Pursuing perfection in intraocular lens calculations: III. Criteria for analyzing outcomes

Wang, Li; Koch, Douglas D.; Hill, Warren; Abulafia, Adi

doi:10.1016/j.jcrs.2017.08.003

Cited by 175 publications

(161 citation statements)

References 5 publications

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“…We followed the recently published protocols comparing their respective accuracies. [20,21] Overall, the refractive outcomes and percentages of eyes with prediction errors within ±0.25 D, ±0.50 D, ±0.75 D, and ±1.00 D for each formula were similar to those in the recent study by Melles et al using a Lenstar 900 optical biometer. [16] All five formulas achieved above 92% of eyes within ±1.00 D of the predicted refraction, much higher than the 85% suggested by Gale et al [25] Recent studies reported that the Barrett Universal II formula was more accurate and showed the better refractive outcomes than the other formulas.…”

Section: Discussionsupporting

confidence: 81%

“…Our selection criteria for the study subjects and methods followed the recommendations of recent studies regarding the protocols for studies of IOL formula accuracy. [20,21] The exclusion criteria were incomplete biometry, corneal astigmatism more than 2.0 diopters (D), LT measurement less than 2.50 mm, complicated cataract surgery (posterior capsular rupture), additional procedures during cataract surgery (combined vitrectomy or glaucoma surgery), postoperative corrected distance visual acuity (CDVA) worse than 20/40, refraction performed before 4 weeks postoperatively, postoperative complications, and incomplete documentation. Patients with a history of corneal disease or refractive surgery and phacomorphic glaucoma were excluded.…”

Section: Methodsmentioning

confidence: 99%

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Accuracy of intraocular lens power calculation formulas using a swept-source optical biometer

et al. 2020

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PurposeTo compare the accuracy of the five commonly used intraocular lens (IOL) calculation formulas integrated to a swept-source optical biometer, the IOLMaster 700, and evaluate the extent of bias within each formula for different ocular biometric measurements. MethodsThe study included patients undergoing cataract surgery with a ZCB00 IOL implant, using IOLMaster 700 optical biometry. A single eye per patient was included in the final analysis for a total of 324 cases. The SRK/T, Hoffer Q, Haigis, Holladay 2, and Barrett Universal II formulas were evaluated. The correlations between the refractive prediction errors calculated using the five formulas and ocular dimensions such as axial length (AL), anterior chamber depth (ACD), corneal power, and lens thickness (LT) were analyzed. ResultsThere were significant differences in the median absolute error predicted by the five formulas after the adjustment for mean refractive prediction errors to zero (P = 0.038). The Barrett Universal II formula had the lowest median absolute error (0.263) and resulted in a higher percentage of eyes with prediction errors within ±0.50 D, ±0.75 D, and ±1.00 D (all P < 0.050). The refractive errors predicted by only the Barrett formula showed no significant correlation with the ocular dimensions: AL, ACD, corneal power, and LT. ConclusionsOverall, the Barrett Universal II formula, integrated to a swept-source optical biometer had the lowest prediction error and appeared to have the least bias for different ocular biometric measurements for the ZCB00 IOL.

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Section: Discussionsupporting

confidence: 81%

Section: Methodsmentioning

confidence: 99%

Accuracy of intraocular lens power calculation formulas using a swept-source optical biometer

et al. 2020

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“…For some formulas, a ME of zero could not be obtained due to limitations in how many decimal places could be entered for the constant into the calculator. In these cases, the small residual mean error was removed by adjusting the refractive prediction error for each eye by an amount equal to the ME in that group as described in the JCRS editorial by Wang et al 14…”

Section: Methodsmentioning

confidence: 99%

Comparison of the Kane formula with existing formulas for intraocular lens power selection

Connell¹,

2019

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ObjectiveTo compare the accuracy of a new intraocular lens (IOL) power formula (Kane formula) with existing formulas using IOLMaster, predominantly model 3, biometry (measures variables axial length, keratometry and anterior chamber depth) and optimised lens constants. To compare the accuracy of three new or updated IOL power formulas (Kane, Hill-RBF V.2.0 and Holladay 2 with new axial length adjustment) compared with existing formulas (Olsen, Barrett Universal 2, Haigis, Holladay 1, Hoffer Q, SRK/T).Methods and analysisA single surgeon retrospective case review was performed from patients having uneventful cataract surgery with Acrysof IQ SN60WF IOL implantation over 11 years in a Melbourne private practice. Using optimised lens constants, the predicted refractive outcome for each formula was calculated for each patient. This was compared with the actual refractive outcome to give the prediction error. Eyes were separated into subgroups based on axial length as follows: short (≤22.0 mm), medium (>22.0 to <26.0 mm) and long (≥26.0 mm).ResultsThe study included 846 patients. Over the entire axial length range, the Kane formula had the lowest mean absolute prediction error (p<0.001, all formulas). The mean postoperative difference from intended outcome for the Kane formula was −0.14+0.27×1 (95% LCL −1.52+0.93×43; 95% UCL +0.54+1.03×149). The formula demonstrated the lowest absolute error in the medium axial length range (p<0.001). In the short and long axial length groups, no formula demonstrated a significantly lower absolute mean prediction error.ConclusionUsing three variables (AL, K, ACD), the Kane formula was a more accurate predictor of actual postoperative refraction than the other formulae under investigation. There were not enough eyes of short or long axial length to adequately power statistical comparisons within axial length subgroups.

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“…The results predicted by each model were compared against the achieved Clinical results (CR), and the both models were compared each to other. In the results evaluation and statistical analysis, we followed the recommendations described in the work of Wang (Wang et al, 2017a). Manuscript to be reviewed Since AL is referred as the most important in predicting IOL power (Mahdavi & Holladay, 2011), the evaluation process is usually divided into subgroups based on AL (Wang et al, 2017a).…”

Section: Evaluation Methodology and Statistical Testsmentioning

confidence: 99%

“…-Optical power of IOL Implanted -Measured residual refraction Rx post -Interrelationship of Rx post and IOL Implanted It is generally known that 1.0 D of IOL prediction error produces approximately 0.7 D of refractive prediction error at the spectacle plane (Wang et al, 2017a). However, this is a general assumption and since eye is a complex optical system it may not reach sufficient accuracy in all eyes.…”

Section: Data Mining and Optimizationmentioning

confidence: 99%

Peer Review #1 of "Improving clinical refractive results of cataract surgery by machine learning (v0.1)"

2019

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To evaluate potential of Support Vector Machine Regression model (SVM-RM) and Multilayer Neural Network Ensemble model (MLNN-EM) as an intraocular lens (IOL) power calculation improvement for clinical workflow. Background. Current IOL power calculation methods offer limited accuracy and especially in eyes with an unusual ocular dimension the accuracy may decrease. In case of an improperly calculated power of the IOL in cataract or refractive lens replacement surgery, there is a risk of re-operation or further refractive correction. This may potentially induce complications and discomfort to the patient. Methods. A dataset containing information about 2194 eyes was obtained using data mining process from Electronic Health Record (EHR) system database of Gemini Eye Clinic. The dataset was optimized and split into Selection set (used in design of models and training), and Verification set (used in the evaluation). Set of mean prediction errors and distribution of predicted refractive error were evaluated for both models and Clinical results. Results. Both models performed significantly better for the most evaluated parameters compared to the clinical results. There was no significant difference between both evaluated models. In the ± 0.50 D prediction error category both SVM-RM and MLNN-EM were slightly better than Barrett Universal II formula (which is often presented as the most accurate calculation formula). Conclusion. In comparison to the method currently used in a clinical setting, both SVM-RM and MLNN-EM has achieved significantly better results in IOL calculations and therefore there is a strong potential to improve clinical cataract refractive outcomes.

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Pursuing perfection in intraocular lens calculations: III. Criteria for analyzing outcomes

Cited by 175 publications

References 5 publications

Accuracy of intraocular lens power calculation formulas using a swept-source optical biometer

Accuracy of intraocular lens power calculation formulas using a swept-source optical biometer

Comparison of the Kane formula with existing formulas for intraocular lens power selection

Peer Review #1 of "Improving clinical refractive results of cataract surgery by machine learning (v0.1)"

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