Mean Absolute Prediction Error Research Articles

Automatic Landmark Detection for Preoperative Planning of High Tibial Osteotomy Using Traditional Feature Extraction and Deep Learning Methods.

Automatic High Tibial Osteotomy (HTO) landmark detection methods promise to improve the effectiveness and standardisation of HTO preoperative planning. Unfortunately, due to the limited number of HTO datasets, existing methods are less robust when dealing with patients with varied deformities than traditional manual planning, severely limiting their clinical viability and application in practical surgical settings. Here, we present a new HTO landmark detection framework using an integration of optimised heatmap-offset aggregation method and traditional feature extraction. Subjective and objective approaches were employed to reflect the final clinical acceptance of our model. Average Mean Absolute Error of prediction results compared to the surgeon's gold standard was 0.35° for the hip-knee-ankle angle. The objective score rated by surgeons reached 4.4 on a scale of 5. The study demonstrated that the automatic detection method has great potential serving as an alternative to manual radiological analysis in practical surgical pre-operative planning.

Competing and Noncompeting Risk Models for Predicting Kidney Allograft Failure.

Prognostic models are becoming increasingly relevant in clinical trials as potential surrogate endpoints, and for patient management as clinical decision support tools. However, the impact of competing risks on model performance remains poorly investigated. We aimed to carefully assess the performance of competing risk and noncompeting risk models in the context of kidney transplantation, where allograft failure and death with a functioning graft are two competing outcomes. We included 11,046 kidney transplant recipients enrolled in 10 countries. We developed prediction models for long-term kidney graft failure prediction, without accounting (i.e., censoring) and accounting for the competing risk of death with a functioning graft, using Cox, Fine-Gray, and cause-specific Cox regression models. To this aim, we followed a detailed and transparent analytical framework for competing and noncompeting risk modelling, and carefully assessed the models' development, stability, discrimination, calibration, overall fit, clinical utility, and generalizability in external validation cohorts and subpopulations. More than 15 metrics were used to provide an exhaustive assessment of model performance. Among 11,046 recipients in the derivation and validation cohorts, 1,497 (14%) lost their graft and 1,003 (9%) died with a functioning graft after a median follow-up post-risk evaluation of 4.7 years (IQR 2.7-7.0). The cumulative incidence of graft loss was similarly estimated by Kaplan-Meier and Aalen-Johansen methods (17% versus 16% in the derivation cohort). Cox and competing risk models showed similar and stable risk estimates for predicting long-term graft failure (average mean absolute prediction error of 0.0140, 0.0138 and 0.0135 for Cox, Fine-Gray, and cause-specific Cox models, respectively). Discrimination and overall fit were comparable in the validation cohorts, with concordance index ranging from 0.76 to 0.87. Across various subpopulations and clinical scenarios, the models performed well and similarly, although in some high-risk groups (such as donors over 65 years old), the findings suggest a trend towards moderately improved calibration when using a competing risk approach. Competing and noncompeting risk models performed similarly in predicting long-term kidney graft failure.

Refractive outcomes following simultaneous silicone oil removal and ciliary sulcus intraocular lens implantation.

This study aimed to assess the refractive outcomes following the combined intervention of silicon oil (SO) removal and ciliary sulcus intraocular lens (IOL) implantation in aphakic eyes after phacovitrectomy. A retrospective examination of medical records from patients who underwent a combined procedure of SO removal and ciliary sulcus IOL implantation from 2019 to 2022 was performed. The primary outcomes of interest included uncorrected distance visual acuity (UDVA), predictive error (PE) and mean absolute predictive error (MAE). Subgroup analyses were performed to compare outcomes between patients with and without intraoperative posterior capsulotomy. The cohort comprised 40 eyes from 40 patients, with a mean duration of SO tamponade of 5.17 ± 1.48 months. A significant improvement in UDVA was observed from 1.58 ± 0.31 logMAR to 0.99 ± 0.31 logMAR one month postoperatively (P < 0.01). The PE was -0.73 ± 0.86 diopters (D), and the MAE was 0.90 ± 0.67 D, with 37.50% and 57.50% of cases achieving PE within ±0.5 D and ±1.0 D, respectively. No significant differences in UDVA, PE, or MAE were found between the capsulotomy and non-capsulotomy subgroups (all P > 0.05). However, a lower proportion of eyes in the capsulotomy group achieved PE within ±1.0 D compared to the non-capsulotomy group one month postoperatively (38.90% vs. 72.70%, P = 0.03). The combined procedure of SO removal and sulcus IOL implantation resulted in a mild myopic shift in postoperative refraction. Intraoperative posterior capsulotomy seems to increase the lability of postoperative refractive outcomes.

Gradient boosting: A computationally efficient alternative to Markov chain Monte Carlo sampling for fitting large Bayesian spatio-temporal binomial regression models

Disease forecasting and surveillance often involve fitting models to a tremendous volume of historical testing data collected over space and time. Bayesian spatio-temporal regression models fit with Markov chain Monte Carlo (MCMC) methods are commonly used for such data. When the spatio-temporal support of the model is large, implementing an MCMC algorithm becomes a significant computational burden. This research proposes a computationally efficient gradient boosting algorithm for fitting a Bayesian spatio-temporal mixed effects binomial regression model. We demonstrate our method on a disease forecasting model and compare it to a computationally optimized MCMC approach. Both methods are used to produce monthly forecasts for Lyme disease, anaplasmosis, ehrlichiosis, and heartworm disease in domestic dogs for the contiguous United States. The data have a spatial support of 3108 counties and a temporal support of 108–138 months with 71–135 million test results. The proposed estimation approach is several orders of magnitude faster than the optimized MCMC algorithm, with a similar mean absolute prediction error.

Comparison of the Accuracy Between the Z CALC2 Calculator and Barrett Toric Calculator in Toric IOL Calculation.

To compare the accuracy of the Z CALC2 calculator and Barrett toric calculator in toric intraocular lens (IOL) calculation. Eighty-five eyes of 85 patients who underwent uneventful cataract surgery with toric IOL implantation were included. The accuracy was compared between the Z CALC2 calculator and Barrett toric calculator under two calculation modes: using simulated keratometry (SimK) from the IOLMaster 700 (Carl Zeiss Meditec AG) for posterior corneal astigmatism (PCA) prediction and employing total corneal astigmatism (total corneal refractive power [TCRP] or measured PCA) obtained from the Pentacam (Oculus Optikgeräte GmbH). The centroid of prediction errors, percentage of eyes with prediction errors within ±0.50 diopter (D), mean prediction error, and mean absolute prediction error were calculated. Subgroup analysis was conducted based on the orientation and magnitude of anterior corneal astigmatism and axial length. When using SimK, the two calculators with predicted PCA showed comparable accuracy. When employing total corneal astigmatism, the Barrett toric calculator with measured PCA showed a lower centroid error (0.15 vs 0.38 D), a higher percentage of eyes with prediction errors within ±0.50 D (47.1% vs 32.9%, P = .018), and a lower mean prediction error (0.57 vs 0.71 D, P = .033) compared to the Z CALC2 calculator with TCRP in the 4-mm zone. In subgroup analysis, when employing total corneal astigmatism, the Barrett toric calculator with measured PCA exhibited superior accuracy, especially in the with-the-rule and anterior corneal astigmatism of 2.00 D or less subgroups. When using SimK, the Z CALC2 calculator and Barrett toric calculator yield comparable accuracy. The Barrett toric calculator with measured PCA may be more recommended when employing total corneal astigmatism. [J Refract Surg. 2024;40(10):e681-e691.].

Bayesian impedance deconvolution using timescale distribution for lithium-ion battery state estimation

The distribution of relaxation times (DRT) and distribution of diffusion times (DDT) are widely recognized as effective model-free methods for deconvolving the internal properties of complex electrochemical systems using electrochemical impedance spectroscopy (EIS) data. This study proposes an integrated framework that employs a Bayesian approach to accurately estimate both DRT and DDT and machine learning techniques to enhance capacity estimation, incorporating Gaussian process regression and transformer networks. These methods utilize the inferred DRT and DDT as inputs to predict the discharge capacity. In addition, we perform a peak analysis on the estimated DRT and DDT to extract additional physically meaningful features, which have also proven to be effective inputs for capacity prediction. The applicability of the proposed framework to the EIS experimental data of lithium-ion cells is demonstrated and compared with existing capacity estimation methods. The proposed framework demonstrates a mean absolute error in capacity prediction below 3.03% when using DRT and DDT directly and 2.71% when using extracted features, outperforming existing methods by approximately one to three percentage points. Our Bayesian approach, combined with peak analysis and the integration of machine learning techniques, enables a more robust diagnosis of the internal state of lithium-ion batteries through EIS measurements.

Effect of Posterior Keratometry and Corneal Radius Ratio on the Accuracy of Intraocular Lens Formulas After Myopic LASIK/PRK.

To investigate the impact of back-to-front corneal radius ratio (B/F ratio) and posterior keratometry (PK) on the accuracy of intraocular lens power calculation formulas in eyes after myopic laser in situ keratomileusis (LASIK)/photorefractive keratectomy (PRK) surgery. A retrospective, consecutive case series study included 101 patients (132 eyes) with cataract after myopic LASIK/PRK. Mean prediction error (PE), mean absolute PE (MAE), median absolute error (MedAE), and the percentage of eyes within ±0.25, ±0.50, and ±1.00 diopters (D) of PE were determined. The Barrett True K-TK formula exhibited the lowest MAE (0.59 D) and MedAE (0.48 D) and the highest percentage of eyes within ±0.50 D of PE (54.55%) in total. In eyes with a B/F ratio of 0.70 or less and PK of -5.70 D or greater, the Potvin-Hill formula displayed the lowest MAE (0.46 to 0.67 D). The Barrett True-TK exhibited the highest prediction accuracy in eyes after myopic LASIK/PRK overall. However, for eyes with a low B/F ratio and flat PK, the Potvin-Hill performed best. [J Refract Surg. 2024;40(9):e635-e644.].

The performance of prognostic models depended on the choice of missing value imputation algorithm: a simulation study

ObjectiveThe development of clinical prediction models is often impeded by the occurrence of missing values in the predictors. Various methods for imputing missing values before modelling have been proposed. Some of them are based on variants of multiple imputation by chained equations, while others are based on single imputation. These methods may include elements of flexible modelling or machine learning algorithms, and for some of them user-friendly software packages are available. The aim of this study was to investigate by simulation if some of these methods consistently outperform others in performance measures of clinical prediction models. Study Design and SettingWe simulated development and validation cohorts by mimicking observed distributions of predictors and outcome variable of a real data set. In the development cohorts, missing predictor values were created in 36 scenarios defined by the missingness mechanism and proportion of non-complete cases. We applied three imputation algorithms that were available in R software: mice, aregImpute and missForest. These algorithms differed in their use of linear or flexible models, or random forests, the way of sampling from the predictive posterior distribution, and the generation of a single or multiple imputed data sets. For multiple imputation we also investigated the impact of the number of imputations. Logistic regression models were fitted with the simulated development cohorts before (full data analysis) and after missing value generation (complete case analysis), and with the imputed data. Prognostic model performance was measured by the scaled Brier score, c-statistic, calibration intercept and slope, and by the mean absolute prediction error evaluated in validation cohorts without missing values. Performance of full data analysis was considered as ideal. ResultsNone of the imputation methods achieved the model’s predictive accuracy that would be obtained in case of no missingness. In general, complete case analysis yielded the worst performance, and deviation from ideal performance increased with increasing percentage of missingness and decreasing sample size. Across all scenarios and performance measures, aregImpute and mice, both with 100 imputations, resulted in highest predictive accuracy. Surprisingly aregImpute outperformed full data analysis in achieving calibration slopes very close to 1 across all scenarios and outcome models. The increase of mice’s performance with 100 compared to 5 imputations was only marginal. The differences between the imputation methods decreased with increasing sample sizes and decreasing proportion of non-complete cases. ConclusionIn our simulation study, model calibration was more affected by the choice of the imputation method than model discrimination. While differences in model performance after using imputation methods were generally small, multiple imputation methods as mice and aregImpute that can handle linear or nonlinear associations between predictors and outcome are an attractive and reliable choice in most situations.

Prediction of body condition score throughout lactation by random regression test-day models.

Regular monitoring of body condition score (BCS) changes during lactation is a crucial management tool in dairy cattle; however, the current BCS measurements are often discontinuous and unevenly spaced in time. The aim of this study was to investigate the ability of random regression test-day models (RR-TDM) to predict BCS for the entire lactation in dairy cows even if the actual scoring is limited to one BCS record. The data consisted of test-day records of milk yield (MY), fat percentage (FP), protein percentage (PP) and BCS (based on a 9-point scale with unit increments; 1-9) collected from 2014 to 2022 in 128 herds in the Walloon Region of Belgium. In total, 20,698 test-day records on 2166 first-parity Holstein cows (2-12 with an average of 9.42 test-day records per cow) were available for MY, FP and PP; and 7985 records on the same animals (2-12 with an average of 3.68 records per cow) were available for BCS. To estimate the solutions, only one randomly selected BCS record per animal along with all her MY, FP and PP records were used, which were then used to predict BCS data (calibration set). The remaining BCS (1-11 with an average 2.86 BCS records per animal) were used to evaluate the goodness of the predictions (validation set). Multiple-trait RR-TDM was used to estimate (co)variance components through the average information restricted maximum likelihood (AI-REML) algorithm. Predicted BCS were grouped into nine classes as the original observed BCS used for comparison. Pearson correlation between the predicted and observed BCS, prediction error (PE), absolute prediction error (APE) and root mean squared prediction error (RMSE) were calculated. Mean (standard deviation; SD) BCS was 4.97 (1.01), 4.95 (1.07) and 4.98 (1.00) BCS units in the full, calibration and validation datasets, respectively. Pearson correlation between the observed and predicted BCS was 0.71, mean (SD) PE was 0.04 (0.52) BCS units, mean (SD) APE was 0.48 (0.53) BCS units and RMSE was 0.72 BCS units. These findings demonstrate the ability of RR-TDM to predict BCS for the entire lactation using a single BCS record along with available test-day records of milk yield and composition in Holstein dairy cows.

Advancing polar motion prediction with derivative information

Abstract Earth Orientation Parameters (EOP) are essential for monitoring Earth’s rotational irregularities, impacting satellite navigation, space exploration, and climate forecasting. This study introduces a hybrid prediction model combining least-squares (LS) and vector autoregression (VAR) to improve Earth’s Pole Coordinates (x, y) forecast accuracy. Using daily sampled IERS EOP 20 C04 data from 2013 to 2023, we conducted 1,000 yearly random trials, performing 48 forecasts per year. Our method evaluates six data combinations, including primary variables (x, y) and their derivatives ( x ̇ , y ̇ $\dot{x},\dot{y}$ ). Results show a systematic improvement in prediction accuracy, especially for ultra-short-term forecasts (10 days into future), with derivative information stabilizing the solutions. The best-performing combination ( x , y , x ̇ , y ̇ $x,y,\dot{x},\dot{y}$ ) achieved a mean absolute prediction error (MAPE) reduction (with respect to the reference data combination – x, y) of up to 8 % for the y and 7 % for the x over a whole 30-day forecast horizon. These findings highlight the effectiveness of incorporating derivatives of polar motion time series into prediction procedure.

Data-based modeling of the Pharmacodynamics for the effect of Propofol and Remifentanil during General Anesthesia

Predicting Bispectral Index (BIS) and Mean Arterial Pressure (MAP) during anesthesia is critical for patient safety and effective anesthesia management. Traditional pharmacodynamic response surface models have limitations in accuracy and adaptability. This paper presents a novel approach for predicting BIS and MAP using machine learning techniques. Rather than using standard pharmacodynamic response surface models, a machine learning-based approach is proposed to model the pharmacodynamic. The proposed method considers the state of the standard propofol and remifentanil pharmacokinetic model and the patient information as features to predict the BIS and MAP values. Training and testing are done on a selected subset of the VitalDB dataset (Lee and Jung, 2018) containing 191 different patients. Results demonstrate that the machine learning-based approach outperforms standard pharmacodynamic models in terms of accuracy. Specifically, the Support Vector Regression (SVR) model achieves a Mean Absolute Prediction Error (MDAPE) 32% smaller than the Eleveld model for BIS prediction. For MAP prediction, the SVR model also demonstrates superior performance with a reduction of 66% of MDAPE. The proposed method provides similar performance as the deep-learning method (Lee et al., 2018) while keeping a simple structure that may be used in other applications.

A critical review of the use of R2 in risk equalization research.

Nearly all empirical studies that estimate the coefficients of a risk equalization formula present the value of the statistical measure R2. The R2-value is often (implicitly) interpreted as a measure of the extent to which the risk equalization payments remove the regulation-induced predictable profits and losses on the insured, with a higher R2-value indicating a better performance. In many cases, however, we do not know whether a model with R2 = 0.30 reduces the predictable profits and losses more than a model with R2 = 0.20. In this paper we argue that in the context of risk equalization R2 is hard to interpret as a measure of selection incentives, can lead to wrong and misleading conclusions when used as a measure of selection incentives, and is therefore not useful for measuring selection incentives. The same is true for related statistical measures such as the Mean Absolute Prediction Error (MAPE), Cumming's Prediction Measure (CPM) and the Payment System Fit (PSF). There are some exceptions where the R2 can be useful. Our recommendation is to either present the R2 with a clear, valid, and relevant interpretation or not to present the R2. The same holds for the related statistical measures MAPE, CPM and PSF.

Accuracy Validation of the New Barrett True Axial Length Formula and the Optimized Lens Factor Using Sum-of-Segment Biometry.

Objectives: This study aims to verify the accuracy of a new calculation formula, Barrett true axial length formula (T-AL), and the optimized lens factor (LF) for predicting postoperative refraction after cataract surgery. Methods: We included 156 Japanese patients who underwent cataract surgery using Clareon monofocal intraocular lenses at our clinic between January 2022 and June 2023. Postoperative spherical equivalent was calculated using subjective refraction values obtained 1 month post-surgery. The LFs were optimized so that the mean prediction error (PE) of each calculation formula was zero (zero optimization). We calculated the mean absolute PE (MAE) to assess accuracy and used a Friedman test for statistical comparisons. The accuracy of T-AL and the optimized LFs was compared with that of the conventional Barrett Universal II formula for ARGOS (AR-B) and OA-2000 (OA-B) with equivalent refractive index. Results: For T-AL, AR-B, and OA-B, the MAEs ± standard deviations were 0.225 ± 0.179, 0.219 ± 0.168, and 0.242 ± 0.206 D, respectively. The Friedman test showed no statistically significant differences among the three groups. The device-optimized LFs were 2.248-2.289 (T-AL), 2.236-2.246 (AR-B), and 2.07-2.08 (OA-B); the corresponding zero-optimized LFs were 2.262-2.287 (T-AL), 2.287-2.303 (AR-B), and 2.160-2.170 (OA-B). Conclusion: There were no significant differences in prediction accuracy among the formulas. However, the accuracy of LF optimization varied by device, with T-AL being closest to the value under zero optimization. This suggests that T-AL is clinically useful for predicting an accurate postoperative refraction without zero optimization.

Retrospective Study of Factors Affecting the Accuracy of Predicting Vancomycin Concentrations in Patients Aged 75 Years and Above.

Background and Objectives: The predicted serum concentrations of vancomycin are determined using population pharmacokinetic parameters. However, the accuracy of predicting vancomycin serum concentrations in the older population remains unclear. Therefore, this study aimed to investigate the accuracy of predicting vancomycin serum concentrations and identifying elements that diminish the prediction accuracy in older people. Materials and Methods: A total of 144 patients aged 75 years or older were included. The serum vancomycin concentrations in the patients were predicted based on population pharmacokinetic parameters common in Japan. We examined the accuracy of serum vancomycin concentration prediction in elderly individuals by comparing the predicted and measured serum vancomycin concentrations in each patient. The prediction accuracy was evaluated using the mean prediction error (ME) and mean absolute error of prediction (MAE) calculated from the measured and predicted serum vancomycin concentrations in each patient. Results: The ME for all patients was 0.27, and the 95% CI included 0, indicating that the predicted values were not significantly biased compared to the measured values. However, the predicted serum concentrations in the <50 kg body weight and serum creatinine (Scr) < 0.6 mg/dL groups were significantly biased compared to the measured values. The group with a history of intensive care unit (ICU) admission showed the largest values for the ME and MAE. Conclusions: Our prediction accuracy was satisfactory but tended to be lower in underweight patients, those with low creatinine levels, and patients admitted to the ICU. Patients with multiple of these factors may experience a greater degree of decreased predictive accuracy.

Recording of single-unit activities with flexible micro-electrocorticographic array in rats for decoding of whole-body navigation

Objective.Micro-electrocorticographic (μECoG) arrays are able to record neural activities from the cortical surface, without the need to penetrate the brain parenchyma. Owing in part to small electrode sizes, previous studies have demonstrated that single-unit spikes could be detected from the cortical surface, and likely from Layer I neurons of the neocortex. Here we tested the ability to useμECoG arrays to decode, in rats, body position during open field navigation, through isolated single-unit activities.Approach. μECoG arrays were chronically implanted onto primary motor cortex (M1) of Wistar rats, and neural recording was performed in awake, behaving rats in an open-field enclosure. The signals were band-pass filtered between 300-3000 Hz. Threshold-crossing spikes were identified and sorted into distinct units based on defined criteria including waveform morphology and refractory period. Body positions were derived from video recordings. We used gradient-boosting machine to predict body position based on previous 100 ms of spike data, and correlation analyses to elucidate the relationship between position and spike patterns.Main results.Single-unit spikes could be extracted during chronic recording fromμECoG, and spatial position could be decoded from these spikes with a mean absolute error of prediction of 0.135 and 0.090 in the x- and y- dimensions (of a normalized range from 0 to 1), and Pearson's r of 0.607 and 0.571, respectively.Significance. μECoG can detect single-unit activities that likely arise from superficial neurons in the cortex and is a promising alternative to intracortical arrays, with the added benefit of scalability to cover large cortical surface with minimal incremental risks. More studies should be performed in human related to its use as brain-machine interface.

Developing Safety Performance Functions for Severe Distraction-Related Crashes along Kentucky’s Rural and Urban Two-Lane Roadways

This study develops safety performance functions (SPFs) for severe (“KA” or “fatal and suspected serious injury”) distraction-related crashes along Kentucky’s rural and urban two-lane undivided roadway segments using recent four-year (2018–2021) crash records. Additional efforts were made to meticulously scrutinize crash narratives categorized as non-distracted and correct those cases. To account for crash under-dispersion, the Conway–Maxwell–Poisson, heterogeneous Conway–Maxwell–Poisson, zero-inflated Conway–Maxwell–Poisson, and zero-inflated heterogeneous Conway–Maxwell–Poisson (ZI-HTCMP) models were fitted and compared. The ZI-HTCMP model outperformed the other models with respect to several goodness-of-fit measures (e.g., mean absolute deviance and mean square prediction error). From the developed SPFs for rural and urban two-lane roads, wider lanes and higher speed limits (55 mph) were associated with increased severe distraction-related crash frequencies. Furthermore, some variables were found to be significant in rural areas, but insignificant in urban areas, and vice versa. For example, major collector roads, minor collector/local roads, the presence of roadside guardrails, wider right-hand shoulders, the presence of horizontal curves, and the presence of vertical grades were associated with increased crash frequencies along rural two-lane roads. In addition, the proportion of heavy vehicles (&gt;5%) and the existence of paved shoulders were associated with increased crashes along urban two-lane roads. The empirical Bayes method was then used to rank the top 10 distraction-related high crash locations (HCLs) for both rural and urban two-lane segments. In-depth investigation of HCLs highlighted single-vehicle distraction-related crashes as the most common collision type. Countermeasures were proposed to help reduce severe distraction-related crashes, for example, installing chevron signs along rural two-lane roads.

Accuracy of 4 Different Methods for Estimation of Remaining Growth and Timing of Epiphysiodesis.

The calculation of remaining growth in children and the timing of epiphysiodesis in those with leg-length discrepancy (LLD) is most often done with 4 methods: the Green-Anderson, White-Menelaus, Moseley straight-line graph, and multiplier methods. The aims of this study were to identify the most accurate method with use of bone age or chronological age and to evaluate the influence of including inhibition in the calculations. One hundred and ninety-one children (10 to 17 years of age) with LLD who underwent surgical closure of the growth plate and were followed until skeletal maturity were identified from a local health register. Patients had at least 2 leg-length examinations with simultaneous bone-age assessments (according to the Greulich and Pyle method), with the last examination performed ≤6 months before surgery. The accuracy of each method was calculated as the mean absolute prediction error (predicted leg length - actual leg length at maturity) for the short leg, the long leg, and the LLD. Comparisons were made among the 4 methods and among calculations made with chronological age versus bone age and those made with versus those without incorporation of a reduced growth rate (inhibition) of the short leg compared with the long leg. The White-Menelaus method with use of bone age and a fixed inhibition rate was the most accurate method, with a prediction error of 1.5 ± 1.5 cm for the short leg, 1.0 ± 1.2 cm for the long leg, and 0.7 ± 0.7 cm for the LLD. Pairwise comparison of short-leg length and LLD according to the White-Menelaus and other methods showed that they were significantly different (p ≤ 0.002). The calculated inhibition rate did not increase accuracy. The White-Menelaus method used with bone age and constant inhibition should be the preferred method when predicting remaining growth and the timing of epiphysiodesis in children between 10 and 17 years of age. One examination is in most cases sufficient for the preoperative clinical investigation when chronological age and bone age are concordant. Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Accuracy of intraocular lens power formulas in patients with average keratometry greater than 46 diopters

ObjectiveTo compare the accuracy of Kane, Barrett Universal II, Haigis, and SRK-T formulas in eyes with average keratometry greater than 46 diopters (D). MethodsA retrospective analysis was conducted on 101 eyes of 101 patients with average keratometry greater than 46 D. The absolute prediction error (EA) was obtained for each patient one month after surgery. The mean absolute prediction error (MEA), median absolute prediction error (MedEA) and the percentage of patients with absolute refractive error less than 0.25 D, 0.50 D, and 1.00 D were calculated for each formula analyzed. ResultsThe Kane formula achieved the lowest MEA (0.53 ± 0.43) and the lowest MedEA (0.41), followed by Barrett Universal II (MEA: 0.56 ± 0.42, MedEA: 0.49), SRK-T (MEA: 0.59 ± 0.44, MedEA: 0.54), and Haigis (MEA: 0.77 ± 0.47, MedEA: 0.69), showing a significant difference in the results. It was also observed that the Kane formula was the most accurate, with the highest percentage of patients, with EA less than 0.25 D, 0.50 D, and 1.00 D (30.7%, 54.4%, and 86.1%, respectively), while the Haigis formula was the least accurate (12.9%, 33.7%, and 69.3%, respectively). ConclusionIn eyes with corneas having average keratometry greater than 46 D, the Kane formula proves to be a useful tool in intraocular lens (IOL) power calculation and demonstrates higher precision compared to the Barrett Universal II, SRK-T, and Haigis formulas.

Fellow eye data for intraocular lens calculation in eyes undergoing combined phacovitrectomy.

To evaluate whether the intraocular lens (IOL) calculation of the fellow eye (FE) can be used in eyes undergoing combined phacovitrectomy. In this retrospective, consecutive case series, we enrolled patients who underwent combined phacovitrectomy with silicone oil removal and IOL implantation at the Goethe-University. Preoperative examinations included biometry (IOLMaster 700; Carl Zeiss). We used the IOL calculation of the FE (FE group) to calculate the prediction error compared with the IOL calculation using only the axial length (AL) of the FE (AL-FE group), as well as using the AL of the operated eye (OE group) in addition to the measurable biometric parameters. IOL calculation was performed using the Barrett Universal II formula. We compared the mean (MAE) and median absolute prediction error (MedAE) with each other. Furthermore, the number of eyes with ±0.50, ±1.00 and ±2.00 dioptres (D) deviation from the target refraction was compared. In total, 79 eyes of 79 patients were included. MedAE was lowest in the OE group (0.41 D), followed by FE group (1.00 D) and AL-FE group (1.02 D). Comparison between the AL-FE and FE groups showed no statistically significant difference (p = 0.712). Comparing eyes within ±0.50 D of the target refraction, the OE group (63.3%) performed best, followed by the AL-FE group (27.8%) and the FE group (26.6%). Our results indicate no clinically relevant difference between using the IOL calculation of the FE versus using only the AL of the FE in addition to the measurable parameters for the IOL calculation. A two-step procedure should always be strived for.

Intraocular lens tilt and decentration after primary and delayed implantation in phacovitrectomy for macula-off rhegmatogenous retinal detachment repair.

To evaluate the tilt and decentration of intraocular lenses (IOLs) in eyes that underwent phacovitrectomy for macula-off rhegmatogenous retinal detachment (RRD) repair. In this retrospective cohort study, 63 patients with macula-off RRD who underwent phacovitrectomy were enrolled and divided into a primary implantation group (group A) and a delayed implantation group (group B) according to the time of IOL implantation. Routine ocular examinations, including optometry, were performed, and the IOL tilt and decentration were measured. Postoperative refractive prediction error and IOL tilt and decentration were compared between the two groups, and the relevant factors of IOL positions were analyzed. The mean spherical refractive prediction error (MSE) was - 0.53 ± 0.39 D in group A and - 0.09 ± 0.55 in group B. The mean absolute spherical refractive prediction error (MASE) was 0.61 ± 0.23 D in group A and 0.50 ± 0.22 D in group B. The difference in MSE (t = -3.623, P < 0.05) but not in MASE (t = 1.866, P = 0.067) between the two groups was significant. The mean IOL tilt and mean decentration were 8.54° ±3.65° and 0.55 ± 0.18 mm in group A and 10.62° ±3.29° and 0.66 ± 0.14 mm, respectively, in Group B. Differences in tilt (t = -2.373, P < 0.05) and decentration (t = -2.698, P < 0.05) between the two groups were both significant. In group B, the mean time interval between phacovitrectomy and IOL implantation (TV-I) was 25.2 ± 6.9 weeks, and the absolute values of both IOL tilt and decentration were positively correlated with TV-I. In phacovitrectomy for macula-off RRD repair, primary IOL implantation leads to a postoperative myopic shift, while delayed IOL implantation may cause greater IOL tilt and decentration.