Absolute Prediction Error Research Articles

Meridional analysis for calculation of the toric power of phakic IOLs.

To present a reproducible method to calculate the toricity needed at the intraocular lens (IOL) plane with toric phakic IOLs (ICL, Staar Surgical) and compare its results with those obtained with the online calculator provided by the manufacturer. Retrospective case series. Private practice, Buenos Aires, Argentina. The formula originally described by Holladay to calculate the IOL power in phakic eyes was used to calculate the required spherical power along the less refractive meridian and along the more refractive meridian. Meridional analysis was applied to calculate the required toricity at the IOL plane and the surgically induced corneal astigmatism was incorporated into the calculations. The refractive cylinder predicted by this method and by the online calculator of the manufacturer were compared to the postoperative refractive cylinder by means of vector analysis. The possible changes in the ratio of toricity in patients with different amounts of astigmatism and anterior chamber depth are assessed in a theoretical section. In 35 eyes, the measured mean postoperative refractive cylinder was 0.09 D @ 99°, the mean predicted postoperative refractive astigmatism was 0.04 D @ 102° according to the manufacturer's online calculator and 0.09 D @100° according to our method. With both methods, 91.43% of eyes had an absolute cylinder prediction error within ±0.50 diopters. The method described in this article to calculate the toricity of phakic IOLs has a refractive accuracy similar to that of the original calculator developed by the manufacturer.

Prediction method of stalk content in strip particles after redrying of tobacco leaves

AbstractFour tobacco leaf modules processed by Yunnan Tobacco Redrying Co., Ltd. during the 2022 roasting season were used to investigate the method of stalk content in strip particles after redrying of tobacco leaves, effectively reducing the loss of strip particles. A total of 151 sets of experimental data were used to construct the prediction model for the stalk content in strip particles after redrying using the BP artificial neural network method, the linear regression method, and the support vector machine method. The results show that the prediction model constructed by the BP artificial neural network method has high accuracy and stability, with a relatively small absolute error of prediction (e = 0.0195%) and the root-mean-square error of interactive verification (RMSECV = 0.0227%), as well as a relatively small mean absolute error of production data validation (e = 0.0675%), while the prediction deviation ratio (RPD = 2.2435) is relatively large. Overall, the prediction model established by BP artificial neural network could provide new insight into the non-destructive detection of stalk content in strip particles of redried tobacco leaves after threshing and redrying and potentially leading to a reduction in tobacco leaf crushing by more than 112,500 kg per year.

Early remaining-useful-life prediction applying discrete wavelet transform combined with improved semi-empirical model for high-fidelity in battery energy storage system

The recycling of lithium-ion batteries (LIBs) from electric vehicles (EVs) for augmenting the capacity of battery energy storage systems (BESS) presents a sustainable approach to leverage investment in LIBs, mitigating economic losses. This study highlights the critical role of accurate capacity estimation and remaining-useful-life (RUL) prediction in determining optimal replacement and augmentation schedules. We introduce a semi-empirical (SE) model that integrates experimental aging data with an adaptive method and an electrochemical model to capture the degradation trends of LIBs accurately. The adoption of the Moore-Penrose pseudoinverse (Pinv) method, suited for the SE model's multi-parameter, single-output nature, significantly improves capacity estimation accuracy, achieving a maximum error below 0.2 Ah. This improved SE (ISE) model accounts for capacity trends and addresses non-stationary phenomena, including noise and signal distortion during parameter identification and capacity recovery phases.To address these challenges, we propose using discrete wavelet transform (DWT) for the decomposition of non-stationary signals, adjusting the window size to signal characteristics for enhanced noise reduction. The application of DWT to denoise α and β parameters, characterized by their non-constant periodicity and noise interference, facilitates the development of DWT combined with the ISE (DWT-ISE) aging model for early RUL prediction. Experimental validation reveals that our DWT-based approach minimizes root-mean square error (RMSE) and mean absolute error (MAE) for early RUL prediction, demonstrating the model's superiority at 300 cycles for identification and achieving the lowest prediction error at 400 cycles. Furthermore, comparison results of other models (Particle filter, and long short-term memory) reveal that our DWT-ISE aging model exhibits the best RMSE and MAE on limited data. Specifically, the RMSE and MAE values for RUL prediction, underscored as 0.032 % and 0.027 % respectively at 300 cycles, are confirmed as the highest evaluation metrics.

A method for predicting vibration characteristics of track structure based on rail acceleration and deep learning

ABSTRACT The dynamic test on track structure plays a crucial role in evaluating the operational state. Nonetheless, existing testing approaches involve numerous sensors, resulting in high costs and time consumption. This study proposes an economical, high-precision, and swift solution to predict vibration responses and displacement variations in track structures using rail acceleration. Taking the ordinary track structure and the damping track structure as examples, based on the high correlation between the vibration responses of the rail, track slab and tunnel wall, combined with the proposed Bayesian optimized temporal convolutional neural network model (BOA-TCN), and taking the rail acceleration envelope and vibration level as input, the vibration of tunnel wall and the displacement of rail and track slab are estimated. The results demonstrate that the BOA-TCN model effectively solves the limitation of traditional neural network in accurately capturing vibration characteristics in the frequency range of 1 ~ 100 Hz. The absolute error of the total vibration level prediction is significantly reduced, only 0.74 dB. The single point prediction accuracy of track structure displacement is increased by 80%, the absolute error is 50% of the traditional model, and R2 can be increased to more than 90%.

Exploring clozapine pharmacokinetics in Tunisian schizophrenic patients: A population-based modelling approach investigating the impact of genetic and non-genetic variables.

Clozapine is characterized by a large within- and between-patient variability in its pharmacokinetics, attributed to non-genetic and genetic factors. A cross-sectional analysis of clozapine trough concentration (Clz C0) issued from Tunisian schizophrenic patients was collected and analysed using a nonparametric modelling approach. We assessed the impact of demographic covariates (age, weight and sex), patient's habits (smoking status, alcohol and caffeine intake) and the genetic factors (CYP1A2*1C, CYP1A2*1F and CYP2C19*2 polymorphisms) on each pharmacokinetic parameter. An external validation of this pharmacokinetic model using an independent data set was performed. Fit goodness between observed- and individual-predicted data was evaluated using the mean prediction error (% MPE), the mean absolute prediction error (% MAPE) as a measure of bias, and the root mean squared error (% RMSE) as a measure of precision. Sixty-three CLz C0 values issued from 51 schizophrenic patients were assessed in this study and divided into building and validation groups. CYP1A2*1F polymorphism and smoking status were the only covariates significantly associated with clozapine clearance. Precision parameters were as follows: 1.02%, 0.95% and 22.4%, respectively, for % MPE, % MAPE and % RMSE. We developed and validated an accurate pharmacokinetic model able to predict Clz C0 in Tunisian schizophrenic patients using the two parameters CYP1A2*1F polymorphism and smoking.

Timing along the cardiac cycle modulates neural signals of reward-based learning

Natural fluctuations in cardiac activity modulate brain activity associated with sensory stimuli, as well as perceptual decisions about low magnitude, near-threshold stimuli. However, little is known about the relationship between fluctuations in heart activity and other internal representations. Here we investigate whether the cardiac cycle relates to learning-related internal representations – absolute and signed prediction errors. We combined machine learning techniques with electroencephalography with both simple, direct indices of task performance and computational model-derived indices of learning. Our results demonstrate that just as people are more sensitive to low magnitude, near-threshold sensory stimuli in certain cardiac phases, so are they more sensitive to low magnitude absolute prediction errors in the same cycles. However, this occurs even when the low magnitude prediction errors are associated with clearly suprathreshold sensory events. In addition, participants exhibiting stronger differences in their prediction error representations between cardiac cycles exhibited higher learning rates and greater task accuracy.

Predictive models for short-term load forecasting in the UK's electrical grid.

There are global efforts to deploy Electric Vehicles (EVs) because of the role they promise to play in energy transition. These efforts underscore the e-mobility paradigm, representing an interplay between renewable energy resources, smart technologies, and networked transportation. However, there are concerns that these initiatives could burden the electricity grid due to increased demand. Hence, the need for accurate short-term load forecasting is pivotal for the efficient planning, operation, and control of the grid and associated power systems. This study presents robust models for forecasting half-hourly and hourly loads in the UK's power system. The work leverages machine learning techniques such as Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Gaussian Process Regression (GPR) to develop robust prediction models using the net imports dataset from 2010 to 2020. The models were evaluated based on metrics like Root Mean Square Error (RMSE), Mean Absolute Prediction Error (MAPE), Mean Absolute Deviation (MAD), and the Correlation of Determination (R2). For half-hourly forecasts, SVR performed best with an R-value of 99.85%, followed closely by GPR and ANN. But, for hourly forecasts, ANN led with an R-value of 99.71%. The findings affirm the reliability and precision of machine learning methods in short-term load forecasting, particularly highlighting the superior accuracy of the SVR model for half-hourly forecasts and the ANN model for hourly forecasts.

Intraocular lens power calculation: angle κ and ocular biomechanics

Purpose: To study the effect of ocular biomechanics on the prediction error of intraocular lens (IOL) power calculation. Setting: Centro Hospitalar Universitário do Porto, Porto, Portugal. Design: Prospective longitudinal study. Methods: This study included 67 subjects. Before cataract surgery subjects underwent biometry with IOLMaster 700 and biomechanical analysis with Corvis Scheimpflug technology. The targeted spherical equivalent was calculated with SRK-T and Barrett Universal II. Associations between prediction error (PE), absolute prediction error (AE), and biometric and biomechanical parameters were performed with stepwise multivariate linear correlation analysis. Results: Using the SRKT formula, there was association between PE and Corvis Biomechanical Index (CBI, B = −0.531, P = .011) and between AE and the horizontal offset between the center of the pupil and the visual axis (angle κ, B = −0.274, P = .007). Considering the Barret Universal II formula, PE was independently associated with anterior chamber depth (B = −0.279, P = .021) and CBI (B = −0.520, P = .013) and AE was associated with angle κ (B = −0.370, P = .007). Conclusions: A large angle κ may reduce the predictability of IOL power calculation. Ocular biomechanics likely influence the refractive outcomes after IOL implantation. This study showed that eyes with softer corneal biomechanics had more myopic PE. This may relate to anteriorization of the effective lens position. Dynamic measurements may be the way to progress into future formulas.

Refractive outcomes using Barrett formulas and patient characteristics of cataract surgery patients with and without prior LASIK/PRK.

The goal of this study is to describe characteristics of cataract surgery patients who previously underwent laser in situ keratomileusis/photorefractive keratectomy (LASIK/PRK) in comparison to non-LASIK/PRK cataract surgery patients including psychiatric comorbidities, as well as describe refractive prediction error after cataract surgery while accounting for axial length (AL) using the Barrett True-K and Barrett Universal II formulas. This was a retrospective study of patients from the University of Colorado Cataract Outcomes Registry. The primary outcomes were refraction prediction error (RPE), mean absolute RPE, and median absolute RPE. Outcomes were stratified by five axial length groups. Univariate and multivariate models for RPE were stratified by the AL group. Two hundred eighty-one eyes with prior LASIK/PRK and 3101 eyes without are included in the study. Patients with prior LASIK/PRK were significantly younger: 67.0 vs 69.9years, p < 0.0001. The LASIK/PRK group had significantly better mean pre-operative BCVA in comparison to the non-LASIK group, logMAR 0.204 vs logMAR 0.288, p = 0.003. The LASIK/PRK group had significantly lower rates of cardiovascular disease (18.5% vs 29.3%, p < 0.001), hypertension (49.1% vs 59.3%, p < 0.012), and type 2 diabetes (10.7% vs 26.0%, p < 0.001), and no significant difference in psychiatric disease. The absolute RPE was higher for the LASIK group for all ALs, but only significantly higher for eyes with AL less than 25mm. Patient eyes with prior LASIK/PRK surgery undergoing cataract surgery were significantly younger, had significantly less comorbidities, and a significantly better pre-operative BCVA. Using the Barrett formulas, absolute prediction error for eyes with longer ALs was not significantly worse for LASIK/PRK eyes than those without and the difference was smaller for eyes with longer AL.

Predicting Social Inequality in Poland Using Price Dispersion on the Real Estate Market

Measuring and predicting price dispersion on the real estate market is an important issue for both investors and policymakers. Price dispersion in the housing market can be seen as an additional dimension for measuring social inequality and one of the main goals of public policies that focus on life satisfaction and the accumulation of permanent wealth. The article considers the Polish real estate market and proposes the prediction methods of four measures of dispersion. They are based on quantiles and allow overall measurement of dispersion. In the prediction of dispersion measures the plug-in predictors utylizing longitudinal mixed models are proposed. Furthermore, the ex ante prediction accuracy measure called the quantile of absolute prediction errors (QAPE) is assessed using the residual bootstrap estimators. QAPE allows for a comprehensive description of the distribution of prediction errors, thus fostering discussion of possible various market scenarios.

A sequence to sequence prediction model for remaining useful life of lithium-ion batteries with Bayesian optimisation process visualization

Accurate battery remaining useful life (RUL) prediction plays an important role in ensuring reliable operation of electric vehicles. In this paper, a hybrid model based on Bayesian optimization of deep convolutional neural network and long short-term memory neural network (BO-DCNN-LSTM) is proposed for battery RUL prediction. Feature extraction of raw charging characteristic curves is performed by the multilayer CNN and preliminary capacity prediction is performed by the multilayer LSTM. The model performance is explored with different training, validation and testing strategies and different prediction starting points. Validation using NASA battery aging data shows that the mean absolute error (MAE) and root mean square error (RMSE) of the RUL prediction are 0.0139 Ah and 0.0195 Ah, respectively, when the prediction starting point is the 50th cycle. In addition, this paper visualizes the process of how the Bayesian optimization (BO) algorithm searches for the global optimal combinations in the high-dimensional hyperparameter space and discusses the impact of these hyperparameters on the prediction, filling the gap in this part of the research.

Advancing Slim-Hole Drilling Accuracy: A C-I-WOA-CNN Approach for Temperature-Compensated Pressure Measurements.

This paper presents a novel method to improve drill pressure measurement accuracy in slim-hole drilling within the petroleum industry, a sector often plagued by extreme conditions that compromise data integrity. We introduce a temperature compensation model based on a Chaotic-Initiated Adaptive Whale Optimization Algorithm (C-I-WOA) for optimizing Convolutional Neural Networks (CNNs), dubbed the C-I-WOA-CNN model. This approach enhances the Whale Optimization Algorithm (WOA) initialization through chaotic mapping, boosts the population diversity, and features an adaptive weight recalibration mechanism for an improved global search and local optimization. Our results reveal that the C-I-WOA-CNN model significantly outperforms traditional CNNs in its convergence speed, global searching, and local exploitation capabilities, reducing the average absolute percentage error in pressure parameter predictions from 1.9089% to 0.86504%, thereby providing a dependable solution for correcting temperature-induced measurement errors in downhole settings.

Retrospective assessment of accuracy of nine intraocular lens power calculation formulae in eyes with axial myopia.

To compare the accuracy of nine conventional and newer-generation formulae in calculating intraocular lens power in eyes with axial myopia. Tertiary eye care center, Bengaluru, India. Retrospective cross-sectional, comparative study conducted in India. Patients undergoing uneventful phacoemulsification in eyes with axial length >26 mm were included. Preoperative biometry was done using Lenstar LS 900 (Haag-Streit AG, Switzerland). Single eye of patients undergoing bilateral implantation was randomly selected. Optimized lens constants were used to calculate the predicted postoperative refraction of each formula, which was then compared with the actual refractive outcomes to give the prediction errors, following which subgroup analysis was performed. The Kane formula, Barrett universal II, Emmetropia Verifying Optical (EVO) 2.0, Hill Radial Basis Function (Hill RBF) 3.0, Olsen formula, along with Wang Koch-adjusted four formulae, that is, Sanders Retzlaff Kraff/Theoretical (SRK/T), Holladay 1, Haigis, and Hoffer Q formula, were compared for intraocular lens power calculations. One hundred and sixty-five eyes that fulfilled all the inclusion criteria were studied. Hill RBF 3.0 had the lowest mean and median absolute prediction errors (0.355 and 0.275, respectively) compared to all formulas. In subgroup analysis (26-28, >28-30, and >30 mm), significant difference was seen only in extremely long eyes (>30 mm). The Hill RBF 3.0 formula generated the maximum percentage of eyes with refractive errors within ±0.25, ±0.5, ±0.75, and ±1 D (46%, 76.2%, 89.9%, and 95.8%, respectively). This is the first study evaluating all the formulas exclusively in the myopic eyes. Hill RBF 3 was found to be superior in accuracy to all other formulas.

Health prediction of lithium-ion batteries by combining with empirical mode decomposition and PF-GPR algorithm

A new model based on empirical mode decomposition (EMD) and particle filter-gaussian progress regression (PF-GPR) algorithm is developed for estimating state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries (LIBs). The capacity degradation process of LIBs is investigated and reveals that the new model is convenient and accurate for estimating SOH and RUL of LIBs. The relative error of SOH prediction is less than 1.5%, while the maximum absolute error of RUL prediction is one cycle. Compared with other models reported in previous works, the present model shows smaller absolute error and root mean square error. In addition, the EMD-PF-GPR fusion model based on combined kernel function and mahalanobis distance possesses a good generalization ability and ability of learning from local variations. The work provides a new method to accurately predict health of LIBs.

The effect of corneal power on the accuracy of 14 IOL power formulas

BackgroundThis study evaluates the impact of corneal power on the accuracy of 14 newer intraocular lens (IOL) calculation formulas in cataract surgery. The aim is to assess how these formulas perform across different corneal curvature ranges, thereby guiding more precise IOL selection.MethodsIn this retrospective case series, 336 eyes from 336 patients who underwent cataract surgery were studied. The cohort was divided into three groups according to preoperative corneal power. Key metrics analyzed included mean prediction error (PE), standard deviation of PE (SD), mean absolute prediction error (MAE), median absolute error (MedAE), and the percentage of eyes with PE within ± 0.25 D, 0.50 D, ± 0.75 D, ± 1.00 D and ± 2.00 D.ResultsIn the flat K group (Km < 43 D), VRF-G, Emmetropia Verifying Optical Version 2.0 (EVO2.0), Kane, and Hoffer QST demonstrated lower SDs (± 0.373D, ± 0.379D, ± 0.380D, ± 0.418D, respectively) compared to the VRF formula (all P < 0.05). EVO2.0 and K6 showed significantly different SDs compared to Barrett Universal II (BUII) (all P < 0.02). In the medium K group (43 D ≤ Km < 46 D), VRF-G, BUII, Karmona, K6, EVO2.0, Kane, and Pearl-DGS recorded lower MAEs (0.307D to 0.320D) than Olsen (OLCR) and Castrop (all P < 0.03), with RBF3.0 having the second lowest MAE (0.309D), significantly lower than VRF and Olsen (OLCR) (all P < 0.05). In the steep K group (Km ≥ 46D), RBF3.0, K6, and Kane achieved significantly lower MAEs (0.279D, 0.290D, 0.291D, respectively) than Castrop (all P < 0.001).ConclusionsThe study highlights the varying accuracy of newer IOL formulas based on corneal power. VRF-G, EVO2.0, Kane, K6, and Hoffer QST are highly accurate for flat corneas, while VRF-G, RBF3.0, BUII, Karmona, K6, EVO2.0, Kane, and Pearl-DGS are recommended for medium K corneas. In steep corneas, RBF3.0, K6, and Kane show superior performance.

Development and validation of age-specific predictive equations for total energy expenditure and physical activity levels for older adults

BackgroundPredicting energy requirements for older adults is compromised by the underpinning data being extrapolated from younger adults. ObjectivesTo generate and validate new total energy expenditure (TEE) predictive equations specifically for older adults using readily available measures (age, weight, height) and to generate and test new physical activity level (PAL) values derived from 1) reference method of indirect calorimetry and 2) predictive equations in adults aged ≥65 y. MethodsTEE derived from “gold standard” methods from n = 1657 (n = 1019 females, age range 65–90 y), was used to generate PAL values. PAL ranged 1.28–2.05 for males and 1.26–2.06 for females. Physical activity (PA) coefficients were also estimated and categorized (inactive to very active) from population means. Nonlinear regression was used to develop prediction equations for estimating TEE. Double cross-validation in a randomized, sex-stratified, age-matched 50:50 split, and leave one out cross-validation were performed. Comparisons were made with existing equations. ResultsEquations predicting TEE using the Institute of Medicine method are as follows: For males, TEE = −5680.17 − 17.50 × age (years) + PA coefficient × (6.96 × weight [kilograms] + 44.21 × height [centimeters]) + 1.13 × resting metabolic rate (RMR) (kilojoule/day). For females, TEE = −5290.72 − 8.38 × age (years) + PA coefficient × (9.77 × weight [kilograms] + 41.51 × height [centimeters]) + 1.05 × RMR (kilojoule/day), where PA coefficient values range from 1 (inactive) to 1.51 (highly active) in males and 1 to 1.44 in females respectively. Predictive performance for TEE from anthropometric variables and population mean PA was moderate with limits of agreement approximately ±30%. This improved to ±20% if PA was adjusted for activity category (inactive, low active, active, and very active). Where RMR was included as a predictor variable, the performance improved further to ±10% with a median absolute prediction error of approximately 4%. ConclusionsThese new TEE prediction equations require only simple anthropometric data and are accurate and reproducible at a group level while performing better than existing equations. Substantial individual variability in PAL in older adults is the major source of variation when applied at an individual level.

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Transfer Learning and DAE-LSTM

Predicting the Remaining Useful Life (RUL) of lithium-ion batteries is one of the core tasks in battery health management. This study aims to enhance the accuracy of RUL prediction by proposing a battery RUL prediction method that integrates source domain battery iteration transfer with Long Short-Term Memory (LSTM) neural networks. Firstly, multiple batteries with known degradation trends are utilized as source domain batteries, and combined with full-lifetime capacity degradation data to construct the Source Domain Battery Iterations Module (SIM) to obtain the optimal LSTM pre-training model. Secondly, the pre-trained model is transferred to the target domain and fine-tuned using the target domain training set. Finally, the fine-tuned LSTM pre-training model is applied to the task of predicting the capacity of target domain batteries, thereby completing the RUL prediction. The effectiveness of the algorithm is validated on three open-source datasets. Experimental results demonstrate that in scenarios where the source domain and target domain battery types are the same, the absolute error of RUL prediction is less than 2 cycles. Moreover, in cases where the battery types differ, except for the 20% prediction starting point, the RUL prediction error is less than 10 cycles, indicating a high level of prediction accuracy.

Short-term prediction of UT1-UTC and LOD via Dynamic Mode Decomposition and combination of least-squares and vector autoregressive model

Abstract This study presents a short-term forecast of UT1-UTC and LOD using two methods, i.e. Dynamic Mode Decomposition (DMD) and combination of Least-Squares and Vector Autoregression (LS+VAR). The prediction experiments were performed separately for yearly time spans, 2018-2022. The prediction procedure started on January 1 and ended on December 31, with 7-day shifts between subsequent 30-day forecasts. Atmospheric Angular Momentum data (AAM) were used as an auxiliary time series to potentially improve the prediction accuracy of UT1-UTC and LOD in LS+VAR procedure. An experiment was also conducted with and without elimination of effect of zonal tides from UT1-UTC and LOD time series. Two approaches to using the best steering parameters for the methods were applied:. First, an adaptive approach, which observes the rule that before every single forecast, a preliminary one must be performed on the pre-selected sets of parameters, and the one with the smallest prediction error is then used for the final prediction; and second, an averaged approach, whereby several forecasts are made with different sets of parameters (the same parameters as in adaptive approach) and the final values are calculated as the averages of these predictions. Depending on the method and data combination mean absolute prediction errors (MAPE) for UT1-UTC vary from 0.63 ms to 1.43ms for the 10th day and from 3.07 ms to 8.05ms for the 30th day of the forecast. Corresponding values for LOD vary from 0.110 ms to 0.245 ms for the 10th day and from 0.148 ms to 0.325 ms for the 30th day.

The LISA-PPV Formula: An Ensemble Artificial Intelligence-Based Thick Intraocular Lens Calculation Formula for Vitrectomized Eyes

PurposeTo investigate the relationship between effective lens position (ELP) and patient characteristics, and to further develop a new intraocular lens (IOL) calculation formula for cataract patients with previous pars plana vitrectomy (PPV). DesignCross-sectional study. MethodsA total of 2793 age-related cataract patients (group 1) and 915 post-PPV cataract patients (group 2) who underwent phacoemulsification with IOL implantation were included. The ELP of two groups was compared and the association between ELP and patient characteristics was further evaluated using standardized multivariate regression coefficients. An ensemble artificial intelligence-based ELP prediction model was developed using a training set of 810 vitrectomized eyes, and a thick-lens IOL formula (LISA-PPV) was constructed and compared with seven existing formulas on an external multi-center testing set of 105 eyes. ResultsCompared to eyes with age-related cataract, vitrectomized eyes showed a similar ELP distribution (P = 0.19), but different standardized coefficients of preoperative biometry for ELP. The standardized coefficients also varied with the type of vitreous tamponade, history of scleral buckling, and ciliary sulcus IOL implantation. The LISA-PPV formula showed the lowest mean and median absolute prediction error (MAE: 0.63 D; MedAE: 0.44 D), and the highest percentages of eyes within ± 0.5 D of prediction error (57.14%) in the testing dataset. ConclusionsThe ELP prediction required optimization specifically for vitrectomized eyes based on their biometric and surgical characteristics. The LISA-PPV formula is a useful and accurate tool for determining IOL power in cataract patients with previous PPV (available at http://ppv-iolcalculator.com/).

Predictive performance of population pharmacokinetic models of imatinib in chronic myeloid leukemia patients

Background and aimChronic myeloid leukemia is a myeloproliferative neoplasm associated with the specific chromosomal translocation known as the Philadelphia chromosome. Imatinib is a potent BCR-ABL tyrosine kinase inhibitor, which is approved as the first line therapy for CML patients. There are various population pharmacokinetic studies available in the literature for this population. However, their use in other populations outside of their cohort for the model development has not been evaluated. This study was aimed to perform the predictive performance of the published population pharmacokinetic models for imatinib in CML population and propose a dosing nomogram.MethodsA systematic review was conducted through PubMed, and WoS databases to identify PopPK models. Clinical data collected in adult CML patients treated with imatinib was used for evaluation of these models. Various prediction-based metrics were used for assessing the bias and precision of PopPK models using individual predictions.ResultsEight imatinib PopPK model were selected for evaluating the model performance. A total of 145 plasma imatinib samples were collected from 43 adult patients diagnosed with CML and treated with imatinib. The PopPK model reported by Menon et al. had better performance than all other PopPK models.ConclusionMenon et al. model was able to predict well for our clinical data where it had the relative mean prediction error percentage ≤ 20%, relative median absolute prediction error ≤ 30% and relative root mean square error close to zero. Based on this final model, we proposed a dosing nomogram for various weight groups, which could potentially help to maintain the trough concentrations in the therapeutic range.