Refractive Astigmatism Outcomes of Femtosecond Laser-Assisted Arcuate Keratotomies Combined with Femtosecond Laser-Assisted Cataract Surgery: Two-Year Results.

PURPOSE: To evaluate the effectiveness and stability of refractive astigmatism reduction after penetrating femtosecond laser-assisted arcuate keratotomy performed at the time of femtosecond laser-assisted cataract surgery. METHODS: Non-randomized retrospective data analysis of all patients that underwent femtosecond laser-assisted cataract surgery with femtosecond laser-assisted arcuate keratotomy over a 4-year period with a non-toric monofocal intraocular lens (2017-2021) at a tertiary care academic center. Postoperative visual acuity, manifest refraction, and predicted residual refractive error were also recorded at 1 month, 3-6 months, 12-18 months, and 2 years postoperatively. Preoperative keratometric astigmatism was compared to postoperative refractive astigmatism using vector calculations and the ASCRS double-angle plot tool. RESULTS: This study comprised 266 eyes (179 patients) that met inclusion criteria. The mean preoperative keratometric astigmatism magnitude was 0.99 ± 0.53 D. At 1 month, 3-6 months, 12-18 months, and 2 years postoperatively, the mean refractive cylinder was 0.49 ± 0.45 D, 0.49 ± 0.45 D, 0.55 ± 0.54 D, and 0.52 ± 0.46 D, respectively. Horizontal against-the-rule astigmatism showed a higher tendency toward undercorrection than vertical with-the-rule astigmatism, which had a slightly higher tendency toward overcorrection. With-the-rule astigmatism had smaller difference vectors between target-induced astigmatism and surgically induced astigmatism. CONCLUSIONS: Femtosecond laser-assisted arcuate keratotomy performed at the time of femtosecond laser-assisted cataract surgery was an effective option for correcting low-to-moderate corneal astigmatism for up to 2 years.

Vector analysis of corneal astigmatism in cataractous eyes based on IOLMaster 700 biometry.

PURPOSE: The purpose of this study was to investigate the effect of the corneal back surface by comparing the keratometric astigmatism (K, derived from the corneal front surface) of a modern optical biometer against astigmatism of Total Keratometry (TK, derived from both corneal surfaces) in a large population with cataractous eyes. The results were then used to define linear prediction models to map K to TK. METHODS: From a large dataset containing bilateral biometric measurements (IOLMaster 700) in 9736 patients prior to cataract surgery, the total corneal astigmatism was decomposed into vectors for K, corneal back surface (BS), and TK. A multivariate prediction model (MV), simplified model with separation of vector components (SM) and a constant model (CM) were defined to map K to TK vector components. RESULTS: The K centroid (X/Y) showed some astigmatism with-the-rule (0.1981/-0.0211 dioptre (dpt)) whereas the TK centroid was located around zero (-0.0071/-0.0381 dpt against-the-rule) and the BS centroid showed systematic astigmatism against-the-rule (-0.2367/-0.0145 dpt). The respective TK-K centroid was located at -0.2052/-0.0302 dpt. The MV model showed the same performance (i.e. mean absolute residuum) as the SM did (0.1098 and 0.1099 dpt respectively) while the CM performed only slightly worse (0.1121 dpt mean absolute residuum). CONCLUSION: In cases where tomographic data are unavailable statistical models could be used to consider the overall contribution of the back surface to the total corneal astigmatism. Since the performance of the CM is sufficiently close to that of MV and SM we recommend using the CM which can be directly considered e.g. as surgically induced astigmatism.

Performance of a simplified strategy for formula constant optimisation in intraocular lens power calculation.

PURPOSE: To investigate the performance of a simple prediction scheme for the formula constants optimised for a mean refractive prediction error. METHODS: Analysis based on a dataset of 888 eyes before and after cataract surgery with IOL implantation (Hoya Vivinex). IOLMaster 700 biometric data, power of the implanted lens and postoperative spherical equivalent refraction were used to calculate the optimised constants (.)opt for SRKT, HofferQ, Holladay and Haigis formula with an iterative nonlinear optimisation. For detuning start values by ±1.5 from (.)opt, the predicted formula constants (.)pred were calculated and compared with (.)opt. Formula performance metrics mean (MPE), median (MEDPE), mean absolute (MAPE), median absolute (MEDAPE), root mean squared (RMSPE) and standard deviation (SDPE) of the formula prediction error were analysed for (.)opt and (.)pred. RESULTS: (.)pred - (.)opt showed a 2nd order parabolic behaviour with maximal deviations up to 0.09 at the tails of detuning and a minimal deviation up to -0.01 for all formulae. The performance curves of different metrics of PE as functions of detuning variations show that the formula constants for zeroing MPE and MEDPE yield almost identical formula constants, optimisation for MAPE, MEDAPE and RMSPE yielded formula constants very close to (.)opt, and optimisation for SDPE could result in formula constants up to 0.5 off (.)opt which is unacceptable for clinical use. CONCLUSION: This simple prediction scheme for formula constant optimisation for zero mean refraction error performs excellently in our monocentric dataset, even for larger deviations of the start value from (.)opt. Further studies with multicentric data and larger sample sizes are required to investigate the performance in a clinical setting further.

Leaving trusted paths: Estimating corneal keratometric index in cataract surgery eyes with zero-power implants.

PURPOSE: This study aimed to estimate the corneal keratometric index in the eyes of cataract surgery patients who received zero-power intraocular lenses (IOLs). METHODOLOGY: This retrospective study analyzed postoperative equivalent spherical refraction and axial length, mean anterior curvature radius and aqueous humor refractive index to calculate the theoretical corneal keratometric index value (nk). Data was collected from 2 centers located in France and Germany. RESULTS: Thirty-six eyes were analyzed. The results revealed a mean corneal keratometric index of 1.329 ± 0.005 for traditional axial length (AL) and 1.331 ± 0.005 for Cooke modified axial length (CMAL). Results ranged from minimum values of 1.318/1.320 to maximum values of 1.340/1.340. CONCLUSION: The corneal keratometric index is a crucial parameter for ophthalmic procedures and calculations, particularly for IOL power calculation. Notably, the estimated corneal keratometric index value of 1.329/1.331 in this study is lower than the commonly used 1.3375 index. These findings align with recent research demonstrating that the theoretical corneal keratometric index should be approximately 1.329 using traditional AL and 1.331 using CMAL, based on the ratio between the mean anterior and posterior corneal curvature radii (1.22).

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.

Comparison of Pre- and Post-DMEK Keratometry and Total Keratometry Values for IOL Power Calculations in Eyes Undergoing Triple DMEK.

PURPOSE: To evaluate prediction accuracy of pre- and post-DMEK keratometry (K) and total keratometry (TK) values for IOL power calculations in Fuchs endothelial corneal dystrophy (FECD) eyes undergoing DMEK with cataract surgery (triple DMEK). METHODS: Retrospective cross-sectional multicenter study of 55 FECD eyes (44 patients) that underwent triple DMEK between 2019 and 2022 between two centers in USA and Europe. Swept-source optical coherence tomography biometry (IOLMaster 700) was used for pre- and post-DMEK measurements. K and TK values were used for power calculations with ten formulae (Barrett Universal II (BUII), Castrop, Cooke K6, EVO 2.0, Haigis, Hoffer Q, Hoffer QST, Holladay I, Kane, and SRK/T). Mean error, mean absolute error (MAE), standard deviation, and percentage of eyes within ±0.50/±1.00 diopters (D) were calculated. Studied formulae were additionally adjusted using a method published previously (IOLup1D Method), which increases the IOL power by 1D. While both eyes from the same patient were considered for descriptive statistics, we restricted to one eye per individual (44 eyes for statistical comparisons. RESULTS: MAEs for all formulae were lower for post-DMEK K and TK than pre-DMEK K and TK by an average of 0.24 and 0.47 D, respectively. The lowest MAE was 0.49 D for Kane using post-DMEK TK, and the highest MAE was 1.05 D for BUII using pre-DMEK TK. Most IOLup1D formulae had lower MAEs than pre-DMEK K and TK formulae. CONCLUSIONS: The IOLup1D Method should be used instead of pre-DMEK K and TK values for triple DMEK in FECD eyes. Using post-DMEK TK values for cataract surgery after DMEK provides better refractive accuracy than any of the three studied methods used for triple DMEK procedures.

Impact of uncertainties in biometric parameters on intraocular lens power formula predicted refraction using a Monte-Carlo simulation.

PURPOSE: The purpose of this study was to investigate the uncertainty in the formula predicted refractive outcome REFU after cataract surgery resulting from measurement uncertainties in modern optical biometers using literature data for within-subject standard deviation Sw. METHODS: This Monte-Carlo simulation study used a large dataset containing 16 667 preoperative IOLMaster 700 biometric measurements. Based on literature Sw values, REFU was derived for both the Haigis and Castrop formulae using error propagation strategies. Using the Hoya Vivinex lens (IOL) as an example, REFU was calculated both with (WLT) and without (WoLT) consideration of IOL power labelling tolerances. RESULTS: WoLT the median REFU was 0.10/0.12 dpt for the Haigis/Castrop formula, and WLT it was 0.13/0.15 dpt. WoLT REFU increased systematically for short eyes (or high power IOLs), and WLT this effect was even more pronounced because of increased labelling tolerances. WoLT the uncertainty in the measurement of the corneal front surface radius showed the largest contribution to REFU, especially in long eyes (and low power IOLs). WLT the IOL power uncertainty dominated in short eyes (or high power IOLs) and the uncertainty of the corneal front surface in long eyes (or low power IOLs). CONCLUSIONS: Compared with published data on the formula prediction error of refractive outcome after cataract surgery, the uncertainty of biometric measures seems to contribute with ⅓ to ½ to the entire standard deviation. REFU systematically increases with IOL power and decreases with axial length.

Evaluation of statistical correction strategies for corneal back surface astigmatism with toric lenses: a vector analysis.

PURPOSE: To compare actual and formula-predicted postoperative refractive astigmatism using measured posterior corneal power measurements and 4 different empiric posterior corneal astigmatism correction models. SETTING: Tertiary care center. DESIGN: Single-center retrospective consecutive case series. METHODS: Using a dataset of 211 eyes before and after tIOL implantation (Hoya Vivinex), IOLMaster 700 (IOLM) or Casia2 (CASIA) keratometric and front/back surface corneal power measurements were converted to power vector components C0 (0/90 degrees) and C45 (45/135 degrees). Differences between postoperative and Castrop formula predicted refraction at the corneal plane using the labeled parameters of the tIOL and the keratometric or front/back surface corneal powers were recorded as the effect of corneal back surface astigmatism (BSA). RESULTS: Generally, the centroid of the difference shifted toward negative C0 values indicating that BSA adds some against the rule corneal astigmatism (ATR). From IOLM/CASIA keratometry, the average difference in C0 was 0.39/0.32 diopter (D). After correction with the Abulafia-Koch, Goggin, La Hood, and Castrop nomograms, it was -0.18/-0.24 D, 0.27/0.18 D, 0.13/0.08 D, and 0.17/0.10 D. Using corneal front/back surface data from IOLM/CASIA, the difference was 0.18/0.12 D. CONCLUSIONS: The Abulafia-Koch method overcorrected the ATR, while the Goggin, La Hood, and Castrop models slightly undercorrected ATR, and using measurements from the CASIA tomographer seemed to produce slightly less prediction error than IOLM.

Unilateral intraindividual comparison and bilateral performance of a monofocal spherical and diffractive extended depth of field intraocular lens mix-and-match approach.

BACKGROUND: To evaluate the intraindividual visual performance of a spherical and extended depth of field (EDOF) IOL used in a mix-and-match approach. METHODS: Single centre (tertiary care centre), retrospective consecutive case series. Included patients had uneventful cataract surgery with implantation of a spherical monofocal IOL (CT Spheris 204) in the dominant eye and a diffractive EDOF IOL (AT LARA 829) in the non-dominant eye. Monocular and binocular defocus curves and visual acuity at various distances were assessed. In addition, binocular reading speed, contrast sensitivity, and patient satisfaction using QOV, Catquest 9SF, and glare/halo questionnaires are reported. RESULTS: A total of 29 patients (58 eyes) were included. We observed significant intra-individual differences for monocular DCIVA, DCNVA, UIVA, and UNVA. There were no differences in monocular BCDVA or UDVA. The monocular defocus curves for the two IOLs significantly differed at defocus steps between -1.0 and -3.5 D. 93.10% of patients reported they would opt for the same combination of IOLs. CONCLUSION: Excellent uncorrected and corrected distance visual acuity was demonstrated in both groups. The mix-and-match approach described in this study yielded good intermediate vision and improved near vision with high-patient satisfaction.

Evaluation of corneal power from an AS-OCT thick lens model and ray tracing: reliability of the keratometer index.

PURPOSE: To investigate and compare different strategies of corneal power calculations using keratometry, paraxial thick lens calculations and ray tracing. SETTING: Tertiary care center. DESIGN: Retrospective single-center consecutive case series. METHODS: Using a dataset with 9780 eyes of 9780 patients from a cataractous population the corneal front (Ra/Qa) and back (Rp/Qp) surface radius/asphericity, central corneal thickness (CCT), and entrance pupil size (PUP) were recorded using the Casia 2 tomographer. Beside keratometry with the Zeiss (PK Z ) and Javal (PK J ) keratometer index, a thick lens paraxial formula (PG) and ray tracing (PR) was implemented to extract corneal power for pupil sizes from 2 mm to 5 mm in steps of 1 mm and PUP. RESULTS: With PUP PK Z /PK J overestimates the paraxial corneal power PG in around 97%/99% of cases and PR in around 80% to 85%/99%. PR is around 1/6 or 5/6 diopters (D) lower compared with PK Z or PK J . For a 2 mm pupil PR is around 0.20/0.91 D lower compared with PK Z /PK J and for a 5 mm pupil PR is comparable with PK Z (around 0.03 D lower) but around 0.70 to 0.75 D lower than PK J . CONCLUSIONS: "True" values of corneal power are mostly required in lens power calculations before cataract surgery, and overestimation of corneal power could induce trend errors in refractive outcome with axial length and lens power if compensated with the effective lens position.

Limitations of constant optimization with disclosed intraocular lens power formulae.

PURPOSE: To investigate the effect of formula constants on predicted refraction and limitations of constant optimization for classical and modern intraocular lens (IOL) power calculation formulae. SETTING: Tertiary care center. DESIGN: Retrospective single-center consecutive case series. METHODS: This analysis is based on a dataset of 888 eyes before and after cataract surgery with IOL implantation (Hoya Vivinex). Spherical equivalent refraction predSEQ was predicted using IOLMaster 700 data, IOL power, and formula constants from IOLCon ( https://iolcon.org ). The formula prediction error (PE) was derived as predSEQ minus achieved spherical equivalent refraction for the SRKT, Hoffer Q, Holladay, Haigis, and Castrop formulae. The gradient of predSEQ (gradSEQ) as a measure for the effect of the constants on refraction was calculated and used for constant optimization. RESULTS: Using initial formula constants, the mean PE was -0.1782 ± 0.4450, -0.1814 ± 0.4159, -0.1702 ± 0.4207, -0.1211 ± 0.3740, and -0.1912 ± 0.3449 diopters (D) for the SRKT, Hoffer Q, Holladay, Haigis, and Castrop formulas, respectively. gradSEQ for all formula constants (except gradSEQ for the Castrop R) decay with axial length because of interaction with the effective lens position (ELP). Constant optimization for a zero mean PE (SD: 0.4410, 0.4307, 0.4272, 0.3742, 0.3436 D) results in a change in the PE trend over axial length in all formulae where the constant acts directly on the ELP. CONCLUSIONS: With IOL power calculation formulae where the constant(s) act directly on the ELP, a change in constant(s) always changes the trend of the PE according to gradSEQ. Formulae where at least 1 constant does not act on the ELP have more flexibility to zero the mean or median PE without coupling with a PE trend error over axial length.

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

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 (ΔM) 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 ΔM 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 ΔM 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 ΔM 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 ΔM from 17 to - 25%. The linear regression considering anterior segment biometric data and the PIOLP was not capable of properly characterizing REF and ΔM, whereas the nonlinear model with a quadratic term for the PIOLP showed a good performance for both REF and ΔM 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 ΔM for the entire range of PIOL steps if a vergence calculation is unavailable.

Prediction of corneal power vectors after cataract surgery with toric lens implantation-A vector analysis.

BACKGROUND: Intraocular lenses are typically calculated based on a pseudophakic eye model, and for toric lenses (tIOL) a good estimate of corneal astigmatism after cataract surgery is required in addition to the equivalent corneal power. The purpose of this study was to investigate the differences between the preoperative IOLMaster (IOLM) and the preoperative and postoperative Casia2 (CASIA) tomographic measurements of corneal power in a cataractous population with tIOL implantation, and to predict total power (TP) from the IOLM and CASIA keratometric measurements. METHODS: The analysis was based on a dataset of 88 eyes of 88 patients from 1 clinical centre before and after tIOL implantation. All IOLM and CASIA keratometric and total corneal power measurements were converted to power vector components, and the differences between preoperative IOLM or CASIA and postoperative CASIA measurements were assessed. Feedforward neural network and multivariate linear regression prediction algorithms were implemented to predict the postoperative total corneal power (as a reference for tIOL calculation) from the preoperative IOLM and CASIA keratometric measurements. RESULTS: On average, the preoperative IOLM keratometric / total corneal power under- / overestimates the postoperative CASIA keratometric / real corneal power by 0.12 dpt / 0.21 dpt. The prediction of postoperative CASIA real power from preoperative IOLM or CASIA keratometry shows that postoperative total corneal power is systematically (0.18 dpt / 0.27 dpt) shifted towards astigmatism against the rule, which is not reflected by keratometry. The correlation of postoperative CASIA real power to the corresponding preoperative CASIA values is better than those as compared to the preoperative IOLM keratometry. However, there is a large variation from preoperative IOLM or CASIA keratometry to the postoperative CASIA real power of up to 1.1 dpt (95% confidence interval). CONCLUSION: One of the challenges of tIOL calculation is the prediction of postoperative total corneal power from preoperative keratometry. Keratometric power restricted to a front surface measurement does not fully reflect the situation of corneal back surface astigmatism, which typically adds some extra against the rule astigmatism.

Characteristics of surgically induced astigmatism after standardized microincisional cataract surgery with a superior limbal incision.

PURPOSE: To determine (1) if measurements of surgically induced astigmatism (SIA) as measured by keratometry (K) and total keratometry (TK) differ (2) if SIA affects the magnitude and/or meridian of keratometric astigmatism (3) if SIA evolves over time. SETTING: Tertiary care center. DESIGN: Retrospective data analysis. METHODS: A swept-source optical coherence tomography biometry dataset (IOLMaster700) consisting of 498 eyes (327 patients) from a tertiary care center was analyzed. For all eyes preoperative and postoperative biometric measurements at 1-month, 3-month, and 6-months postoperative visits were considered for vector analysis of SIA K and SIA TK . RESULTS: Centroids in right and left eyes were 0.26 diopters (D) @5 degrees/0.31 D @1 degree for SIA K and 0.27 D @4 degrees/0.34 D @1 degree for SIA TK . Centroids for difference vectors K-TK in right and left eyes were 0.02 D @ 176 degrees/0.03 D @6 degrees. The mean SIA magnitudes in right and left eyes were 0.48 ± 0.41 D and 0.50 ± 0.37 D for SIA K and 0.53 ± 0.42 D and 0.54 ± 0.40 D for SIA TK . In eyes with ATR astigmatism, an increase in postoperative astigmatism magnitude was more common than a decrease. More than 30% of eyes showed changes in the meridian of more than 15 degrees. CONCLUSIONS: Overall, we observed differences in K- and TK-derived SIA, and changes in SIA magnitude over time. For postsurgical interventions, postoperative astigmatism meridian values should be measured to base treatments. Astigmatism magnitude showed a tendency to decrease for steep-meridian incisions and to increase in flat-meridian incisions.

Prediction of spectacle refraction uncertainties with discrete IOL power steps and manufacturing tolerances according to ISO using a Monte Carlo model.

PURPOSE: The purpose of this study was to develop a concept for predicting the effects of both discrete intraocular lens (IOL) power steps (PS) and power labelling tolerances (LT) on the uncertainty of the refractive outcome (REFU). DESIGN: Retrospective non-randomised cross-sectional Monte Carlo simulation study. METHODS: We evaluated a dataset containing 16 669 IOLMaster 700 preoperative biometric measurements. The PS and the delivery range of two modern IOLs (Bausch and Lomb enVista and Alcon SA60AT) were considered for this Monte Carlo simulation. The uncertainties from PS or LT were assumed to be normally distributed according to ±½ the IOL PS or the ISO 11979 LT. REFU was recorded and analysed for all simulations. RESULTS: With both lenses the REFU from discrete PS ranged from 0.11 to 0.12 dpt. Due to the larger PS for low/high power lenses with the enVista/SA60AT, REFU is more dominant in initially myopic/hyperopic eyes. REFU from LT ranged from 0.18 to 0.19 dpt for both lenses. Since LT increases stepwise with IOL power, REFU is more prevalent in initially hyperopic eyes requiring high IOL power values, and for lenses with a wide delivery range towards higher powers. CONCLUSIONS: Since surgeons and patients are typically aware of the effect of discrete PS on REFU, these might be tolerated in cataract surgery. However, REFU resulting from LT is inevitable while the true measured IOL power is not reported on the package, leading to background noise in postoperative achieved refraction.

Therapeutic visual rehabilitation in a patient with high hyperopia and flat cornea years after radial keratotomy.

A 51-year-old man was referred for refractive surgery evaluation. Spectacle dependence and poor visual quality in both eyes was his chief complaint. He cannot tolerate contact lenses. Corrected distance visual acuity (CDVA) was 20/40 in both eyes. Manifest refraction was +5.25 -2.25 @ 90 (20/40) in the right eye and +6.25 -2.25 @ 105 (20/40) in the left eye. The patient had a history of radial keratotomy (RK) almost 30 years ago in both eyes and at the slitlamp presented 8 RK incisions, proportionally spaced between one another. All incisions were closed, and there were no relevant signs of scarring. The patient denied any history of ocular trauma, systemic disease, or medications. Corneal topography with different technologies revealed an irregular pattern with marked central flattening in both eyes, with some points below 30 diopters (D) (Supplemental Figures 1 and 2, available at http://links.lww.com/JRS/A862 and http://links.lww.com/JRS/A863, respectively). There were no signs of cataract, and fundus examination was normal. Optical coherence tomography (OCT) of the right eye revealed a more homogeneous thickness pattern, little variation between the thinnest and thickest areas, and adequate transparency (Figure 1JOURNAL/jcrs/04.03/02158034-202306000-00018/figure1/v/2023-05-31T172126Z/r/image-tiff). In the left eye, there is wide variability between the thinnest and thickest stromal points, with annular thinning and central thickening (Figure 2JOURNAL/jcrs/04.03/02158034-202306000-00018/figure2/v/2023-05-31T172126Z/r/image-tiff). Both eyes show marked epithelial irregularity. Considering this patient's current ocular status, how would you reach visual rehabilitation? Because he is contact lens intolerant, would you consider surface ablation, for example, photorefractive keratectomy (PRK) with mitomycin-C (MMC)? If that were the case, would you think of an optimized or a topography-guided (TG) treatment? Would you immediately consider a corneal transplant option? Would you instead consider a more conservative approach? Which one and why?

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.

Preconditioning of clinical data for intraocular lens formula constant optimisation using Random Forest Quantile Regression Trees.

PURPOSE: To implement a fully data driven strategy for identifying outliers in clinical datasets used for formula constant optimisation, in order to achieve proper formula predicted refraction after cataract surgery, and to assess the capabilities of this outlier detection method. METHODS: 2 clinical datasets (DS1/DS2: N = 888/403) of eyes treated with a monofocal aspherical intraocular lens (Hoya XY1/Johnson&Johnson Vision Z9003) containing preoperative biometric data, power of the lens implant and postoperative spherical equivalent (SEQ) were transferred to us for formula constant optimisation. Original datasets were used to generate baseline formula constants. A random forest quantile regression algorithm was set up using bootstrap resampling with replacement. Quantile regression trees were grown and the 25% and 75% quantile, and the interquartile range were extracted from SEQ and formula predicted refraction REF for the SRKT, Haigis and Castrop formulae. Fences were defined from the quantiles and data points outside the fences were marked and removed as outliers before recalculating the formula constants. RESULTS: NB = 1000 bootstrap samples were derived from both datasets, and random forest quantile regression trees were grown to model SEQ versus REF and to estimate the median and 25% and 75% quantiles. The fence boundaries were defined as being from 25% quantile - 1.5·IQR to 75% quantile + 1.5·IQR, with data points outside the fence being marked as outliers. In total, for DS1 and DS2, 25/27/32 and 4/5/4 data points were identified as outliers for the SRKT/Haigis/Castrop formulae respectively. The respective root mean squared formula prediction errors for the three formulae were slightly reduced from: 0.4370 dpt;0.4449 dpt/0.3625 dpt;0.4056 dpt/and 0.3376 dpt;0.3532 dpt to: 0.4271 dpt;0.4348 dpt/0.3528 dpt;0.3952 dpt/0.3277 dpt;0.3432 dpt for DS1;DS2. CONCLUSION: We were able to prove that with random forest quantile regression trees a fully data driven outlier identification strategy acting in the response space is achievable. In a real life scenario this strategy has to be complemented by an outlier identification method acting in the parameter space for a proper qualification of datasets prior to formula constant optimisation.

Standard vs total keratometry for intraocular lens power calculation in cataract surgery combined with DMEK.

PURPOSE: To compare the prediction accuracy of standard keratometry (K) and total keratometry (TK) for intraocular lens (IOL) power calculation in eyes undergoing combined cataract surgery and Descemet membrane endothelial keratoplasty (triple DMEK). SETTING: Tertiary care academic referral center. DESIGN: Retrospective case series. METHODS: Review of 83 eyes (63 patients) that underwent triple DMEK between 2019 and 2021. Biometry measurements were obtained using a swept-source optical biometer (IOLMaster 700). 63 eyes were used for statistical analysis. Mean error, mean absolute error (MAE), SD, median absolute error, maximum absolute error, root mean squared prediction error, and the percentage of eyes within prediction errors of ±0.50 diopters (D) and ±1.00 D were calculated for 9 multivariate and third-generation formulas using K and TK values (Barrett Universal II, Yeo EVO 2.0, Cooke K6, Kane, Pearl-DGS, Haigis, Holladay 1, Hoffer Q, and SRK/T). Formulas were additionally tested by using the prediction for an IOL power 1 D below the IOL used (IOLup1D). RESULTS: For all formulas, MAE was lower for K than for TK by an average of 0.21 D. The lowest MAE value observed was 0.67 D for "adjusted" SRK/T using K, and the highest MAE values observed were 1.24 D and 1.24 D for nonadjusted Hoffer Q and Haigis using TK, respectively. Overall, lower MAE values were observed for multivariate formulas and SRK/T. CONCLUSIONS: In triple DMEK eyes, the prediction accuracy of K was higher than that of TK. The most accurate formulas were SRK/T and multivariate formulas using K with the IOLup1D adjustment.