Macular Thickness, a New Variable to Take Into Account in Intraocular Lens Calculation?

doi:10.21203/rs.3.rs-1753362/v1

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Macular Thickness, a New Variable to Take Into Account in Intraocular Lens Calculation?

https://doi.org/10.21203/rs.3.rs-1753362/v1

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Purpose Evaluate the influence of a posterior pole variable, macular thickness, in the calculation of the intraocular lens to be implanted in cataract patients since optical biometry has been used. Therefore, the aim of this work is to determine the importance of different biometric, topographic and retinal variables in the calculation of the intraocular lens to be implanted and to propose a new biometric formula that includes these new variables. Methods A retrospective cross-sectional observational study was carried out using a database of 230 patients who underwent cataract surgery at the Oftalvist centre in Almeria between January 2021-December 2021. Correlations between implanted IOL power and biometric, topography and retina variables were explored. A linear model was proposed with IOL power as dependent variable. The method was forward-backward steps. Results The model achieves an adjusted R-squared of 0.96 indicating that the power of the implanted lens is 96% dependent on the value of the axial length, the mean of the preoperative Ks (the mean has been performed because of the excessive collinearity between the two Ks) and the preoperative macular thickness. The introduction of the preoperative macular thickness improves the R-squared of the model and is significant in the calculation of the coefficients. Conclusions Macular thickness is a variable to take into account as it improves our prediction model of the IOL to be implanted. Keywords: Biometry, macular thickness, cataract surgery, intraocular lens power, calculation formula, precision, refractive accuracy.

Nowadays, cataract surgery is not just a simple procedure to remove the opacity of the crystalline lens, but has become an operation in which the final refractive result is very important. In some cases, it has even become a refractive surgery to offer the patient greater and greater independence from his or her glasses.

This implies the need for increasingly efficient and accurate calculations. To this end, calculation formulae have been evolving and adding more and more variables to their structures. Initially, first-generation formulas, such as Fyodorov (1) or Binkhorst, considered that the effective lens position (ELP) was fixed and independent of the anatomical characteristics of the eye, and only varied according to the characteristics of the crystalline lens, which led to a lack of predictability of the position and refractive results. The second generation formulas, such as SRK or SRKII (2) introduced the variable axial length (AL), which substantially improved ELP prediction. Third generation formulas such as Hoffer Q (3) Holladay 1 (4) and SRK/T (5) added keratometry. The fourth generation formulae started to add multiple variables in their formulae: Haigis (6) incorporated the anterior chamber depth measured from epithelium to lens (ACD) preoperatively, Holladay II (7) and Barrett Universal II (8) added lens thickness (LT) and horizontal corneal diameter (WTW) (9) introduced corneal centre thickness (CCT) and patient sex, Olsen (10) used a C constant calculated with the preoperative ACD, and Panacea (11) included the corneal sphericity value and the ratio between corneal anterior and posterior curvature.

Most of these variables are obtained from the anterior pole of the eye, but we have not found, to date in our literature search, any study that evaluates the influence of a posterior pole variable, macular thickness, in the calculation of the intraocular lens to be implanted in cataract patients since optical biometry has been used. Therefore, the aim of this work is to determine the importance of different biometric, topographic and retinal variables in the calculation of the intraocular lens to be implanted and to propose a new biometric formula that includes these new variables.

Study Design and Patient Selection

A retrospective cross-sectional observational study was carried out using a database of 230 patients who underwent cataract surgery at the Oftalvist centre in Almeria between January 2021-December 2021.

The sample size was calculated with the Granmo calculator accepting an alpha risk of 0.05 and a beta risk of 0.2 in a bilateral contrast, 117 subjects are required to detect a difference equal to or greater than 0.15 units. The common standard deviation is assumed to be 0.5 and a correlation coefficient between the initial and final measurement of 0.9. A loss to follow-up rate of 10% was estimated.

Patient Information

The study complied with the declaration of Helsinki and obtained permission from the ethics committee of the Department of Nursing, Physiotherapy and Medicine with code EFM 179/2022. All patients signed an informed consent for the scientific use of their anonymised clinical data that could be revoked.

Inclusion Criteria

Inclusion criteria were: patients without previous refractive surgical procedures such as laser-assisted in situ keratomileusis (LASIK), photorefractive keratotomy (PRK) or incisional surgery, with previous corneal diseases such as keratoconus or corneal scarring, with a history of previous intraocular surgery and with retinal abnormalities. Uncomplicated surgery. Only one eye was selected from each patient.

Variables collected

The variables collected were: sex, age. Preoperative and postoperative corrected and uncorrected (decimal) visual acuity obtained with the Topcon® IS-600 refractive column.

Postoperative residual refraction, preoperative and postoperative intraocular pressure (IOP) with the Topcon® TRK-2P autorefractometer. Central corneal pachymetry, anterior to posterior corneal ratio, anterior chamber depth (ACD), white to white (WTW), spherical aberration, astigmatic aberration and coma aberration with PENTACAM®.

Axial length, preoperative anterior keratometry (K1 and K2), implanted lens power, refractive residuals of each formula and lens power calculated according to each biometric formula in emmetropia, obtained with IOL MASTER 500® for the SRK/T, Hoffer Q, Haigis and Holladay 1 formulas.

Macular thickness with CIRRUS HD-OCT 500®.

The implanted lens power, refractive residual and emmetropic lens power for the KANE formula were obtained using the online calculator.

Statistical Analysis

Patient demographic and biometric data were described as frequencies (percentages) and means (SD). Normality of the data was assessed using the Kolmogorov-Smirnov test. Parametric tests such as Pearson correlations and Anova and non-parametric tests such as Spearman correlations and Kruskal-Wallis tests were applied, as appropriate, to compare demographic and biometric data. A significance level of α = 0.05 was considered. Statistical analysis was performed with SPSS software for Windows (V.27.0, SPSS) and R software version 3.5.1.

Table 1 evaluated the descriptive characteristics of the biometric variables. The axial length mean was 23.49 mm.

Table 1: Descriptive Statistics Biometric, topography and Retina variables.

AL: Axial Length, ACD: Anterior Chamber Depth, WTW: White to White distance.

The preoperative and one-months postoperative macular thickness were collected. There was a significant difference between both macular thickness

Correlations between implanted IOL power and biometric, topography and retina variables were explored. There was statistical significance among IOL power and AL, ACD, WTW and A constant biometry variables (Table 2).

Table 2: Correlations between IOL power and biometric, topography and retina variables.

No statistical significance was found between IOL power and topography variables (ant-pot relation, spherical, Astigmatism and coma aberration) (Table 2).

A linear model was proposed with IOL power as dependent variable. The method was forward-backward steps. The adjusted R square was 0,96 that meant that the model explained 96 % of the value of the IOL power (Table 3). The final predictor variables were: AL, K mean (preoperative K1 and K2 mean) and preoperative macular thickness (Table 4).

Table 3: Model Summary^b

Table 4: Coefficients^aof the models.

Figure 1: Model Predictors effect.

The conditions of a linear model were calculated:

Heteroscedasticity Breusch-Pagan test was non statistical significant.

The predictor effects were evaluated in Figure 1.

Table 5: Correlations between the new biometry formula and the most frequently formulas

The main correlation between the new formula was with SRKT and Holladay I (Table 5).

The constant incorporation of variables into the intraocular lens calculation formulae has been improving the calculation of the IOL to be implanted for years. The introduction of LA and keratometry were fundamental, but not sufficient. Little by little, variables have been added which, although not essential for the calculations, help to improve the efficiency of the formulas they generate.

Improving the accuracy of IOL calculations is becoming increasingly important as more and more trifocal and extended depth of focus (Edof) type lenses are implanted, which require much greater precision of the refractive outcome to maximise visual efficiency and not be penalised by unexpected refractive outcomes.

In the study by Ribeiro et al. (12) they evaluated the changes of each optical parameter on the refractive state of the eye and found that a 0.25 mm change in ACD measurement corresponds to an error of 0.10 D in an eye with an NA of 30.0 mm and 0.50 D in an eye with an AL of 20.0 mm. Following these results, studies were carried out assessing various ranges of ACD values to evaluate which formula was more effective. (13, 14).

However, formulas such as SRK/T (5) or Hoffer Q (3) which are still very frequently used today, did not include ACD in their variables.

Thickening of the lens occurs throughout life predominantly towards the anterior chamber (15). In an older population, as is often the case in the cataract population, the lens is thickened and therefore the depth of the ACD is shallower. Ribeiro et al. (12) found that for every 1% increase in lens thickness, the refractive error changes by 0.097, a 0.104 mm change in LT means a 0.25 D change in refractive error. Studies have already been published relating the different ocular parameters, including LT and the prediction error of the different formulas (16, 17).

However, there are no studies that support the incorporation of macular thickness as a variable to be taken into account in the calculation of the power of the lens to be implanted. One of the problems with ultrasonic biometry was that it measured the LA up to the inner limiting membrane, but the photoreceptor layer is in a more posterior area, close to Bruch's membrane (18, 19). Because of this, IOL power calculation formulae dating back to the 1970s added an average of 200 µm to the measured axial length to compensate for the difference.

The disparity was great, as the Binkhorst formula used a correction value of 0.25 mm, the Hoffer formula used a value of 0 and the Holladay formula used a value of 0.20 mm. However, the authors of the SRK/T formula decided that the retinal thickness factor was an interdependent variable of AL, with greater accuracy and prediction being observed using low values in long eyes and high values in short eyes, the relationship being determined by the following formula: retinal thickness = 0.65696-0.02029xAL (5). Nowadays, macular thickness is estimated at 250 microns as normal, but its great variability with respect to sex, age and different populations in healthy eyes has been studied (20). The introduction of optical biometers using the principle of partial coherence laser interferometry has improved the measurement of AL (21) but since its introduction into daily clinical practice, the influence of retinal thickness, in particular macular thickness, in improving the efficacy of biometric formulae has not been re-evaluated.

In our study, the significant predictor variables were axial length, mean preoperative K1 and K2 and preoperative macular thickness.

Unlike other authors, the anterior chamber depth did not improve the model and showed a high collinearity factor (VIF Variance Inflation Factor) with the LA. This is logical as the anterior chamber depth is part of the total axial length. The model achieves an adjusted R-squared of 0.96 indicating that the power of the implanted lens is 96% dependent on the value of the axial length, the mean of the preoperative Ks (the mean has been performed because of the excessive collinearity between the two Ks) and the preoperative macular thickness. The introduction of the preoperative macular thickness improves the R-squared of the model and is significant in the calculation of the coefficients. Table 5 shows the correlations between the most commonly used biometric formulae.

Macular thickness is a variable to take into account as it improves our prediction model of the IOL to be implanted.

One of the limitations of our work is that we have used a single biometer, IOL Master 500®, which includes the keratometry measurement on the anterior face, so the results cannot be extrapolated to other biometers or other calculations that are performed with keratometry. total. The patients in our database are quite heterogeneous, so in the future it will be necessary to expand the sample with patients with parameters outside the norm.

Future studies will seek to incorporate the new proposed formula into clinical practice to validate the results of the new biometric calculation with a new database.

Statements & Declarations Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

All authors contributed to the study's conception and design. NSL, APR, HAA and GCL performed material preparation, data collection, and analysis. NSL wrote the first draft of the manuscript and all authors commented on previous versions. All authors read and approved the final manuscript.

Ethics approval

This study was performed in line with the principles of the Declaration of Helsinki. Therefore, the Ethics Committee of the University of Almería Code was approved: EFM 179/2022. However, this is an observational study. Therefore, the Department of Nursing, Physiotherapy, and Medicine of Almeria University Research Ethics Committee has confirmed that no ethical approval is required.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Consent to publish

The authors affirm that human research participants provided informed consent for the publication of the anonymized data.

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Tables 1 to 5 are available in the Supplementary Files section

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Table1DescriptiveStatisticsBiometricvariables..xlsx
Table 1: Descriptive Statistics Biometric, topography and Retina variables.Table 1
Table2Table2CorrelationsbetweenIOLpowerandbiometrictopographyandretinavariables.pdf
Table 2: Correlations between IOL power and biometric, topography and retina variables.
Table3ModelSummaryb.xlsx
Table 3: Model Summary^bTable 3
Table4Coefficientsaofthemodels.pdf
Table 4: Coefficients^aof the models.
Table5Correlationsbetweenthenewbiometryformulaandthemostfrequentlyformulas.pdf
Table 5: Correlations between the new biometry formula and the most frequently formulas

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Macular Thickness, a New Variable to Take Into Account in Intraocular Lens Calculation?

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Abstract

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Introduction

Materials And Methods

Study Design and Patient Selection

Patient Information

Inclusion Criteria

Variables collected

Statistical Analysis

Results

Discussion

Conclusion

Limitation

Declarations

References

Tables

Additional Declarations

Supplementary Files

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