Absolute Prediction Error Research Articles

Accuracy of refractive outcomes using standard or total keratometry for intraocular lens power formulas in conventional cataract surgery

PurposeTo evaluate if total keratometry (TK) is better than standard keratometry (K) for predicting an accurate intraocular lens (IOL) refractive outcome in virgin eyes using four IOL power calculation formulas.Methods447 eyes that underwent monofocal intraocular lens implantation were enrolled in this study. IOLMaster 700 (Carl Zeiss Meditech, Jena, Germany) was used for optical biometry. Prediction error (PE), mean absolute prediction error (MAE), median absolute prediction error (MedAE), proportions of eyes within ± 0.25 diopters (D), ± 0.50 D, ± 0.75 D, ± 1.00 D, ± 2.00 D prediction error, and formula performance index (FPI) were calculated for each K- and TK-based formula.ResultsOverall, the accuracy of each TK and K formula was comparable. The MAEs and MedAEs showed no difference between most of the K-based and the TK-based formula; only the MAE of TK was significantly higher than that of K using the Haigis. The percent of eyes within ± 0.25 D PE for TK was not significantly different from that for K. The analysis of PE across various optical dimensions revealed that TK had no effect on the refractive results in eyes with different preoperative axial length, anterior chamber depth, keratometry, and lens thickness. The K-based Barrett Universal II formula performed excellently, showed the leading FPI score, and had the best refractive prediction outcomes among the four formulas.ConclusionTK and K can be used for IOL power calculation in monofocal IOL implantation cataract surgery in virgin eyes, as both are comparable. In all investigated formulas, the predictive accuracy of TK-based formulas is not superior to that of standard K-based formulas.

Developing an integrated mathematical model of fermentation-pervaporation system for bioethanol production

This study presents a novel integrated model to describe the fermentation-pervaporation system for bioethanol recovery. The proposed model incorporates the effects of by-product inhibition on the fermentation process and variation in feed concentration during the pervaporation process. The model is based on a modified Monod model to describe the fermentation process and the solution diffusion model for the pervaporation process, which can predict the concentrations of biomass, glucose, and ethanol in the bioreactor, as well as the partial flux of ethanol and water, total flux, the volume of permeate, and concentration of ethanol permeate. Simulation results showed that the proposed modified Monod model outperforms the conventional Monod model in predicting biomass, glucose, and ethanol concentrations due to its ability to consider the inhibitory effect of by-products with low mean absolute prediction error values of 4.1%, 12.6 and 5.2%. Moreover, the inclusion of the byproduct inhibitory effect in the fermentation model improved the accuracy of the integrated model in predicting the bioethanol permeation flux by 7.9%. The proposed integrated model provides a useful tool for designing and optimizing fermentation-pervaporation systems for bioethanol recovery to promote the adoption of continuous fermentation at the industrial scale.

Randomized Trial Comparing Prediction Accuracy of Two Swept Source Optical Coherence Tomography Biometers.

To compare the prediction accuracy of the Argos biometer using standard keratometry to the prediction accuracy of the IOLMaster 700 biometer using Total Keratometry. This was a randomized, prospective, single surgeon study of 80 right eyes of 80 patients that had preoperative biometry with both the Argos and IOLMaster 700 devices, followed by cataract surgery and intraocular lens (IOL) implantation. Prediction errors (directional and absolute) for each device were determined from the 1 month postoperative manifest refraction. The directional prediction error was 0.07 ± 0.32 D for the Argos and 0.08 ± 0.34 D for the IOLMaster 700. The mean of the difference in prediction error (directional) was 0.02 D, which was not statistically significant (p > 0.05). The absolute prediction error was 0.21 ± 0.25 D for the Argos and 0.25 ± 0.24 D for the IOLMaster 700. The mean of the difference in absolute prediction error was 0.04 D, which was statistically significant (p < 0.004) but not clinically significant. The percentage of eyes with absolute prediction error ≤ 0.5 D was 91% (73 eyes) for the Argos and 88% (70 eyes) for the IOLMaster 700. This difference was not statistically significant. The prediction accuracies were similar between the Argos and IOLMaster 700 in eyes with normal axial length. There was a significant difference in mean absolute prediction error between devices; however, this was not clinically meaningful.

Influence and Predictive Value of Optional Parameters in New-generation IOL Formulae.

To evaluate the accuracy of various variations of new-generation multivariate intraocular lens (IOL) power calculation using the Barrett Universal II, Castrop, EVO 2.0, Hill-RBF 3.0, Kane, and PEARL-DGS formulae with and without optional biometric parameters. Tertiary care academic medical center. retrospective case series. ology: Single-center study. Inclusion of patients after uneventful cataract surgery implanting AU00T0 IOLs. Data from one single eye per patient were randomly included. Eyes with a best-corrected visual acuity worse than 0.1 logMAR were excluded. IOLCON-optimized constants were used for all formulae other than the Castrop formula. The outcome measures were prediction error (PE) and absolute prediction error (absPE) for the 6 study formulae. 251 eyes from 251 patients were assessed. Excluding lens thickness (LT) led to statistically significant differences in absPE in several formulae. Leaving out horizontal corneal diameter impacted absPE in several formulae . Differences in PE offset were observed between the various formula variations. When using multivariable formulae with an A-constant, including certain optional parameters is vital for optimal refractive results. Formula variations excluding certain biometric parameters need specifically optimized constants and do not perform similarly when using the constant of the respective formula using all parameters.

A data-driven process-quality-property analytical framework for conductive composites in additive manufacturing

Developing conductive composites via additive manufacturing (AM) enables unparalleled flexibility in material composition and product properties. However, it is challenging due to strong interactions between product properties and process parameters. This work aims to investigate and develop a data-driven process-quality-property (PQP) framework to explore the PQP relationships for conductive composites by statistical analysis with the fused filament fabrication (FFF) AM process. Three machine learning (ML) methods are used in the framework to model the PQP relationships. Two conductive composites, copper-based material (Electrifi) and carbon-based material (Protopasta), are investigated. The results show strong correlations among the process parameters, surface quality, and resistance. Among the three ML methods, the artificial neural network achieves the lowest mean absolute percentage error (MAPE) in predictions of resistance for Electrifi (MAPE = 0.1487) and Protopasta (MAPE = 0.1119), respectively. Compared with the trial-and-error method, the PQP framework demonstrates the potential to reduce the time and cost of property prediction and help practitioners to understand AM composites. This framework could contribute to applications and innovations of AM conductive composites.

Predictability assessment of as-built hardness of Ti-6Al-4V alloy fabricated via laser powder bed fusion

Evaluating the predictability of selective laser melting (SLM) process has been a persistent endeavor in the manufacturing community since the technique’s inception. The ability to accurately predict the as-built hardness of fabricated parts is critical to judge its serviceability for specific applications. This investigation aims at achieving such predictability with as little as experimentation possible. In this research, reliable historical SLM data was mined on as-built Ti-6Al-4V alloy, from technical articles published over the past fifteen years. Three predictive models of Gaussian Process Regression (GPR), Neural Network (NN) and parametric Multiple Linear Regression (MLR) were built for as-fabricated bulk hardness of Ti-6Al-4V alloy manufactured via SLM. Ten-fold cross-validation training of the models involved a substantial 1,284 spatial datapoints of major SLM process parameters such as laser power, scanning speed, hatch spacing, layer thickness and volumetric energy density. Ultimate assessment of hardness predictability was performed by making predictions on ten cubic Ti-6Al-4V test coupons fabricated using a SLM machine. The GPR model stood out amongst other models, yielding a mean absolute prediction error of 6.12 HV over all samples. The overall predictability of these models came out to be in declining order of GPR > NN > MLR. Rudimentary characterization of test coupons revealed variation in observed hardness. Two tiers of robust validations, comprehensive dataset, statistical insights, actual experimental validation and material characterization make this study extremely unique for as-fabricated hardness of SLM-ed Ti-6Al-4V alloy.

Comparing the Effectiveness of Smartphone Applications in the Measurement of Interpupillary Distance.

Purpose To determine the accuracy of three smartphone applications in the measurement of interpupillary distance (IPD). Methods This study compared measurements from three smartphone applications to measurements obtained by a single trained examiner using a digital pupilometer in 44 subjects. The mean absolute error (MAE) of IPD prediction by each application was compared. Additionally, the frequency at which each application measured IPD within ± 0.05 mm, ± 0.10 mm, ± 0.25 mm, ± 0.50 mm, ± 0.75 mm, and ± 1.00 mm of the digital pupilometer measurement was determined. Results The Eye Measure (Dotty Digital, Sydney, New South Wales, Australia) and Warby Parker (Warby Parker, New York, New York) applications had significantly lower MAE of IPD measurements (0.511364 mm) compared to the PDCheck AR (EyeQue Corp., Newark, California) application (1.375 mm). The Warby Parker application most frequently obtained accurate IPD measurements within the following ranges: ± 0.05 mm, ± 0.10 mm, ± 0.25 mm, ± 0.50 mm, ± 0.75 mm, and ± 1.00 mm. Conclusion Of the three smartphone applications compared in this study, the Warby Parker application performed to the highest degree of accuracy and may serve as an adequate alternative when conventional IPD measurement methods are either unavailable or unable to be performed accurately.

Evaluation of Bayesian spatiotemporal infectious disease models for prospective surveillance analysis

BackgroundCOVID-19 brought enormous challenges to public health surveillance and underscored the importance of developing and maintaining robust systems for accurate surveillance. As public health data collection efforts expand, there is a critical need for infectious disease modeling researchers to continue to develop prospective surveillance metrics and statistical models to accommodate the modeling of large disease counts and variability. This paper evaluated different likelihoods for the disease count model and various spatiotemporal mean models for prospective surveillance.MethodsWe evaluated Bayesian spatiotemporal models, which are the foundation for model-based infectious disease surveillance metrics. Bayesian spatiotemporal mean models based on the Poisson and the negative binomial likelihoods were evaluated with the different lengths of past data usage. We compared their goodness of fit and short-term prediction performance with both simulated epidemic data and real data from the COVID-19 pandemic.ResultsThe simulation results show that the negative binomial likelihood-based models show better goodness of fit results than Poisson likelihood-based models as deemed by smaller deviance information criteria (DIC) values. However, Poisson models yield smaller mean square error (MSE) and mean absolute one-step prediction error (MAOSPE) results when we use a shorter length of the past data such as 7 and 3 time periods. Real COVID-19 data analysis of New Jersey and South Carolina shows similar results for the goodness of fit and short-term prediction results. Negative binomial-based mean models showed better performance when we used the past data of 52 time periods. Poisson-based mean models showed comparable goodness of fit performance and smaller MSE and MAOSPE results when we used the past data of 7 and 3 time periods.ConclusionWe evaluate these models and provide future infectious disease outbreak modeling guidelines for Bayesian spatiotemporal analysis. Our choice of the likelihood and spatiotemporal mean models was influenced by both historical data length and variability. With a longer length of past data usage and more over-dispersed data, the negative binomial likelihood shows a better model fit than the Poisson likelihood. However, as we use a shorter length of the past data for our surveillance analysis, the difference between the Poisson and the negative binomial models becomes smaller. In this case, the Poisson likelihood shows robust posterior mean estimate and short-term prediction results.

Stability of clinical prediction models developed using statistical or machine learning methods.

Clinical prediction models estimate an individual's risk of a particular health outcome. A developed model is a consequence of the development dataset and model-building strategy, including the sample size, number of predictors, and analysis method (e.g., regression or machine learning). We raise the concern that many models are developed using small datasets that lead to instability in the model and its predictions (estimated risks). We define four levels of model stability in estimated risks moving from the overall mean to the individual level. Through simulation and case studies of statistical and machine learning approaches, we show instability in a model's estimated risks is often considerable, and ultimately manifests itself as miscalibration of predictions in new data. Therefore, we recommend researchers always examine instability at the model development stage and propose instability plots and measures to do so. This entails repeating the model-building steps (those used to develop the original prediction model) in each of multiple (e.g., 1000) bootstrap samples, to produce multiple bootstrap models, and deriving (i) a prediction instability plot of bootstrap model versus original model predictions; (ii) the mean absolute prediction error (mean absolute difference between individuals' original and bootstrap model predictions), and (iii) calibration, classification, and decision curve instability plots of bootstrap models applied in the original sample. A case study illustrates how these instability assessments help reassure (or not) whether model predictions are likely to be reliable (or not), while informing a model's critical appraisal (risk of bias rating), fairness, and further validation requirements.

Model and prediction relationship of sound velocity and porosity of seafloor sediments

The relationship between sound velocity and porosity in seafloor sediments is widely utilized for predicting or inverting sediment acoustical and physical parameters. This paper analyzes the theoretical relationship between sound velocity and porosity using the General Model of Sound Speed (GMSS). The GMSS model elucidates an empirical equation relationship in the form of a quadratic fitted polynomial based on actual measurement data from the South China Sea. The accuracy of the GMSS model and the regression empirical equation are found to be consistent, as demonstrated by the relative error, absolute error, and standard deviation of the sound velocity predictions. Our findings suggest that if the scattering of measured data is inherent to seafloor sediment, the empirical equation fails to adequately explain sediment variations and lacks broad applicability for accurate prediction and calculation. Additionally, we observe that a ± 5% relative error in porosity has minimal impact on the application of the GMSS model and empirical equations for predicting sound velocity errors. However, the empirical equation, relying solely on the porosity parameter, inadequately accounts for sound velocity scattering in seafloor sediments with the same porosity due to inherent limitations. In contrast, the GMSS model can incorporate additional physical parameters beyond porosity to better explain and predict sound velocity in seafloor sediments scattering in seafloor sediments with the same porosity, albeit with increased complexity. This paper provides an analytical method and theoretical foundation for inverting or predicting the relationship between sound velocity and porosity of seafloor sediments.

Towards model-informed precision dosing of piperacillin: multicenter systematic external evaluation of pharmacokinetic models in critically ill adults with a focus on Bayesian forecasting

PurposeInadequate piperacillin (PIP) exposure in intensive care unit (ICU) patients threatens therapeutic success. Model-informed precision dosing (MIPD) might be promising to individualize dosing; however, the transferability of published models to external populations is uncertain. This study aimed to externally evaluate the available PIP population pharmacokinetic (PopPK) models.MethodsA multicenter dataset of 561 ICU patients (11 centers/3654 concentrations) was used for the evaluation of 24 identified models. Model performance was investigated for a priori (A) predictions, i.e., considering dosing records and patient characteristics only, and for Bayesian forecasting, i.e., additionally including the first (B1) or first and second (B2) therapeutic drug monitoring (TDM) samples per patient. Median relative prediction error (MPE) [%] and median absolute relative prediction error (MAPE) [%] were calculated to quantify accuracy and precision.ResultsThe evaluation revealed a large inter-model variability (A: MPE − 135.6–78.3% and MAPE 35.7–135.6%). Integration of TDM data improved all model predictions (B1/B2 relative improvement vs. A: |MPE|median_all_models 45.1/67.5%; MAPEmedian_all_models 29/39%). The model by Kim et al. was identified to be most appropriate for the total dataset (A/B1/B2: MPE − 9.8/− 5.9/− 0.9%; MAPE 37/27.3/23.7%), Udy et al. performed best in patients receiving intermittent infusion, and Klastrup et al. best predicted patients receiving continuous infusion. Additional evaluations stratified by sex and renal replacement therapy revealed further promising models.ConclusionThe predictive performance of published PIP models in ICU patients varied considerably, highlighting the relevance of appropriate model selection for MIPD. Our differentiated external evaluation identified specific models suitable for clinical use, especially in combination with TDM.

Topology Optimization of Wing Ribs for Additive Manufacturing

This paper describes the design of a lightweight wing rib structure by combining topology optimisation with additive manufacturing. In addition, a deep feed-forward neural network model is proposed to perform the load prediction for the constructed wing structure incorporated with this optimised rib. The strain energy of the front rib has a minimum strain energy 1330 J for the initial volume state when the topology is not optimised for lightweighting. The relative error of the load prediction values obtained by the output layer of the deep feed-forward neural network is less than 0.02%. The absolute error of the small load prediction was less than 0.30 N. The presented results demonstrate the viability of additive manufactured rib and its implementation in global wing load prediction model for faster designs.

Real-time temperature distribution reconstruction via linear parameter-varying state-space model and Kalman filter in rack-based cooling data centers

Rapidly growing data centers (DCs) are facing challenges with energy consumption increase and thermal environmental security. Thus, this paper proposed a novel real-time temperature distribution prediction and reconstruction model for rack-based cooling DCs, in order to facilitate efficient energy consumption and thermal environment management. The parameter identification method coupling with inherent physical relationships was employed to obtain the linear parameter-varying state-space model, rapidly and approximately predicting the nonlinear thermal dynamic processes within rack-based cooling DC. Then, Kalman filter was adopted to reconstruct the temperature distribution in real-time based upon the developed linear parameter-varying state-space temperature prediction model and measurement information. This study comprehensively analyzed the temperature estimation performance of the proposed model under various model input biases, different sensor numbers and layouts. The results show that the input biases have significant impacts on the accuracy of the temperature prediction model, e.g., ±5% bias in IT workload resulting in an increase of 0.6 times in the mean absolute error (MAE) of the temperature prediction. The proposed temperature reconstruction model reveals excellent performance: compared to the original temperature prediction model, the MAE was decreased by about 5%, 10%, and 13% for reconstruction scenarios using one, two, and three sensors, respectively.

Предсказание временных рядов солнечной активности с помощью искусственной нейронной сети LSTM

A numerical model for predicting the parameters of solar activity — the number of sunspots R and the radioflux at a wave of 10.7 cm F10.7 ahead for 27 days — has been built. The numerical model uses an artificial neural network (NN) with LSTM (Long short-term memory) layers. For both the number of sunspots and the radioflux, the model predicts the levels and limits of variation of the values for 27 days. The average absolute prediction error of the model is less than 6 %. The real-time model is implemented on the site http://aurora.pgia.ru and can be an addition to long-term forecasts of other INTERNET resources.

Comparing Predictive Accuracy of a Swept Source Optical Coherence Tomography Biometer and an Optical Low Coherence Reflectometry Biometer.

To compare the refractive accuracy resulting from calculations based on measurements with a swept-source optical coherence tomography (SS-OCT) biometer compared to calculations based on measurements with an optical low coherence reflectometry (OLCR) biometer at one month postoperatively. This was a retrospective comparative non-interventional study of preoperative biometry and postoperative refraction and visual acuity of 200 eyes. All eyes had preoperative biometry with both the Argos (Movu, a Santec company) and Lenstar LS900 (Haag-Streit AG) devices. Data were collected for mean postoperative prediction error (directional and absolute), preoperative mean K, delta K (corneal astigmatism), axial length, and anterior chamber depth. The mean directional prediction error was -0.15 ± 0.47 D for Argos and -0.31 ± 0.50 D for Lenstar LS900, and there was a statistically significant mean of the differences (0.16 ± 0.24 D; p < 0.001). The mean absolute prediction error was 0.35 ± 0.34 D for Argos and 0.42 ± 0.41 D for Lenstar LS900, and there was a statistically significant mean of the differences (-0.07 ± 0.24 D; p < 0.001). Neither the differences in directional prediction error nor the differences in absolute prediction error were clinically significant. The directional and absolute prediction accuracies were statistically significant, but not clinically different between the Argos and Lenstar LS900 devices. In addition, differences between preoperative K, AL, and ACD measurements were not clinically significant.

The Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program Bariatric Surgical Risk/Benefit Calculator: 1-year weight.

Data-driven tools can be designed to provide patient-personalized estimates of health outcomes. Clinical calculators are commonly built to assess risk, but potential benefits of treatment should be equally considered. The American College of Surgeons Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP) sought to create a risk and benefit calculator for adult patients considering primary metabolic and bariatric surgery with multiple prediction features: (1) 30-day risk, (2) 1-year body mass index (BMI) projections, and (3) 1-year co-morbidity remission. To assess the performance of the 1-year BMI projections feature of this tool. Not-for-profit organization, clinical data registry. MBSAQIP data from 596,024 cases across 4.5 years from 882 centers with ∼2.5 million records through 18 months postoperatively were included. A generalized estimating equation model was used to estimate BMI over time for 4 primary procedures: laparoscopic adjustable gastric band, laparoscopic sleeve gastrectomy, laparoscopic Roux-en-Y gastric bypass, and biliopancreatic diversion with duodenal switch. The mean absolute error (MAE) in BMI predictions through postoperative month 12 was 1.68 units; overall correlation of actual and predicted BMI was .94. MAE of postoperative BMI estimates (1-12 mo) was lowest for laparoscopic sleeve gastrectomy (1.64) and highest for biliopancreatic diversion with duodenal switch (1.99). BMI predictions at 12 months showed MAE = 2.99 units. Predicted BMI closely aligned with actual BMI values across the 12-month postoperative period. The MBSAQIP Bariatric Surgical Risk/Benefit Calculator is publicly available with the intent to facilitate patient-clinician communication and guide surgical decision making. This tool can aid in evaluating postoperative risk as well as benefits and long-term expectations.

Hardware Design and Implementation of Multiagent MLP Regression for the Estimation of Gunshot Direction on IoBT Edge Gateway

The advancements in the internet of things, artificial intelligence, and state-of-the-art computing techniques are the main pillars of the next generation defense technology. The internet of battlefield things (IoBT) with edge intelligence offers new opportunities for defense professionals for smart and effective military operations. The IoBT network connects soldiers by placing smart sensors on armors, weapons, body, and surroundings. This paper presents a novel edge intelligence based estimation of gunshot direction from the sensor-enabled glove for smart IoBT wearables. A multi-agent multi-layer perceptron (MA-MLP) and other regression models are developed and tested on the experimental dataset collected during this study. The MA-MLP model consists of two distinct MLP networks and a fusion block to estimate the gunshot direction. This paper demonstrates the effect of multiple subjects, sensor positions, and gun material on mean absolute error (MAE) of MA-MLP prediction. The software simulation results show that our proposed MA-MLP model has outperformed traditional machine learning techniques like linear regression, SVM and MLP with an MAE of 4.09°. Two different hardware designs i.e., intellectual property (IP) cores of the pre-trained MA-MLP model are implemented and tested on a field-programmable gate array (FPGA) for a system on a chip (SoC) based edge gateway. The first IP core requires 280 nanoseconds with a power consumption of 354 milliwatts while the second IP core requires 380 nanoseconds with 178 milliwatts power consumption per inference. Prediction accuracy of 97.48% with a reduction of throughput to 92.2% is achieved for both IP cores. This work is one of the first attempts to implement the FPGA-based edge intelligence for the IoBT wearables. The short computation time, low power consumption, small footprint, significant throughput reduction, desired accuracy, and processor offloading are achieved by both flexible hardware models designed explicitly for the edge intelligence.

Comparison of the Barrett toric calculator using measured and predicted posterior corneal astigmatism and the Kane and Abulafia-Koch calculators.

To compare the accuracy of the Barrett toric calculator with measured and predicted posterior corneal astigmatism (MPCA and PPCA, respectively), the Abulafia-Koch (AK) formula, and the toric Kane formula. Ein-Tal Eye Center, Tel-Aviv, Israel. Retrospective cohort. Consecutive cases of patients who underwent uneventful cataract extraction surgery with implantation of a toric intraocular lens between March 2015 and July 2019 were retrospectively reviewed. 1 eligible eye from each patient was included. The predicted postoperative refractive astigmatism was calculated using each method and compared with the postoperative refractive astigmatism to give the prediction error. 80 eyes of 80 patients were included in this study. The mean centroid and the mean and median absolute prediction errors using Kane (0.25 diopters [D] ± 0.54 @ 6 degrees, 0.50 D ± 0.31 and 0.45 D, respectively) were significantly different compared with MPCA (0.12 D ± 0.52 @ 16 degrees, P < .001, .44 D ± 0.28 and 0.36 D, P = .027, respectively), PPCA (0.09 D ± 0.49 @ 12 degrees, P < .001, .41 D ± 0.27 and 0.35 D, P < .001, respectively), and AK (0.11 D ± 0.49 @ 11 degrees, P < .001, .42 D ± 0.27 and 0.35 D, P = .004, respectively). No significant differences were found between the calculators in the predictability rates within ±0.25 D, ±0.50 D, ±0.75 D, and ±1.00 D. The measured posterior corneal curvature in the Barrett calculator yielded comparable outcomes to its prediction by the Barrett and AK formulas. The Kane calculator showed a slight against-the-rule prediction error compared with the other methods, resulting in a small higher median absolute error with marginal clinical importance.

Evaluation of Vancomycin Area Under the Concentration-Time Curve Predictive Performance Using Bayesian Modeling Software With and Without Peak Concentration: An Academic Hospital Experience for Adult Patients Without Renal Impairment.

The revised U.S. consensus guidelines on vancomycin therapeutic drug monitoring (TDM) recommend obtaining trough and peak samples to estimate the area under the concentration-time curve (AUC) using the Bayesian approach; however, the benefit of such two-point measurements has not been demonstrated in a clinical setting. We evaluated Bayesian predictive performance with and without peak concentration data using clinical TDM data. We retrospectively analyzed 54 adult patients without renal impairment who had two serial peak and trough concentration measurements in a ≤1-week interval. The concentration and AUC values were estimated and predicted using Bayesian software (MwPharm++; Mediware, Prague, Czech Republic). The median prediction error (MDPE) for bias and median absolute prediction error (MDAPE) for imprecision were calculated based on the estimated AUC and measured trough concentration. AUC predictions using the trough concentration had an MDPE of -1.6% and an MDAPE of 12.4%, whereas those using both peak and trough concentrations had an MDPE of -6.2% and an MDAPE of 16.9%. Trough concentration predictions using the trough concentration had an MDPE of -8.7% and an MDAPE of 18.0%, whereas those using peak and trough concentrations had an MDPE of -13.2% and an MDAPE of 21.0%. The usefulness of the peak concentration for predicting the AUC on the next occasion by Bayesian modeling was not demonstrated; therefore, the practical value of peak sampling for AUC-guided dosing can be questioned. As this study was conducted in a specific setting and generalization is limited, results should be interpreted cautiously.

The predictive accuracy of Barrett toric calculator using measured posterior corneal astigmatism derived from swept source-OCT and Scheimpflug camera.

To compare the performance of Barrett toric calculator incorporated with measured posterior corneal astigmatism (PCA) derived from IOL Master 700 and Pentacam HR versus predicted PCA. The predicted residual astigmatism using Barrett toric IOL calculator with predicted PCA, measured PCA from IOL Master 700 and measured PCA from Pentacam were calculated with the preoperative keratometry and intended IOL axis with modification. The vector analysis was performed to calculate the mean absolute prediction error (MAE), the centroid of the prediction error and the percentage of eyes with a prediction error within ±0.50 D, ±0.75 D, and ±1.00 D. In 57 eyes of 57 patients with mean age of 70.42 ± 10.75 years, the MAE among the three calculation methods were 0.59 ± 0.38 D (Predicted PCA), 0.60 ± 0.38 D (Measured PCA from IOL Master 700) and 0.60 ± 0.36 D (Measured PCA from Pentacam) with no significant difference, either in the whole sample, the WTR eyes and the ATR eyes (F = 0.078, 0.306 and 0.083, p = 0.925, 0.739 and 0.920, respectively). Measured PCA obtained from IOL Master 700 resulted in one level reduction (from Tn to Tn-1) in 49.12% eyes in cylindrical model selection, while measured PCA obtained from Pentacam resulted in one level reduction of toric model selection in 18.18% eyes. The present study suggested that the incorporation of measured PCA values derived from IOL Master 700 and Pentacam produce comparable clinical outcome with the predicted PCA mode in Barrett toric calculator.