Effect of pharmacological pupil dilation on measurements and iol power calculation made using the new swept-source optical coherence tomography-based optical biometer

Purpose: to determine whether pupil dilation affects biometric measurements and intraocular lens (IOL) power calculation made using the new swept-source optical coherence tomography-based optical biometer (IOLMaster 700©; Carl Zeiss Meditec, Jena, Germany). Procedures: eighty-one eyes of 81 patients evaluated for cataract surgery were prospectively examined using the IOLMaster 700© before and after pupil dilation with tropicamide 1%. The measurements made were: axial length (AL), central corneal thickness (CCT), aqueous chamber depth (ACD), lens thickness (LT), mean keratometry (MK), white-to-white distance (WTW) and pupil diameter (PD). Holladay II and SRK/T formulas were used to calculate IOL power. Agreement between measurement modes (with and without dilation) was assessed through intraclass correlation coefficients (ICC) and Bland-Altman plots. Results: mean patient age was 75.17 ± 7.54 years (range: 57–92). Of the variables determined, CCT, ACD, LT and WTW varied significantly according to pupil dilation. Excellent intraobserver correlation was observed between measurements made before and after pupil dilation. Mean IOL power calculation using the Holladay 2 and SRK/T formulas were unmodified by pupil dilation. Conclusions: the use of pupil dilation produces statistical yet not clinically significant differences in some IOLMaster 700© measurements. However, it does not affect mean IOL power calculation.

Agreement and clinical comparison between a new swept-source optical coherence tomography-based optical biometer and an optical low-coherence reflectometry biometer

Purpose To compare measurements taken using a swept-source optical coherence tomography-based optical biometer (IOLmaster 700) and an optical low-coherence reflectometry biometer (Lenstar 900), and to determine the clinical impacts of differences in their measurements on intraocular lens (IOL) power predictions. Methods Eighty eyes of 80 patients scheduled to undergo cataract surgery were examined with both biometers. The measurements made using each device were axial length (AL), central corneal thickness (CCT), aqueous depth (AQD), lens thickness (LT), mean keratometry (MK), white-to-white distance (WTW), and pupil diameter (PD). Holladay 2 and SRK/T formulas were used to calculate IOL power. Differences in measurement between the two biometers were determined using the paired t-test. Agreement was assessed through intraclass correlation coefficients (ICC) and Bland–Altman plots. Results Mean patient age was 76.3±6.8 years (range 59–89). Using the Lenstar, AL and PD could not be measured in 12.5 and 5.25% of eyes, respectively, while IOLMaster 700 took all measurements in all eyes. The variables CCT, AQD, LT, and MK varied significantly between the two biometers. According to ICCs, correlation between measurements made with both devices was excellent except for WTW and PD. Using the SRK/T formula, IOL power prediction based on the data from the two devices were statistically different, but differences were not clinically significant. Conclusions No clinically relevant differences were detected between the biometers in terms of their measurements and IOL power predictions. Using the IOLMaster 700, it was easier to obtain biometric measurements in eyes with less transparent ocular media or longer AL.

A Diagnostic Calculator for Detecting Glaucoma on the Basis of Retinal Nerve Fiber Layer, Optic Disc, and Retinal Ganglion Cell Analysis by Optical Coherence Tomography

Purpose: The purpose of this study was to develop and validate a multivariate predictive model to detect glaucoma by using a combination of retinal nerve fiber layer (RNFL), retinal ganglion cell-inner plexiform (GCIPL), and optic disc parameters measured using spectral-domain optical coherence tomography (OCT). Methods: Five hundred eyes from 500 participants and 187 eyes of another 187 participants were included in the study and validation groups, respectively. Patients with glaucoma were classified in five groups based on visual field damage. Sensitivity and specificity of all glaucoma OCT parameters were analyzed. Receiver operating characteristic curves (ROC) and areas under the ROC (AUC) were compared. Three predictive multivariate models (quantitative, qualitative, and combined) that used a combination of the best OCT parameters were constructed. A diagnostic calculator was created using the combined multivariate model. Results: The best AUC parameters were: inferior RNFL, average RNFL, vertical cup/disc ratio, minimal GCIPL, and inferior-temporal GCIPL. Comparisons among the parameters did not show that the GCIPL parameters were better than those of the RNFL in early and advanced glaucoma. The highest AUC was in the combined predictive model (0.937; 95% confidence interval, 0.911–0.957) and was significantly (P = 0.0001) higher than the other isolated parameters considered in early and advanced glaucoma. The validation group displayed similar results to those of the study group. Conclusions: Best GCIPL, RNFL, and optic disc parameters showed a similar ability to detect glaucoma. The combined predictive formula improved the glaucoma detection compared to the best isolated parameters evaluated. The diagnostic calculator obtained good classification from participants in both the study and validation groups.