scholarly journals Lenstar LS 900 versus Pentacam-AXL: analysis of refractive outcomes and predicted refraction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Henrique Aragão Arruda ◽  
Joana M. Pereira ◽  
Arminda Neves ◽  
Maria João Vieira ◽  
Joana Martins ◽  
...  
Keyword(s):  
Refractive Error ◽  
Prediction Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Spherical Equivalent ◽  
Refractive Outcomes ◽  
Iol Calculation ◽  
Median Absolute Error ◽  
The Difference ◽  
Mean Prediction Error

AbstractAnalysis of refractive outcomes, using biometry data collected with a new biometer (Pentacam-AXL, OCULUS, Germany) and a reference biometer (Lenstar LS 900, HAAG-STREIT AG, Switzerland), in order to assess differences in the predicted and actual refraction using different formulas. Prospective, institutional study, in which intraocular lens (IOL) calculation was performed using the Haigis, SRK/T and Hoffer Q formulas with the two systems in patients undergoing cataract surgery between November 2016 and August 2017. Four to 6 weeks after surgery, the spherical equivalent (SE) was derived from objective refraction. Mean prediction error (PE), mean absolute error (MAE) and the median absolute error (MedAE) were calculated. The percentage of eyes within ± 0.25, ± 0.50, ± 1.00, and ± 2.00 D of MAE was determined. 104 eyes from 76 patients, 35 males (46.1%), underwent uneventful phacoemulsification with IOL implantation. Mean SE after surgery was − 0.29 ± 0.46 D. Mean prediction error (PE) using the SRK/T, Haigis and Hoffer Q formulas with the Lenstar was significantly different (p > 0.0001) from PE calculated with the Pentacam in all three formulas. Percentage of eyes within ± 0.25 D MAE were larger with the Lenstar device, using all three formulas. The difference between the actual refractive error and the predicted refractive error is consistently lower when using Lenstar. The Pentacam-AXL user should be alert to the critical necessity of constant optimization in order to obtain optimal refractive results.

scholarly journals Comparison of Two Toric IOL Calculation Methods

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
C. Kern ◽  
K. Kortüm ◽  
M. Müller ◽  
A. Kampik ◽  
S. Priglinger ◽  
...  
Keyword(s):  
Intraocular Lens ◽  
Absolute Error ◽  
Spherical Equivalent ◽  
Calculation Methods ◽  
Refractive Outcome ◽  
Toric Intraocular Lens ◽  
Iol Calculation ◽  
Median Absolute Error ◽  
Lens Power ◽  
Posterior Corneal Astigmatism

Purpose. To compare two calculators for toric intraocular lens (IOL) calculation and to evaluate the prediction of refractive outcome. Methods. Sixty-four eyes of forty-five patients underwent cataract surgery followed by implantation of a toric intraocular lens (Zeiss Torbi 709 M) calculated by a standard industry calculator using front keratometry values. Prediction error, median absolute error, and refractive astigmatism error were evaluated for the standard calculator. The predicted postoperative refraction and toric lens power values were evaluated and compared after postoperative recalculation using the Barrett calculator. Results. We observed a significant undercorrection in the spherical equivalent (0.19 D) by using a standard calculator (p≤0.05). According to the Baylor nomogram and the refractive influence of posterior corneal astigmatism (PCA), undercorrection of the cylinder was lower for patients with WTR astigmatism, because of the tendency of overcorrection. An advantage of less residual postoperative SE, sphere, and cylinder for the Barrett calculator was observed when retrospectively comparing the calculated predicted postoperative refraction between calculators (p≤0.01). Conclusion. Consideration of only corneal front keratometric values for toric lens calculation may lead to postoperative undercorrection of astigmatism. The prediction of postoperative refractive outcome can be improved by using appropriate methods of adjustment in order to take PCA into account.

Corneal topographic ring vs zone in trifocal IOL calculation

2021 ◽  
pp. 184-186
Author(s):  
A.D. Loginova ◽  
◽  
S.V. Shukhaev ◽  
S.S. Kudlakhmedov ◽  
E.V. Boiko ◽  
...  
Keyword(s):  
Power Distribution ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Accurate Calculation ◽  
Iol Implantation ◽  
Topographic Data ◽  
Trifocal Iol ◽  
Iol Calculation ◽  
Km Value ◽  
Median Absolute Error

Purpose. To compare the results of trifocal IOL calculation using various corneal topographic data (ring and zone). Methods. This retrospective study involved 35 patients (40 eyes), underwent cataract surgery (FLACS) with trifocal IOL implantation (AcrySof IQ PanOptix). The calculation was performed using IOL-Master 500 according to 4 formulas (Haigis, HofferQ, Holladay 1, SRK / T) and Tomey OA-2000 according to 2 formulas (Barrett II Universal, Olsen). Topographic values included Km collected from Pentacam HR Power Distribution Apex map with diameter of 3.0 and 5.0 mm on a ring and zone. Predicted and actual refraction were compared after surgery. Results. Mean Km value on 3 mm zone and ring was: 42.75±1,46 D and 42.91±1.43 D, respectively (p<0.0001). Mean Km value on 5 mm zone and ring was: 43.09±1.5 D and 43.55±1.48 D, respectively (p<0.0001). According to 6 formulas Mean Absolute Error (MAE) calculated using 3 mm zone data was significantly less then on 3mm ring: 0.3± 0.28; 0.48±0.3 and Median Absolute Error (MedAE) was 0.225 (0.3); 0.465 (0.397) respectively (p<0.01). The same data were obtained on 5mm zone and ring: MAE was 0.29±0.28; 0.35±0.29 and MedAE amounted to 0.225 (0.3); 0.29 (0.38) respectively (p=0.02). Conclusion. Mean Km value on Power Distribution Apex map according to ring is significantly greater then according to zone. 1) Predicted refraction using corneal topographic ring data deviates towards hyperopia relative to the actual postoperative refraction. 2) The use of topographic data on zone allows to obtain more accurate calculation of trifocal IOL than when using the data on the ring. Key words: IOL calculation, Trifocal IOL, corneal topography.

scholarly journals Refractive outcomes following cataract surgery in patients who have had myopic laser vision correction

2019 ◽  
Vol 4 (1) ◽  
pp. e000242 ◽  
Author(s):  
Chung Shen Chean ◽  
Boon Kang Aw Yong ◽  
Samuel Comely ◽  
Deena Maleedy ◽  
Stephen Kaye ◽  
...  
Keyword(s):  
Cataract Surgery ◽  
Refractive Error ◽  
Control Group ◽  
Prediction Errors ◽  
Spherical Equivalent ◽  
Refractive Outcomes ◽  
Vision Correction ◽  
Laser Vision ◽  
The Mean ◽  
The Difference

ObjectivePrediction errors are increased among patients presenting for cataract surgery post laser vision correction (LVC) as biometric relationships are altered. We investigated the prediction errors of five formulae among these patients.Methods and analysisThe intended refractive error was calculated as a sphero-cylinder and as a spherical equivalent for analysis. For determining the difference between the intended and postoperative refractive error, data were transformed into components of Long's formalism, before changing into sphero-cylinder notation. These differences in refractive errors were compared between the five formulae and to that of a control group using a Kruskal-Wallis test. An F-test was used to compare the variances of the difference distributions.Results22 eyes post LVC and 19 control eyes were included for analysis. Comparing both groups, there were significant differences in the postoperative refractive error (p=0.038). The differences between the intended and postoperative refractive error were greater in post LVC eyes than control eyes (p=0.012), irrespective of the calculation method for the intended refractive error (p<0.01). The mean difference between the intended and postoperative refractive error was relatively small, but its variance was significantly greater among post LVC eyes than control eyes (p<0.01). Among post LVC eyes, there were no significant differences between the mean intended target refraction and between the intended and postoperative refractive error using five biometry formulae (p=0.76).ConclusionBiometry calculations were less precise for patients who had LVC than patients without LVC. No particular biometry formula appears to be superior among patients post LVC.

scholarly journals Intraocular lens power calculations in eyes with pseudoexfoliation syndrome

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aleksandra Wlaź ◽  
Agnieszka Kustra ◽  
Agnieszka Rozegnał-Madej ◽  
Tomasz Żarnowski
Keyword(s):  
Cataract Surgery ◽  
Intraocular Lens ◽  
Prediction Error ◽  
Absolute Error ◽  
Pseudoexfoliation Syndrome ◽  
Control Group ◽  
Refractive Outcomes ◽  
Iol Calculation ◽  
Lens Power ◽  
And Control

AbstractTo compare refractive outcomes after cataract surgery in pseudoexfoliation syndrome (PEX) and control eyes and to investigate the accuracy of 3 intraocular lens (IOL) calculation formulas in these eyes. In this prospective comparative study 42 eyes (PEX group) and 38 eyes (control group) of 80 patients were included. The follow-up was 3 months. The refractive prediction error (RPE), mean absolute error (MAE), median absolute error (MedAE) and the percentages of eyes within ± 0.25 D, ± 0.5 D, ± 1.0 D and ± 2.0 D of prediction error were calculated. Three IOL calculation formulas (SRK/T, Barrett Universal II and Hill-RBF) were evaluated. PEX produced statistically significantly higher mean absolute errors and lower percentages of eyes within ± 0.5 D than control eyes in all investigated IOL calculation formulas. There were no statistically significant differences in the median absolute errors between the 3 formulas in either PEX or control eyes. Refractive outcomes after cataract surgery are statistically significantly worse in PEX than in control eyes. All three IOL calculation formulas produced similar results in both PEX and control eyes.Trial registration: ClinicalTrials.gov registration number NCT04783909.

scholarly journals Optimizing the intraocular lens formula constant according to intraocular lens diameter

2021 ◽  
Vol 14 (5) ◽  
pp. 700-703
Author(s):  
Abdul R El-Khayat ◽  
Keyword(s):  
Intraocular Lens ◽  
Refractive Error ◽  
District General Hospital ◽  
Spherical Equivalent ◽  
Inclusion Criteria ◽  
Refractive Outcomes ◽  
Lens Diameter ◽  
The Difference ◽  
The Uk ◽  
Different Diameters

AIM: To determine whether the different diameters of a specific intraocular lens (IOL) have significantly different optimized SRK/T A constants and whether these new A constants can improve refractive outcomes. METHODS: Data were collected prospectively from Jan. 2011 to Dec. 2012 on all patients undergoing routine cataract surgery at a district general hospital in the UK. Patients were divided into three groups according to the size of the Akreos AO MI60 IOL used. A constants for the SRK/T formula were optimized according to the size of the IOL. These optimized A constants were then used to select future refractive outcomes. RESULTS: A total of 2398 cataract operations were performed during the study period of which 1131 met the inclusion criteria. The three optimized A constants for the different sized IOLs were 118.98, 119.13, 119.32. The difference between them was highly significant (P≤0.0001). Two optimized A constants for three sizes of IOL led to an improvement in refractive outcomes (from 93.4% to 94.6% of refractive outcomes within 1.00 D of predicted spherical equivalent). The optimized A constant for the largest IOL was based on a small number of cases and was not used. CONCLUSION: Optimizing the A constant for the three distinct sizes of the Bausch &#x0026; Lomb Akreos MI60 lens lead to three significantly different A constants. In our practice, using two different optimized A constants for three different sized IOLs give the least refractive error, however, using three optimized A constants may give better results with a larger dataset.

scholarly journals Refractive outcomes after vitrectomy combined with phacoemulsification of idiopathic macular holes

2021 ◽  
Vol 14 (2) ◽  
pp. 250-254
Author(s):  
Bo-Shi Liu ◽  
◽  
Rui Niu ◽  
Qiong Chen ◽  
Ze-Tong Nie ◽  
...  
Keyword(s):  
Refractive Error ◽  
Prediction Error ◽  
Power Calculation ◽  
Closure Rate ◽  
Refractive Outcomes ◽  
Age Related ◽  
Macular Holes ◽  
Myopic Shift ◽  
Idiopathic Macular Holes ◽  
The Difference

AIM: To report the refractive outcomes after vitrectomy combined with phacoemulsification and intraocular lens (IOL) implantation (phaco-vitrectomy) in idiopathic macular holes (IMH). METHODS: A total of 56 eyes with IMH (IMH group) that underwent phaco-vitrectomy and 44 eyes with age-related cataract (ARC group) that underwent cataract surgery were retrospectively reviewed. The best corrective visual acuity (BCVA), predicted refractive error (PRE), actual refractive error (ARE), axial length (AL), were measured in both groups before and 6mo after operation. The power calculation of IOL and the predicted refractive error (PRE) were calculated according to the SRK/T formula. The difference of PRE and ARE between the two groups were compared and analyzed. RESULTS: In the IMH group, the diameters of macular holes were 271.73±75.85 μm, the closure rate was 100%. The pre- and post-operative BCVA were 0.80±0.35 and 0.40±0.35 logMAR. The PRE of A-ultrasound and IOL Master in the IMH group was -0.27±0.25 and 0.10±0.66 D. The postoperative mean absolute prediction error (MAE) was observed to be 0.58±0.65 and 0.53±0.37 D in the IOL Master and A-ultrasound (P=0.758). The PRE and ARE of the IMH group were 0.10±0.66 D and -0.19±0.64 D (P=0.102). The PRE and ARE of the ARC group was -0.43±0.95 and -0.31±0.93 D (P=0.383). The difference between PRE and ARE was -0.33±0.81 and 0.09±0.64 D in the IMH and ARC groups (P=0.021). The proportion of myopic shift was 67.9% in the IMH group and 27.3% in the ARC group (P=0.004). CONCLUSION: The myopic shift can be observed in patients with IMH after phaco-vitrectomy.

scholarly journals The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA

2022 ◽  
Vol 13 ◽  
Author(s):  
Niklas Wulms ◽  
Lea Redmann ◽  
Christine Herpertz ◽  
Nadine Bonberg ◽  
Klaus Berger ◽  
...  
Keyword(s):  
White Matter ◽  
Sample Size ◽  
Mean Absolute Error ◽  
External Validation ◽  
Similarity Index ◽  
Absolute Error ◽  
Training Sample ◽  
White Matter Hyperintensity ◽  
Training Sample Size ◽  
The Difference

Introduction: White matter hyperintensities of presumed vascular origin (WMH) are an important magnetic resonance imaging marker of cerebral small vessel disease and are associated with cognitive decline, stroke, and mortality. Their relevance in healthy individuals, however, is less clear. This is partly due to the methodological challenge of accurately measuring rare and small WMH with automated segmentation programs. In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.Methods: We evaluated the effect of varying training sample sizes on the accuracy and the robustness of the predicted white matter hyperintensity volume in a population (n = 201) with a low prevalence of confluent WMH and a substantial proportion of participants without WMH. BIANCA was trained with seven different sample sizes between 10 and 40 with increments of 5. For each sample size, 100 random samples of T1w and FLAIR images were drawn and trained with manually delineated masks. For validation, we defined an internal and external validation set and compared the mean absolute error, resulting from the difference between manually delineated and predicted WMH volumes for each set. For spatial overlap, we calculated the Dice similarity index (SI) for the external validation cohort.Results: The study population had a median WMH volume of 0.34 ml (IQR of 1.6 ml) and included n = 28 (18%) participants without any WMH. The mean absolute error of the difference between BIANCA prediction and manually delineated masks was minimized and became more robust with an increasing number of training participants. The lowest mean absolute error of 0.05 ml (SD of 0.24 ml) was identified in the external validation set with a training sample size of 35. Compared to the volumetric overlap, the spatial overlap was poor with an average Dice similarity index of 0.14 (SD 0.16) in the external cohort, driven by subjects with very low lesion volumes.Discussion: We found that the performance of BIANCA, particularly the robustness of predictions, could be optimized for use in populations with a low WMH load by enlargement of the training sample size. Further work is needed to evaluate and potentially improve the prediction accuracy for low lesion volumes. These findings are important for current and future population-based studies with the majority of participants being normal aging people.

Accuracy of common IOL power formulas in 611 eyes based on axial length and corneal power ranges

2020 ◽  
pp. bjophthalmol-2020-315882
Author(s):  
Veronika Röggla ◽  
Achim Langenbucher ◽  
Christina Leydolt ◽  
Daniel Schartmüller ◽  
Luca Schwarzenbacher ◽  
...  
Keyword(s):  
Axial Length ◽  
Prediction Error ◽  
Absolute Error ◽  
Power Calculation ◽  
Corneal Power ◽  
Biometric Parameters ◽  
Iol Power Calculation ◽  
Iol Power ◽  
Median Absolute Error ◽  
Axial Eye Length

AimsTo provide clinical guidance on the use of intraocular lens (IOL) power calculation formulas according to the biometric parameters.Methods611 eyes that underwent cataract surgery were retrospectively analysed in subgroups according to the axial length (AL) and corneal power (K). The predicted residual refractive error was calculated and compared to evaluate the accuracy of the following formulas: Haigis, Hoffer Q, Holladay 1 and SRK/T. Furthermore, the percentages of eyes with ≤±0.25, ≤±0.5 and 1 dioptres (D) of the prediction error were recorded.ResultsThe Haigis formula showed the highest percentage of cases with ≤0.5 D in eyes with a short AL and steep K (90%), average AL and steep cornea (73.2%) but also in long eyes with a flat and average K (65% and 72.7%, respectively). The Hoffer Q formula delivered the lowest median absolute error (MedAE) in short eyes with an average K (0.30 D) and Holladay 1 in short eyes with a steep K (Holladay 1 0.24 D). SRK/T presented the highest percentage of cases with ≤0.5 D in average long eyes with a flat and average K (80.5% and 68.1%, respectively) and the lowest MedAE in long eyes with an average K (0.29 D).ConclusionOverall, the Haigis formula shows accurate results in most subgroups. However, attention must be paid to the axial eye length as well as the corneal power when choosing the appropriate formula to calculate an IOL power, especially in eyes with an unusual biometry.

scholarly journals P112 Prediction of free Ceftriaxone concentration in children: disease and maturation do matter

2019 ◽  
Vol 104 (6) ◽  
pp. e64.2-e64
Author(s):  
H-Y Shi ◽  
X Huang ◽  
Q Li ◽  
Wu Y-E ◽  
MW Khan ◽  
...  
Keyword(s):  
Linear Regression ◽  
Mean Absolute Error ◽  
Predictive Ability ◽  
Predictive Performance ◽  
Absolute Error ◽  
Regression Equations ◽  
Free Concentration ◽  
In Vivo Binding ◽  
Mean Prediction Error

BackgroundTo evaluate the predictive ability of the existing formula to measure free ceftriaxone levels in children, and optimize the formula by adding disease and maturation factors.MethodsFifty children receiving ceftriaxone were evaluated, and the predictive performance of the different equations were assessed by mean absolute error (MAE), mean prediction error (MPE) and linear regression of predicted vs. actual free levels.ResultsThe average free ceftriaxone concentration was 2.11 ± 9.51µg/ml. The predicted free concentration was 1.15 ± 4.39µg/ml with the in vivo binding equation, which increased to 1.58 ± 7.73µg/ml and 2.01 ± 9.53µg/ml when adjusted for age (disease adapted equation), and age and albumin (disease-maturation equation) respectively. The average MAE values were 0.48 (in vivo banding equation), 0.34 (disease adapted equation) and 0.41 (disease maturation equation). The average MPE values were -0.41 (in vivo binding equation), 0.14 (disease adapted equation) and 0.09 (disease maturation equation). The respective linear regression equations and coefficients were y=1.8647x+1.0731(R2=0.7398), y=1.1455x+0.8414(R2=0.8674), and y=0.9664x(R2=0.8641) for the in vivo binding, disease adapted and disease maturation equations respectively.ConclusionCompared to the in vivo binding equation, the disease adapted and disease maturation equations showed lower MAE and MPE values, and the latter showed the lowest MPE value. In addition, the slope of the disease maturation equation was closer to 1 compared to the other two. Therefore, the optimized disease maturation equation should be used to measure free ceftriaxone levels in children.Disclosure(s)Nothing to disclose.

Comparison of using Galilei Dual Scheimpflug Analyzer G4 and Barrett formula in predicting low cylinder preoperatively for cataract surgeries

2019 ◽  
Vol 30 (6) ◽  
pp. 1320-1327
Author(s):  
Yi-Ju Ho ◽  
Chi-Chin Sun ◽  
Jiahn-Shing Lee ◽  
Ken-Kuo Lin ◽  
Chiun-Ho Hou
Keyword(s):  
Cataract Surgery ◽  
Absolute Error ◽  
Corneal Astigmatism ◽  
Intraocular Lens Implantation ◽  
Surgically Induced Astigmatism ◽  
Lens Implantation ◽  
Induced Astigmatism ◽  
Median Absolute Error ◽  
The Difference ◽  
Surgically Induced

Purpose: To compare corneal astigmatism estimation from Barrett toric calculator, with measurement from Galilei Dual Scheimpflug Analyzer G4 in low corneal cylinder patients. Methods: Preoperative corneal astigmatism was measured using Auto Kerato-Refractometer (AutoKM), IOL Master, and Galilei G4 (combined Placido-dual Scheimpflug analyzer) and was processed by Barrett toric calculator with measurements obtained from Auto Kerato-Refractometer and from IOL Master. A total of 42 eyes undergoing cataract surgery with nontoric intraocular lens implantation were included. Corneal astigmatism was calculated based on manifest refractive astigmatism with implications of surgically induced astigmatism. Errors in predicted residual astigmatism were calculated by the difference between postoperative manifest cylindrical refractive error and preoperative corneal cylinder using vector analysis. Results: Centroid error in predicted residual astigmatism was with-the-rule 0.36 D for AutoKM and 0.48 D for IOL Master, was lower at 0.24 D for the Barrett–IOL Master, and was lowest at 0.21 D for the Barrett–AutoKM ( p < .001). The Galilei G4 demonstrated the highest centroid error for SimK (0.53 D) and lower for total corneal power (0.49 D). The Barrett toric calculator obtained the lowest median absolute error in predicted residual astigmatism for AutoKM (0.43 D) and IOL Master (0.54 D). The Barrett–IOL Master demonstrated that 61% and 76% of eyes were within 0.50 and 0.75 D of the predicted residual astigmatism, respectively. Conclusion: The Barrett–IOL Master had more accurate prediction of residual astigmatism for low astigmatism eyes before cataract surgery compared to Galilei Dual Scheimpflug Analyzer G4 in this study.

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