scholarly journals External evaluation of population pharmacokinetic models for vancomycin in neonates

2018 ◽  
Author(s):  
Tõnis Tasa ◽  
Riste Kalamees ◽  
Jaak Vilo ◽  
Irja Lutsar ◽  
Tuuli Metsvaht
Keyword(s):  
Therapeutic Target ◽  
External Validation ◽  
Population Based ◽  
Variable Number ◽  
Validation Dataset ◽  
Population Pharmacokinetic ◽  
Model Based ◽  
Prediction Distribution ◽  
Error Metrics ◽  
Precision And Accuracy

AbstractIntroductionNumerous vancomycin population pharmacokinetic (PK) models of neonates have been published. We aimed to comparatively evaluate a set of these models by quantifying their model-based and Bayesian concentration prediction performances using an external retrospective dataset, and estimate their attainment rates in predefined therapeutic target ranges.MethodsImplementations of 12 published PK models were added in the Bayesian dose optimisation tool, DosOpt. Model based concentration predictions informed by variable number of individual concentrations were evaluated using multiple error metrics. A simulation study assessed the probabilities of target attainment (PTA) in trough concentration target ranges 10–15 mg/L and 10–20 mg/L.ResultsNormalized prediction distribution error analysis revealed external validation dataset discordances (global P < 0.05) with all population PK models. Inclusion of a single concentration improved both precision and accuracy. The model by Marques-Minana et al. (2010) attained 68% of predictions within 30% of true concentrations. Absolute percentage errors of most models were within 20-30%. Mean PTA with Zhao et al. (2013) was 40.4% [coefficient-of-variation (CV) 0.5%] and 62.9% (CV 0.4%) within 10–15 mg/L and 10–20 mg/L, respectively.ConclusionPredictive performances varied widely between models. Population based predictions were discordant with external validation dataset but Bayesian modelling with individual concentrations improved both precision and accuracy. Current vancomycin PK models achieve relatively low attainment of commonly recommended therapeutic target ranges.

scholarly journals External validation of the predictive performance of population pharmacokinetic models for phenobarbital in pediatric patients

2020 ◽  
Author(s):  
Sunae Ryu ◽  
Woo Jin Jung ◽  
Zheng Jiao ◽  
Jung Woo Chae ◽  
Hwi-yeol Yun
Keyword(s):  
External Validation ◽  
Predictive Performance ◽  
Validation Dataset ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Pharmacokinetic Models ◽  
Bayesian Forecasting ◽  
Predictive Capability ◽  
Young Infants ◽  
Simulation Based

Aim: Several studies have reported population pharmacokinetic models for phenobarbital (PB), but the predictive performance of these models has not been well documented. This study aims to do external validation of the predictive performance in published pharmacokinetic models. Methods: Therapeutic drug monitoring data collected in neonates and young infants treated with PB for seizure control, was used for external validation. A literature review was conducted through PubMed to identify population pharmacokinetic models. Prediction- and simulation-based diagnostics, and Bayesian forecasting were performed for external validation. The incorporation of size or maturity functions into the published models was also tested for prediction improvement. Results: A total of 79 serum concentrations from 28 subjects were included in the external validation dataset. Seven population pharmacokinetic studies of PB were selected for evaluation. The model by Voller et al. [27] showed the best performance concerning prediction-based evaluation. In simulation-based analyses, the normalized prediction distribution error of two models (those of Shellhaas et al. [24] and Marsot et al. [25]) obeyed a normal distribution. Bayesian forecasting with more than one observation improved predictive capability. Incorporation of both allometric size scaling and maturation function generally enhanced the predictive performance, but with marked improvement for the adult pharmacokinetic model. Conclusion: The predictive performance of published pharmacokinetic models of PB was diverse, and validation may be necessary to extrapolate to different clinical settings. Our findings suggest that Bayesian forecasting improves the predictive capability of individual concentrations for pediatrics.

scholarly journals Towards Rational Dosing Algorithms for Vancomycin in Neonates and Infants Based on Population Pharmacokinetic Modeling

2015 ◽  
Vol 60 (2) ◽  
pp. 1013-1021 ◽  
Author(s):  
Esther J. H. Janssen ◽  
Pyry A. J. Välitalo ◽  
Karel Allegaert ◽  
Roosmarijn F. W. de Cock ◽  
Sinno H. P. Simons ◽  
...  
Keyword(s):  
Pediatric Population ◽  
External Validation ◽  
Population Pharmacokinetic ◽  
Target Area ◽  
Pharmacokinetic Models ◽  
Time Curve ◽  
Population Pharmacokinetic Modeling ◽  
Model Based ◽  
Dosing Algorithm ◽  
Dosing Regimens

ABSTRACTBecause of the recent awareness that vancomycin doses should aim to meet a target area under the concentration-time curve (AUC) instead of trough concentrations, more aggressive dosing regimens are warranted also in the pediatric population. In this study, both neonatal and pediatric pharmacokinetic models for vancomycin were externally evaluated and subsequently used to derive model-based dosing algorithms for neonates, infants, and children. For the external validation, predictions from previously published pharmacokinetic models were compared to new data. Simulations were performed in order to evaluate current dosing regimens and to propose a model-based dosing algorithm. The AUC/MIC over 24 h (AUC24/MIC) was evaluated for all investigated dosing schedules (target of >400), without any concentration exceeding 40 mg/liter. Both the neonatal and pediatric models of vancomycin performed well in the external data sets, resulting in concentrations that were predicted correctly and without bias. For neonates, a dosing algorithm based on body weight at birth and postnatal age is proposed, with daily doses divided over three to four doses. For infants aged <1 year, doses between 32 and 60 mg/kg/day over four doses are proposed, while above 1 year of age, 60 mg/kg/day seems appropriate. As the time to reach steady-state concentrations varies from 155 h in preterm infants to 36 h in children aged >1 year, an initial loading dose is proposed. Based on the externally validated neonatal and pediatric vancomycin models, novel dosing algorithms are proposed for neonates and children aged <1 year. For children aged 1 year and older, the currently advised maintenance dose of 60 mg/kg/day seems appropriate.

scholarly journals Optimal sampling strategies for darunavir and external validation of the underlying population pharmacokinetic model

2020 ◽  
Author(s):  
Gabriel Stillemans ◽  
Leila Belkhir ◽  
Bernard Vandercam ◽  
Anne Vincent ◽  
Vincent Haufroid ◽  
...  
Keyword(s):  
Population Pharmacokinetic Model ◽  
Pharmacokinetic Model ◽  
External Validation ◽  
Diagnostic Methods ◽  
Validation Dataset ◽  
Pharmacokinetic Parameters ◽  
Population Pharmacokinetic ◽  
Optimal Sampling ◽  
Sampling Strategies ◽  
Validation Set

Abstract Purpose A variety of diagnostic methods are available to validate the performance of population pharmacokinetic models. Internal validation, which applies these methods to the model building dataset and to additional data generated through Monte Carlo simulations, is often sufficient, but external validation, which requires a new dataset, is considered a more rigorous approach, especially if the model is to be used for predictive purposes. Our first objective was to validate a previously published population pharmacokinetic model of darunavir, an HIV protease inhibitor boosted with ritonavir or cobicistat. Our second objective was to use this model to derive optimal sampling strategies that maximize the amount of information collected with as few pharmacokinetic samples as possible. Methods A validation dataset comprising 164 sparsely sampled individuals using ritonavir-boosted darunavir was used for validation. Standard plots of predictions and residuals, NPDE, visual predictive check, and bootstrapping were applied to both the validation set and the combined learning/validation set in NONMEM to assess model performance. D-optimal designs for darunavir were then calculated in PopED and further evaluated in NONMEM through simulations. Results External validation confirmed model robustness and accuracy in most scenarios but also highlighted several limitations. The best one-, two-, and three-point sampling strategies were determined to be pre-dose (0 h); 0 and 4 h; and 1, 4, and 19 h, respectively. A combination of samples at 0, 1, and 4 h was comparable to the optimal three-point strategy. These could be used to reliably estimate individual pharmacokinetic parameters, although with fewer samples, precision decreased and the number of outliers increased significantly. Conclusions Optimal sampling strategies derived from this model could be used in clinical practice to enhance therapeutic drug monitoring or to conduct additional pharmacokinetic studies.

PROSPECTIVE VALIDATION OF A MODEL-BASED DOSING REGIMEN FOR AMIKACIN IN NEONATES: FEASIBLE AND RELEVANT

2015 ◽  
Vol 101 (1) ◽  
pp. e1.40-e1
Author(s):  
Anne Smits ◽  
Roosmarijn De Cock ◽  
Karel Allegaert ◽  
Sophie Vanhaesebrouck ◽  
Meindert Danhof ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Dosing Regimen ◽  
Model Based ◽  
Prediction Distribution ◽  
Before And After ◽  
Almost All ◽  
Trough Levels

IntroductionA neonatal amikacin dosing regimen was previously developed based on a population pharmacokinetic model. The aim of the current study was to prospectively validate this model-derived dosing regimen.MethodsFirst, early (before and after second dose) therapeutic drug monitoring (TDM) observations were evaluated for achieving target trough (<3 mg/L) and peak (>24 mg/L) levels. Secondly, observed concentrations were compared with model-predicted concentrations, whereby the results of an NPDE (normalized prediction distribution error) were considered as well. Subsequently, Monte Carlo simulations were performed. Finally, remaining causes limiting amikacin predictability (prescription errors and disease characteristics of outliers) were explored.ResultsIn 579 neonates [median (range) birth bodyweight 2285 (420–4850) g, postnatal age 2 (1–30) days, gestational age 34 (24–41) weeks], 90.5% of early peak levels reached 24 mg/L and 60.2% of trough levels was <3 mg/L (93.4% ≤5 mg/L). Observations were accurately predicted by the model without bias, which was confirmed by the NPDE. Monte Carlo simulations showed that peak concentrations >24 mg/L were reached in almost all patients. Trough values <3 mg/L were documented in 78–100% and 45–96% of simulated cases, respectively, when ibuprofen was co-administered or not. Suboptimal trough levels were found in patient subgroups with postnatal age <14 days and current weight >2000g.ConclusionsProspective validation of a model-based neonatal amikacin dosing regimen resulted in optimized peak and trough concentrations in almost all patients. Adapted dosing for patients with suboptimal trough levels was proposed. Besides improving dosing individualization, feasibility and relevance of neonatal prospective validation studies was demonstrated.

scholarly journals Development and validation of a deep learning algorithm based on fundus photographs for estimating the CAIDE dementia risk score

2021 ◽  
Author(s):  
Rong Hua ◽  
Jianhao Xiong ◽  
Gail Li ◽  
Yidan Zhu ◽  
Zongyuan Ge ◽  
...  
Keyword(s):  
Deep Learning ◽  
Risk Score ◽  
Learning Algorithm ◽  
External Validation ◽  
Population Based ◽  
Validation Dataset ◽  
Internal Validation ◽  
Deep Learning Algorithm ◽  
Dementia Risk ◽  
Fundus Photographs

AbstractImportanceThe Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) dementia risk score is a recognized tool for dementia risk stratification. However, its application is limited due to the requirements for multidimensional information and fasting blood draw. Consequently, effective, convenient and noninvasive tool for screening individuals with high dementia risk in large population-based settings is urgently needed.ObjectiveTo develop and validate a deep learning algorithm using retinal fundus photographs for estimating the CAIDE dementia risk score and identifying individuals with high dementia risk.DesignA deep learning algorithm trained via fundus photographs was developed, validated internally and externally with cross-sectional design.SettingPopulation-based.ParticipantsA health check-up population with 271,864 adults were randomized into a development dataset (95%) and an internal validation dataset (5%). The external validation used data from the Beijing Research on Ageing and Vessel (BRAVE) with 1,512 individuals.ExposuresThe estimated CAIDE dementia risk score generated from the algorithm.Main Outcome and MeasureThe algorithm’s performance for identifying individuals with high dementia risk was evaluated by area under the receiver operating curve (AUC) with 95% confidence interval (CI).ResultsThe study involved 258,305 participants (mean aged 42.1 ± 13.4 years, men: 52.7%) in development, 13,559 (mean aged 41.2 ± 13.3 years, men: 52.5%) in internal validation, and 1,512 (mean aged 59.8 ± 7.3 years, men: 37.1%) in external validation. The adjusted coefficient of determination (R2) between the estimated and actual CAIDE dementia risk score was 0.822 in the internal and 0.300 in the external validations, respectively. The algorithm achieved an AUC of 0.931 (95%CI, 0.922–0.939) in the internal validation group and 0.782 (95%CI, 0.749–0.815) in the external group. Besides, the estimated CAIDE dementia risk score was significantly associated with both comprehensive cognitive function and specific cognitive domains.Conclusions and RelevanceThe present study demonstrated that the deep learning algorithm trained via fundus photographs could well identify individuals with high dementia risk in a population-based setting. Our findings suggest that fundus photography may be utilized as a noninvasive and more expedient method for dementia risk stratification.Key PointsQuestionCan a deep learning algorithm based on fundus images estimate the CAIDE dementia risk score and identify individuals with high dementia risk?FindingsThe algorithm developed by fundus photographs from 258,305 check-up participants could well identify individuals with high dementia risk, with area under the receiver operating characteristic curve of 0.931 in internal validation and 0.782 in external validation dataset, respectively. Besides, the estimated CAIDE dementia risk score generated from the algorithm exhibited significant association with cognitive function.MeaningThe deep learning algorithm based on fundus photographs has potential to screen individuals with high dementia risk in population-based settings.

scholarly journals Comparing the characteristics and predicting the survival of patients with head and neck melanoma versus body melanoma: a population-based study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuxin Ding ◽  
Runyi Jiang ◽  
Yuhong Chen ◽  
Jing Jing ◽  
Xiaoshuang Yang ◽  
...  
Keyword(s):  
Head And Neck ◽  
Health Management ◽  
External Validation ◽  
Area Under The Curve ◽  
Population Based ◽  
Survival Prediction ◽  
Cancer Specific Survival ◽  
Cox Models ◽  
Lentigo Maligna Melanoma ◽  
Clinical Tools

Abstract Background Previous studies reported cutaneous melanoma in head and neck (HNM) differed from those in other regions (body melanoma, BM). Individualized tools to predict the survival of patients with HNM or BM remain insufficient. We aimed at comparing the characteristics of HNM and BM, developing and validating nomograms for predicting the survival of patients with HNM or BM. Methods The information of patients with HNM or BM from 2004 to 2015 was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The HNM group and BM group were randomly divided into training and validation cohorts. We used the Kaplan-Meier method and multivariate Cox models to identify independent prognostic factors. Nomograms were developed via the rms and dynnom packages, and were measured by the concordance index (C-index), the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and calibration plots. Results Of 70,605 patients acquired, 21% had HNM and 79% had BM. The HNM group contained more older patients, male sex and lentigo maligna melanoma, and more frequently had thicker tumors and metastases than the BM group. The 5-year cancer-specific survival (CSS) and overall survival (OS) rates were 88.1 ± 0.3% and 74.4 ± 0.4% in the HNM group and 92.5 ± 0.1% and 85.8 ± 0.2% in the BM group, respectively. Eight variables (age, sex, histology, thickness, ulceration, stage, metastases, and surgery) were identified to construct nomograms of CSS and OS for patients with HNM or BM. Additionally, four dynamic nomograms were available on web. The internal and external validation of each nomogram showed high C-index values (0.785–0.896) and AUC values (0.81–0.925), and the calibration plots showed great consistency. Conclusions The characteristics of HNM and BM are heterogeneous. We constructed and validated four nomograms for predicting the 3-, 5- and 10-year CSS and OS probabilities of patients with HNM or BM. These nomograms can serve as practical clinical tools for survival prediction and individual health management.

scholarly journals Prediction of Thiol Group Changes in Minced Raw and Cooked Chicken Meat with Plant Extracts—Kinetic and Neural Network Approaches

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1647
Author(s):  
Anna Kaczmarek ◽  
Małgorzata Muzolf-Panek
Keyword(s):  
Plant Extracts ◽  
Predictive Models ◽  
External Validation ◽  
Thiol Group ◽  
Chicken Meat ◽  
Validation Dataset ◽  
Heat Treated ◽  
Different Temperatures ◽  
Meat Samples ◽  
Network Approaches

The aim of the study was to develop predictive models of thiol group (SH) level changes in minced raw and heat-treated chicken meat enriched with selected plant extracts (allspice, basil, bay leaf, black seed, cardamom, caraway, cloves, garlic, nutmeg, onion, oregano, rosemary, and thyme) during storage at different temperatures. Meat samples with extract addition were stored under various temperatures (4, 8, 12, 16, and 20 °C). SH changes were measured spectrophotometrically using Ellman’s reagent. Samples stored at 12 °C were used as the external validation dataset. SH content decreased with storage time and temperature. The dependence of SH changes on temperature was adequately modeled by the Arrhenius equation with average high R2 coefficients for raw meat (R2 = 0.951) and heat-treated meat (R2 = 0.968). Kinetic models and artificial neural networks (ANNs) were used to build the predictive models of thiol group decay during meat storage. The obtained results demonstrate that both kinetic Arrhenius (R2 = 0.853 and 0.872 for raw and cooked meat, respectively) and ANN (R2 = 0.803) models can predict thiol group changes in raw and cooked ground chicken meat during storage.

scholarly journals DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3308
Author(s):  
Won Sang Shim ◽  
Kwangil Yim ◽  
Tae-Jung Kim ◽  
Yeoun Eun Sung ◽  
Gyeongyun Lee ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Early Stage ◽  
External Validation ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Deep Convolutional Neural Networks ◽  
Prognostic Features ◽  
Multi Scale ◽  
Model Based ◽  
Number Of Patients

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.

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