Keratometry using hand-held and automated keratometers with and without speculum in Indian pediatric cataract – A comparative study

To study the keratometry of Indian pediatric eyes, the effect of speculum on keratometry reading, the concordance of hand held and automated keratometry and the effect of unilateral and bilateral cataract on keratometry and IOL power calculation. This was conducted as a cross- sectional observational study on 101 eyes of children in the age range of 41 post-conceptional weeks to 144 months. All cooperative patients were subject to automated keratometry followed by keratometry using hand held keratometer with and without speculum. Hand held keratometer with and without speculum documented significantly increased average K as well as astigmatism and decreased calculated IOL power when compared to automated keratometry (p<0.01). No significant difference in K readings was observed between unilateral and bilateral cataracts and among males and females (p>0.05). As the age increased, astigmatism increased significantly (R=0.07; p=0.007) whereas no such correlation was observed for keratometry (p>0.05). Hand held keratometry offers the convenience of obtaining accurate keratometry, astigmatism and IOL power measurements in children.

Biometry in Silicone Oil Filled Eyes. A Review

The “gold standard” of modern vitreoretinal surgery is silicone oil tamponade of the vitreous cavity. The lens opacity development is in the list of complications of prolonged silicone oil eye filling (from 2 weeks to 2 years). Polydimethylsiloxanes hydrophobicity, direct contact with the front of the silicone bladder, macrophage and toxic reaction, trophic disturbances are the causes leading to the cataract initiation. This makes the problem of cataract surgery and preliminary intraocular lens calculation in silicone oil filled eyes before its removing very relevant as well as cloudy retina visualization and the necessity of minimization of number of operations through their combination. Certainly, the main error in IOL power calculation is associated with axial length measurement inaccuracy, as the most significant term of an equation. Silicone oil filled eyes biometry errors, and, consequently, postoperative refraction biases remain unresolved problem until now. To date authors report only 58 % of cases in which target refraction was achieved after combined surgery. Some researchers figure out that average calculation error after phacoemulsification with IOL implantation in avitreal eyes was 0.8 D despite of the optical biometry usage. Today it is represented by several methods: partial coherent interferometry, optical low-coherence reflectometry and optical coherence tomography, which are implemented in devices such as IOLMaster 500, Lenstar LS 900 and IOLMaster 700, which have their own characteristics and measurement accuracy. Their advantages as well as creation an accurate IOL calculation method for silicone oil filled eyes could reduce postoperative refraction error that outline significant medical and social problem.

Comparison of biometric methods in young children with congenital cataracts in their eyes

BACKGROUND: Relevant keratometric and biometric indicators are necessary for intraocular lens (IOL) power calculation, which is difficult to verify in young children. AIM: Evaluation of the accuracy of various ultrasound methods and optical biometry for axial length measurement in young children with congenital cataracts. MATERIAL AND METHODS: Forty-six children (74 eyes) with congenital cataracts (43 eyes) and pseudophakia (31 eyes) at the age of 6 months to 4 years were examined. Various methods measured the axial length: ultrasound A-scan under general anesthesia by US-4000, ultrasound B-scan without general anesthesia by Voluson E8, and optical biometry by AL-Scan in cases of transparent optics. RESULTS: The greater axial length difference was observed between A-scan and optical biometry (less by 0,78 mm) than between B-scan and optical biometry (more by 0,27 mm). The median axial length difference between A-scan and B-scan was equal for infants and young children with congenital cataracts (0,525 mm and 0,535 mm, respectively). CONCLUSION: Axial length should be measured by different methods in young children with their further comparison to obtaining more accurate biometric indicators for IOL power calculation. The decrease of 12 mm in axial length, which occurs during the A-scan, can lead to errors in the IOL calculation of 36 diopters and unplanned refraction in the long-term period.

MOPower: an R-shiny application for the simulation and power calculation of multi-omics studies.

Background: Multi-omics studies are increasingly used to help understand the underlying mechanisms of clinical phenotypes, integrating information from the genome, transcriptome, epigenome, metabolome, proteome and microbiome. This integration of data is of particular use in rare disease studies where the sample sizes are often relatively small. Methods development for multi-omics studies is in its early stages due to the complexity of the different individual data types. There is a need for software to perform data simulation and power calculation for multi-omics studies to test these different methodologies and help calculate sample size before the initiation of a study. This software, in turn, will optimise the success of a study. Results: The interactive R shiny application MOPower described below simulates data based on three different omics using statistical distributions. It calculates the power to detect an association with the phenotype through analysis of n number of replicates using a variety of the latest multi-omics analysis models and packages. The simulation study confirms the efficiency of the software when handling thousands of simulations over ten different sample sizes. The average time elapsed for a power calculation run between integration models was approximately 500 seconds. Additionally, for the given study design model, power varied with the increase in the number of features affecting each method differently. For example, using MOFA had an increase in power to detect an association when the study sample size equally matched the number of features. Conclusions: MOPower addresses the need for flexible and user-friendly software that undertakes power calculations for multi-omics studies. MOPower offers users a wide variety of integration methods to test and full customisation of omics features to cover a range of study designs.

Effect of Implantable Collamer Lens on Anterior Segment Measurement and Intraocular Lens Power Calculation Based on IOLMaster 700 and Sirius

Purpose. To investigate the possible effect of an implantable collamer lens (ICL) on ocular biometrics and intraocular lens (IOL) power calculation. Methods. Ocular measurements were taken preoperatively and at the two-month follow-up using IOLMaster 700 and Sirius in 85 eyes (43 patients) who had previously undergone ICL surgery. IOL power was calculated using either IOLMaster 700 (Barrett Universal II formula) or Sirius (ray-tracing). All data were compared using the paired t-test. Results. The difference between preoperative and postoperative anterior chamber depth (ACD), lens thickness (LT), and keratometry on the steep axis (K2) measured by IOLMaster 700 was statistically significant (p < 0.001). In 11 of 85 eyes, IOLMaster misjudged the anterior surface of the ICL as that of the lens, leading to an error in ACD and LT. There were no significant differences between preoperative and postoperative axial length (AL) (p = 0.223), white to white (WTW) (p = 0.100), keratometry on flat axis (K1) (p = 0.117), or central corneal thickness (CCT) (p = 0.648), measured using IOLMaster. The difference in IOL power calculated using the Barrett II formula was significant (p = 0.013). Regression analysis showed that AL and K had the greatest influence on IOL calculation (p < 0.001), and ACD and LT had less influence (p = 0.002, p = 0.218, respectively). K1 and K2 were modified to exclude the influence of K2, and modified IOLs showed no difference between pre and postoperation (p = 0.372). Preoperative and postoperative ACD measured using Sirius were significantly different (p < 0.001); however, the IOL power calculated using ray-tracing technology showed no significant differences (p > 0.05). Conclusions. The ocular biometric apparatus may misjudge the anterior surface of the lens, resulting in measurement errors of ACD and LT, which has little effect on the calculation of IOL power when using IOLMaster 700 (Barrett Universal II formula) and Sirius (ray-tracing).

Development and Evaluation of the Prognostic Nomogram to Predict Refractive Error in Patients With Primary Angle-Closure Glaucoma Who Underwent Cataract Surgery Combined With Goniosynechialysis

Objective: To evaluate the accuracy of different intraocular lens (IOL) power calculation formulas and develop prognostic nomograms to predict the risk of postoperative refractive error in primary angle-closure glaucoma (PACG) patients.Methods: A total of 111 eyes with PACG underwent goniosynechialysis combined with phacoemulsification and IOL implantation were included. SRK/T, Barrett II, Hoffer Q, and Kane formulas were used to predict postoperative refraction. Prediction error (PE) and absolute predictive error (APE) produced by the four formulas were calculated and compared. An APE &gt;0.50 D was defined as the event. Binary logistic regression analysis and prognostic nomogram models were conducted to investigate reliable predictors associated with postoperative refraction.Results: The Kane (−0.06 D) and Barrett II (−0.07 D) formulas had mean prediction error close to zero (p = 0.44, p = 0.41, respectively). The Hoffer Q and SRK/T produced significantly myopic outcomes (p = 0.003, p = 0.013, respectively). The percentage of eyes within ± 0.5 D was 49.5% (55/111), 44.1% (49/111), 43.2% (48/111), and 49.5% (54/111), for the Kane, Barrett II, Hoffer Q, and SRK/T formula, respectively. Nomogram showed that AL had the greatest impact on the refractive outcomes, indicating a shorter preoperative AL is associated with a greater probability of refractive error event. The area under the receiver operator curve (AUC) of the nomogram for the Kane, Barrett II, Hoffer Q, and SRK/T was 0.690, 0.701, 0.708, and 0.676, respectively.Conclusions: The Kane and Barrett II formulas were comparable, and they outperformed Hoffer Q and SRK/T in the total eyes with PACG receiving cataract surgery combined with goniosynechialysis. The developed nomogram models can effectively predict the occurrence of postoperative refractive error events.

Inaccuracy in arc power calculation through a product of voltage and current averages

Power is an indirect measurand, determined by processing voltage and current analogue signals through calculations. Using arc welding as a case study, the objective of this work was to bring up subsidies for power calculation. Based on the definitions of correlation and covariance in statistics, a mathematical demonstration was developed to point out the difference between the product of two averages (e.g. P = $$\overline{U} x \overline{I}$$ U ¯ x I ¯ ) and the average of the products (e.g. P = ($$\overline{UxI}$$ UxI ¯ ). Complementarily, a brief on U and I waveform distortion sources were discussed, emphasising the difference between signal standard deviations and measurement errors. It was demonstrated that the product of two averages is not the same as the average of the products, unless in specific conditions (when the variables are fully correlated). It was concluded that the statistical correlation can easily flag the interrelation, but if assisted by covariance, these statistics quantify the inaccuracy between approaches. Finally, although the statistics' determination is easy to implement, it is proposed that power should always be calculated as the average of the instantaneous U and I products. It is also proposed that measurement error sources should be observed and mitigated, since they predictably interfere in power calculation accuracy.

Allowing for stratification in sample size planning of two-arm trials with continuous or binary outcome: Overview and tutorial

More often than not, clinical trials and even nonclinical medical experiments have to be run with observational units sampled from populations to be assumed heterogeneous with respect to covariates associated with the outcome. Relevant covariates which are known prior to randomization are usually categorical in type, and the corresponding subpopulations are called strata. In contrast to randomization which in most cases is performed in a way ensuring approximately constant sample size ratios across the strata, sample size planning is rarely done taking stratification into account. This holds true although the statistical literature provides a reasonably rich repertoire of testing procedures for stratified comparisons between two treatments in a parallel group design. For all of them, at least approximate methods of power calculation are available from which algorithms or even closed-form formulae for required sample sizes can be derived. The objective of this tutorial is to give a systematic review of the most frequently applicable of these methods and to compare them in terms of their efficiency under standard settings. Based on the results, recommendations for the sample size planning of stratified two-arm trials are given.