE

To evaluate the accuracy of various variations of new-generation multivariate intraocular lens (IOL) power calculation using the Barrett Universal II, Castrop, Emmetropia Verifying Optical 2.0, Hill-Radial Basis Function 3.0, Kane, and PEARL-DGS formulas with and without optional biometric parameters.

SETTING

Tertiary care academic medical center.

DESIGN

Retrospective case series. Single-center study.

METHODS

Inclusion of patients after uneventful cataract surgery implanting AU00T0 IOLs. Data from one eye per patient were randomly included. Eyes with a corrected distance visual acuity worse than 0.1 logMAR were excluded. IOLCON-optimized constants were used for all formulas other than the Castrop formula. The outcome measures were prediction error (PE) and absolute prediction error (absPE) for the 6 study formulas.

RESULTS

251 eyes from 251 patients were assessed. Excluding lens thickness led to statistically significant differences in absPE in several formulas. Leaving out horizontal corneal diameter did not impact absPE in several formulas. Differences in PE offset were observed between the various formula variations.

CONCLUSIONS

When using multivariate formulas with an A-constant, including certain optional parameters is vital for optimal refractive results. Formula variations excluding certain biometric parameters need specifically optimized constants and do not perform similarly when using the constant of the respective formula using all parameters.