New method to assess the accuracy of intraocular lens power calculation formulas according to ocular biometric parameters

Fernández, Joaquín; Rodríguez‐Vallejo, Manuel; Poyales, Francisco; Burguera, Noemí; Garzón, Nuria

doi:10.1097/j.jcrs.0000000000000165

Cited by 11 publications

(8 citation statements)

References 28 publications

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“… 2018 ; Fernández et al. 2020 ). Conversely, for eyes with biometric values that deviate from the average, accuracy can vary depending on the formulas (Gavin & Hammond 2008 ; Eom et al.…”

Section: Introductionmentioning

confidence: 99%

Algorithmic intraocular lens power calculation formula selection by keratometry, anterior chamber depth and axial length

Kim¹,

Eom

Yoon

et al. 2021

Acta Ophthalmologica

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Purpose To compare the prediction accuracy of algorithmic intraocular lens (IOL) power calculation formula selection method using conventional formulas (Haigis, Hoffer Q, Holladay 1, SRK/T and/or Barrett Universal II) based on keratometry (K), anterior chamber depth (ACD) and axial length (AL). Methods A total of 1653 patients (1653 eyes) implanted with Tecnis ZCB00 IOL during cataract surgery were enrolled in this study. Intraocular lens (IOL) power calculation formulas with a small absolute value in the sum of the area under the curve measured by K, ACD and AL subgroup were selected to calculate IOL power in the relevant biometry subgroup. The median absolute error (MedAE) calculated by the Haigis, Hoffer Q, Holladay 1, SRK/T and Barrett Universal II formulas individually was compared to that calculated by the algorithmic selection method using four formulas, Haigis, Hoffer Q, Holladay 1 and SRK/T, or five formulas when Barrett is included. Results The MedAE was 0.27 D in the Haigis, 0.30 D in the Hoffer Q, 0.27 D in the Holladay 1, 0.29 D in the SRK/T and 0.26 D in the Barrett Universal II formulas. The MedAEs determined by the algorithmic selection method using four (019 D) and five (0.21 D) formulas were significantly lower than those by the conventional IOL power calculation formulas. Conclusions The IOL power calculation formula selection method by biometry subgroup combined with biometric parameters K, ACD and AL may offer a more superior postoperative refractive error prediction in cataract surgery.

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“… 2018 ; Fernández et al. 2020 ). Conversely, for eyes with biometric values that deviate from the average, accuracy can vary depending on the formulas (Gavin & Hammond 2008 ; Eom et al.…”

Section: Introductionmentioning

confidence: 99%

Algorithmic intraocular lens power calculation formula selection by keratometry, anterior chamber depth and axial length

Kim¹,

Eom

Yoon

et al. 2021

Acta Ophthalmologica

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“…The relationships between the AXL and IOL power with the hyperopic shift suggest that this effect should be particularly investigated in future studies, putting special emphasis on short eyes implanted with a high IOL power for which the ALP variation can produce a higher change in SE than IOLs with a lower power. Although conclusions could not be made through the descriptive analysis of PE for several biometric parameters, our results also suggest that eyes with longer LT and bigger WTW should be further explored in future studies, having not previously been described for the Barrett formula [ 15 ]. On the other hand, in our study, the percentages of eyes in ± 0.50 D and ± 1.00 D after refraction stabilization were 67% and 90%, respectively.…”

Section: Discussionmentioning

confidence: 76%

“…The assessment of confounding factors as corneal power changes between 1 and 12 months was evaluated with the paired t -test. The influence of biometric parameters and PE was descriptively evaluated with plots previously described in the literature [ 15 ]. The association between PCO or Nd:YAG and PE changes was evaluated with the Fisher’s exact test.…”

Section: Methodsmentioning

confidence: 99%

Prediction Error Stabilization and Long-Term Standard Results with a Monofocal Intraocular Lens

Macías-Murelaga

Garay‐Aramburu

Bergado-Mijangos

et al. 2022

Vision

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The aim of this study was to assess the stability and differences between objective (O-Rx) and subjective (S-Rx) refraction for the assessment of the prediction error (PE). A secondary aim was to report the results of a monofocal intraocular lens (IOL). 100 subjects were included for whom S-Rx and O-Rx were obtained for all visits, and for visual performance, posterior capsular opacification incidence and Nd:YAG rates at 12 months. Either S-Rx and O-Rx showed a hyperopic shift from 1 to 6 months (p < 0.05) and stabilization after 6 months. S-Rx was related with the axial length (rho = −0.29, p = 0.007), obtaining a major tendency towards hyperopia in short eyes implanted with high-power IOLs. O-Rx showed a myopic shift in comparison to S-Rx (p < 0.05). This resulted in a decrease of the number of eyes in ±0.50 D and ±1.00 D from 79 to 67% and from 94 to 90%, respectively. The median (interquartile range) uncorrected and corrected visual acuities were 0.1 (0.29) and 0 (0.12) logMAR, respectively, and seven eyes required Nd:YAG capsulotomy at 12 months. Some caution should be taken in PE studies in which O-Rx is used or S-Rx is measured in a 1-month follow-up. Constant optimization should be conducted for this IOL after S-Rx stabilization.

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“…This also allows the simple calculation of area using the trapezoidal rule. 15 Curvilinear regression (fitting a curve) finds a mathematical expression that produces a curved line to be the closest or exact fit to the measured data points, when the relationship between the variables is non-linear. 14 The validity of this curvilinear regression must be assessed statistically, and there are five main models that can be used to assess GOF.…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, a piecewise linear function can be fitted to the defocus curve data which, like a spline curve, is guaranteed to connect to all data points on the curve. This also allows the simple calculation of area using the trapezoidal rule 15 …”

Section: Introductionmentioning

confidence: 99%

Optimising curve fitting techniques to look for standardisation of the analysis of defocus curves derived from multifocal intraocular lenses

Law

Buckhurst

Aggarwal

et al. 2022

Ophthalmic Physiologic Optic

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Introduction:To establish the most appropriate curve fitting method to allow accurate comparison of defocus curves derived from intraocular lenses (IOLs). Methods: Defocus curves were plotted in five IOL groups (monofocal, extended depth of focus, refractive bifocal, diffractive bifocal and trifocal). Polynomial curves from 2nd to 11th order and cubic splines were fitted. Goodness of fit (GOF) was assessed using five methods: least squares, coefficient of determination (R 2 adj ), Akaike information criteria (AIC), visual inspection and Snedecor and Cochran. Additional defocus steps at −2.25 D and −2.75 D were measured and compared to the calculated visual acuity (VA) values. Area under the defocus curve and range of focus were also compared.Results: Goodness of fit demonstrated variable results, with more lenient methods such as R 2 adj leading to overfitting and conservative methods such as AIC resulting in underfitting. Furthermore, conservative methods diminished the inflection points resulting in an underestimation of VA. Polynomial of at least 8th order was required for comparison of area methods, but overfitted the EDoF and monofocal groups; the spline curve was consistent for all IOLs and methods. Conclusions:This study demonstrates the inherent difficulty of selecting a single polynomial function. The R 2 method can be used cautiously along with visual inspection to guard against overfitting. Spline curves are suitable for all IOLs, guarding against the issues of overfitting. Therefore, for analysis of the defocus profile of IOLs, the fitting of a spline curves is advocated and should be used wherever possible.

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New method to assess the accuracy of intraocular lens power calculation formulas according to ocular biometric parameters

Cited by 11 publications

References 28 publications

Algorithmic intraocular lens power calculation formula selection by keratometry, anterior chamber depth and axial length

Algorithmic intraocular lens power calculation formula selection by keratometry, anterior chamber depth and axial length

Prediction Error Stabilization and Long-Term Standard Results with a Monofocal Intraocular Lens

Optimising curve fitting techniques to look for standardisation of the analysis of defocus curves derived from multifocal intraocular lenses

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