Regional comparison of preoperative biometry for cataract surgery between two domestic institutions

Predictability of combined cataract surgery and trabeculectomy using Barrett Universal Ⅱ formula

PURPOSE

To compare the predictability of intraocular lens (IOL) power calculation using the Barrett Universal II and the SRK/T formulas in eyes undergoing combined cataract surgery and trabeculectomy.

METHODS

We retrospectively reviewed the clinical charts of 56 consecutive eyes undergoing cataract surgery and trabeculectomy. IOL power calculations were performed using the Barrett Universal II and SRK/T formulas. We compared the prediction error, the absolute error, and the percentages within ± 0.5 D and ±1.0 D of the targeted refraction, 3 months postoperatively, and also investigated the relationship of the prediction error with the keratometric readings and axial length, using the two formulas.

RESULTS

The prediction error using the SRK/T formula was significantly more myopic than that using the Barrett Universal II formula (paired t-test, p<0.001). The absolute error using the Barrett Universal II formula was significantly smaller than that using the SRK/T formula (p = 0.039). We found significant correlations of the prediction error with the axial length (Pearson correlation coefficient, r = 0.273, p = 0.042), and the keratometric readings (r = -0.317, p = 0.017), using SRK/T formula, but no significant correlations between them (r = 0.219, p = 0.167, and r = -0.023, p = 0.870), using the Barrett Universal II formula.

CONCLUSIONS

The Barrett Universal II formula provides a better predictability of IOL power calculation and is less susceptible to the effect of the axial length and the corneal shape, than the SRK/T formula. The Barrett Universal formula, rather than the SRK/T formula, may be clinically helpful for improving the refractive accuracy in such eyes.

Machine learning adaptation of intraocular lens power calculation for a patient group

Background

To examine the effectiveness of the use of machine learning for adapting an intraocular lens (IOL) power calculation for a patient group.

Methods

In this retrospective study, the clinical records of 1,611 eyes of 1,169 Japanese patients who received a single model of monofocal IOL (SN60WF, Alcon) at Miyata Eye Hospital were reviewed and analyzed. Using biometric metrics and postoperative refractions of 1211 eyes of 769 patients, constants of the SRK/T and Haigis formulas were optimized. The SRK/T formula was adapted using a support vector regressor. Prediction errors in the use of adapted formulas as well as the SRK/T, Haigis, Hill-RBF and Barrett Universal II formulas were evaluated with data from 395 eyes of 395 distinct patients. Mean prediction errors, median absolute errors, and percentages of eyes within ± 0.25 D, ± 0.50 D, and ± 1.00 D, and over + 0.50 D of errors were compared among formulas.

Results

The mean prediction errors in the use of the SRT/K and adapted formulas were smaller than the use of other formulas (P < 0.001). In the absolute errors, the Hill-RBF and adapted methods were better than others. The performance of the Barrett Universal II was not better than the others for the patient group. There were the least eyes with hyperopic refractive errors (16.5%) in the use of the adapted formula.

Conclusions

Adapting IOL power calculations using machine learning technology with data from a particular patient group was effective and promising.

Refractive Prediction Error in Cataract Surgery Using an Optical Biometer Equipped with Anterior-Segment Optical Coherence Tomography

PURPOSE

To evaluate refractive error after cataract surgery using an optical biometer equipped with anterior-segment optical coherence tomography (AS-OCT).

SETTING

Chukyo Eye Clinic, Nagoya, Japan.

DESIGN

Retrospective observational design.

METHODS

In total, 150 patients with cataract (150 eyes, mean age 73.4 ± 8.2 years, men 76, women 74), who underwent measurement of parameters with the anterior-segment OCT scanners ANTERIONTM (AS-OCTB) and IOL Master 700 (OCTB) before cataract surgery, were enrolled in the study. Refractive prediction error was compared between the two devices using the SRK/T, Haigis, and Barrett UII formulas for IOL power calculation.

RESULTS

There were significant differences between AS-OCTB and OCTB in axial length, mean corneal refractive power, anterior chamber depth, lens thickness, and corneal diameter. In the SRK/T formula, the arithmetic means of refractive prediction errors for AS-OCTB and OCTB were -0.06 ± 0.46 D and 0.02 ± 0.42 D, respectively. In the Haigis formula, the arithmetic means of refractive prediction errors for AS-OCTB and OCTB were -0.23 ± 0.40 D and -0.08 ± 0.35 D, respectively. In the Barrett UII formula, the arithmetic means of refractive prediction errors for AS-OCTB and OCTB were -0.02 ± 0.38 D and 0.11 ± 0.36 D, respectively. AS-OCTB showed significantly larger refractive prediction error toward myopia than OCTB in all three formulas (P <0.0001).

CONCLUSION

The refractive prediction error using AS-OCTB showed a small difference from that using OCTB. While clinically comparable, the two methods could drive meaningful differences in IOL selection.

Nationwide multicentre comparison of preoperative biometry and predictability of cataract surgery in Japan

AIM

To compare the preoperative biometric data and the refractive accuracy of cataract surgery among major surgical sites in a nationwide multicentre study.

METHODS

We prospectively obtained the preoperative biometric data of 2143 eyes of 2143 consecutive patients undergoing standard cataract surgery at major 12 facilities and compared the preoperative biometry as well as the postoperative refractive accuracy among them.

RESULTS

We found significant differences in most preoperative variables, such as axial length (one-way analysis of variance, p=0.003), anterior chamber depth (p<0.001), lens thickness (p<0.001) and central corneal thickness (p<0.001), except for mean keratometry (p=0.587) and corneal astigmatism (p=0.304), among the 12 surgical sites. The prediction error using the Sanders-Retzlaff-Kraff/Theoretical (SRK/T formula was significantly more hyperopic than that using the Barrett Universal II formula (paired t-test, p<0.001). The absolute error using the SRK/T formula was significantly larger than that using the Barrett Universal II formula (p=0.016). The prediction error using the SRK/T formula was significantly more hyperopic than that using the Barrett Universal II formula at 10 of 12 institutions, but significantly more myopic at one institution. The absolute error using the SRK/T formula was significantly larger than that using the Barrett Universal II formula at 4 of 12 institutions but significantly smaller at two institutions.

CONCLUSIONS

Regional divergences of the preoperative biometry were not necessarily negligible, and the optimised intraocular lens power calculation formula was individually different among the 12 facilities. Our findings highlight the importance of individual optimisation of these formulas at each facility, especially in consideration of these biometric variations.Trial registration numberClinical Trial Registry; 000039976.

Use of a Machine Learning Method in Predicting Refraction after Cataract Surgery

The present study aims to describe the use of machine learning (ML) in predicting the occurrence of postoperative refraction after cataract surgery and compares the accuracy of this method to conventional intraocular lens (IOL) power calculation formulas. In total, 3331 eyes from 2010 patients were assessed. The objects were divided into training data and test data. The constants for the IOL power calculation formulas and model training for ML were optimized using training data. Then, the occurrence of postoperative refraction was predicted using conventional formulas, or ML models were calculated using the test data. We evaluated the SRK/T formula, Haigis formula, Holladay 1 formula, Hoffer Q formula, and Barrett Universal II formula (BU-II); similar to ML methods, we assessed support vector regression (SVR), random forest regression (RFR), gradient boosting regression (GBR), and neural network (NN). Among the conventional formulas, BU-II had the lowest mean and median absolute error of prediction. Therefore, we compared the accuracy of our method with that of BU-II. The absolute errors of some ML methods were lower than those of BU-II. However, no statistically significant difference was observed. Thus, the accuracy of our method was not inferior to that of BU-II.